Advancement of non-destructive spectral measurements for the quality of major tropical fruits and vegetables: a review

Umuhoza aline.

1 Department of Agricultural Machinery Engineering, Chungnam National University, Daejeon, Republic of Korea

Tanima Bhattacharya

Mohammad akbar faqeerzada.

2 Department of Smart Agricultural Systems, Chungnam National University, Daejeon, Republic of Korea

Moon S. Kim

3 Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD, United States

Insuck Baek

Byoung-kwan cho.

The quality of tropical fruits and vegetables and the expanding global interest in eating healthy foods have resulted in the continual development of reliable, quick, and cost-effective quality assurance methods. The present review discusses the advancement of non-destructive spectral measurements for evaluating the quality of major tropical fruits and vegetables. Fourier transform infrared (FTIR), Near-infrared (NIR), Raman spectroscopy, and hyperspectral imaging (HSI) were used to monitor the external and internal parameters of papaya, pineapple, avocado, mango, and banana. The ability of HSI to detect both spectral and spatial dimensions proved its efficiency in measuring external qualities such as grading 516 bananas, and defects in 10 mangoes and 10 avocados with 98.45%, 97.95%, and 99.9%, respectively. All of the techniques effectively assessed internal characteristics such as total soluble solids (TSS), soluble solid content (SSC), and moisture content (MC), with the exception of NIR, which was found to have limited penetration depth for fruits and vegetables with thick rinds or skins, including avocado, pineapple, and banana. The appropriate selection of NIR optical geometry and wavelength range can help to improve the prediction accuracy of these crops. The advancement of spectral measurements combined with machine learning and deep learning technologies have increased the efficiency of estimating the six maturity stages of papaya fruit, from the unripe to the overripe stages, with F1 scores of up to 0.90 by feature concatenation of data developed by HSI and visible light. The presented findings in the technological advancements of non-destructive spectral measurements offer promising quality assurance for tropical fruits and vegetables.

1. Introduction

Tropical fruits and vegetables are agricultural crops that are typically grown in tropical regions where the climate is warm, with temperatures ranging from 20 to 35 0 C ( Bahadur et al., 2020 ). Tropical regions are found amidst the tropics of Cancer and Capricorn, and encompass equatorial zones in Oceania, Asia, Africa, Central and South America, and the Caribbean ( Zakaria, 2023 ). Crops grown naturally in such weather conditions provide essential minerals, water, fiber, and vitamins that contribute significantly to the well-being of humans by safeguarding against ailments such as diabetes, hypertension, and cancer ( Emelike and Akusu, 2019 ).

The agricultural revolution and the adaptation of numerous tropical plants to regions outside of their natural range have muddied their classification, and little is known about what properly defines and distinguishes tropical fruits and vegetables from their temperate counterparts ( Indiarto, 2020 ). Fernandes et al. ( Fernandes et al., 2011 ) described crop classification according to size, acidity, seed type, and bearing. Included among alkaline crops are apples, bananas, peaches, cherries, persimmon, and litchi ( Fernandes et al., 2011 ). Acidic crops include strawberry, orange, kiwi, pineapple, lemon, star fruit, and logan, whereas sub-acidic examples are mango, pear, blackberry, papaya, blueberry, cherimoya, and mulberry ( Fernandes et al., 2011 ). Chakraborty et al. ( Chakraborty et al., 2014 ) agreed and structured the classification of tropical fruits based on that of Fernandes. Sarkar et al. ( Sarkar et al., 2018 ) reported classification system according to maturity stage by means of ethylene gas emission and respiration rate, including both climacteric and non-climacteric tropical produce ( Sarkar et al., 2018 ). Tropical climacteric produce such as avocado, apple, pear, mango, papaya, broccoli, banana, kiwi, and tomato undergoes maturation in correlation with an escalation in their respiration rate and the release of ethylene gas ( Indiarto, 2020 ), whereas tropical non-climacteric crops such as grape, berry, citrus, litchi, strawberry, raspberry, pumpkin, watermelon, cucumber, and pineapple do not undergo an elevation in their respiration rate as they reach maturity ( Indiarto, 2020 ). The contrasting report of Retamales et al. ( Retamales, 2011 ) centers around the production of temperate crops worldwide. In this report, apple, raspberry, pear, peach, kiwi, blueberry, strawberry and plum were considered as temperate fruits ( Retamales, 2011 ). In addition, Benichou et al. ( Benichou et al., 2018 ) have also classified temperate fruits as tree (apple, plum, pear and peach), vine (grape and kiwi), and small fruits such as raspberry, blueberry and currant ( Benichou et al., 2018 ).

Papaya, pineapple, avocado, mango, and banana are considered to be major tropical fruits globally ( Mukhametzyanov et al., 2022 ). According to a market review prediction for the years 2013 to 2022 by the Food and Agriculture Organization of the United Nations (FAO), the most exported tropical fruits globally from Central America and the Caribbean, South America and Asia, Africa, and others in millions of tons were papaya, pineapple, avocado and mango with 3.7, 3.2, 2.3, and 2.1, respectively ( Altendorf, 2019 ). On the other hand, recent data have shown that global vegetable production increased by 68% between 2000 and 2021 ( FAO, 2022 ). Because of the continuous and emergent demand for tropical fruits and vegetables worldwide, the present emphasis is on quality assurance in relation to end-user inclinations and commercial standards ( Silva and Abud, 2017 ). The quality of tropical fruits and vegetables is characterized by both external and internal parameters ( Jha and Matsuoka, 2000 ). External parameters namely color, defects, size and shape depend on not only the appearance of the product, but also on the standards set ( Cubero et al., 2016 ), whereas internal parameters such as nutritional value, internal defects, flavor, and texture are subjective to physicochemical composition and climate change ( Zainalabidin et al., 2019 ). The quality of fruits and vegetables influences consumer preference and is directly or indirectly linked with further value-addition and processing technologies ( James et al., 2010 ).

Several studies have identified postharvest losses as the most prominent factor among the origins of crop quality deterioration ( Porat et al., 2018 ; Etana, 2019 ; Ahmad et al., 2021 ). Adding to that, high temperature and relative humidity are mentioned in the biological and chemical degradation of produce freshness, which affects sweetness, flavor, weight, turgor, and nutritional value ( Elik et al., 2019 ). However, past reports indicated that low-temperature cooling systems and edible coating materials can be used to maintain and monitor the quality of these crops ( Mendy et al., 2019 ; Jodhani and Nataraj, 2021 ). Conventional methods relying on the quantification of different quality traits such as dry matter content, oil content, and moisture content have also been reported in the study of quality parameters of fruits and vegetables; however, these methods were found to be undesirable, destructive, time-consuming, and labor-intensive ( Magwaza and Tesfay, 2015 ; Kyriacou and Rouphael, 2018 ). Therefore, the application of non-destructive bio-sensing methods as a promising alternative for evaluating the value of tropical produce has been adopted ( Ndlovu et al., 2022 ; Okere et al., 2022 ).

Computer vision and popular pre-trained convolutional neural network (CNN) models have been used as recognition systems to sort and grade different fruits and vegetables, especially in supermarkets, regarding their variety and species ( Dubey and Jalal, 2012 ). However, computer vision can only assess external quality attributes due to the lack of spectral information ( Rahman and Cho, 2016 ; Bhargava and Bansal, 2021 ). Acoustic emission technology involves the mechanical destruction of produce when subjected to mechanical or thermal stimulus ( Aboonajmi et al., 2015 ) and is not appropriate for all categories of fruits and vegetables ( Adedeji et al, 2020 ). Extensive works have been published on the evaluation of fruits and vegetables by spectral measurements such as Fourier transform infrared (FTIR) spectroscopy ( Egidio et al., 2009 ), Near-infrared (NIR), Raman spectroscopy ( Pandiselvam et al., 2022 ), and hyperspectral imaging (HSI) ( Wang and Zhai, 2018 ). Generally, these reports have concentrated on the utilization of spectral measurements for determining targeted quality parameters of a particular fruit or vegetable variety. For instance, visible and near-infrared spectroscopy was used to investigate the internal browning in mango fruits ( Gabriëls et al., 2020 ). Ali et al. ( Ali et al., 2023 ) investigated FTIR, NIR, and machine vision in the quality monitoring of pineapples. Metlenkin et al. ( Metlenkin et al., 2022 ) distinguished Hass avocado fruits by defects using hyperspectral imaging (HSI). The question revolves around the practical utilization of these approaches and the challenges associated with improving data processing speed and in-line implementation ( Cortés et al., 2019 ; Si et al., 2022 ). Quick hardware and software are required to fulfill the demands of swift analysis for extensive hyperspectral datasets ( Xu et al., 2023 ) and machine learning algorithms, especially those relying on deep learning act as black boxes rather than using interpretability models for high-stakes decisions ( Caceres-Hernandez et al., 2023 ).

The present review highlights the current advances in non-destructive spectral measurements for quality assessment, specifically for major tropical fruits and vegetables. The quality parameters of these tropical produces are covered first. The discussion on each of the spectral measurements, the tropical crops used, and the specific findings obtained from various studies, which are summarized in Table 1 , follows and can deliver valuable information on the capabilities and efficiency of these techniques. In addition, the merits and demerits of each of these spectral measurements, which are presented in Table 2 , will guide future researchers in selecting the proper evaluation method when evaluating the quality of tropical produces. To facilitate comprehension and quick understanding of key terminologies involved, the list of abbreviations and definitions contained in the paper is presented in Table 3 .

Table 1

A comparison of the application of various non-destructive spectral measurements in the quality assessment of tropical fruits and vegetables.

Table 2

Merits and demerits of non-destructive spectral measurements in the quality control of tropical fruits and vegetables.

Table 3

List of abbreviations and acronyms used in the paper.

2. Quality inspection of Tropical fruits and vegetables

Quality inspection is the process of evaluating specific parameters of fruits and vegetables to ensure required quality standards ( Phey et al., 2020 ). The intention of quality inspection is to detect any internal or external characteristics that can aid in identifying both standard quality parameters and defects or non-conformities that can affect the safety of fruits and vegetables or their usability in particular functions such as diets, trade, and industrial chains ( Kirezieva et al., 2013 ).

2.1. External quality of tropical fruits and vegetables

The appearance of fruits and vegetables is a sensory attribute that directly influences the perceived worth of the produce for consumers ( Zhang et al., 2014 ). The external quality of tropical crops is indicated by a number of factors, including size, shape, color, and external defects, as shown in Table 4 ( Ganiron, 2014 ). The size and shape are two complementary factors that differ depending on the variety of the plant and are both assessed in relation to market grading standards ( Abbaszadeh et al., 2013 ). The size is determined by measuring area, perimeter, length, and width, which is more complex due to the morphological irregularities of tropical crops natural state ( Cubero et al., 2011 ). Moreda et al. ( Moreda et al., 2009 ) described some non-invasive systems for assessing the size of fruits and vegetables. The systems are based on (1) measuring the volume of the gap between the fruit and the outer casing of an embracing gauge; (2) measuring the distance between a radiation source and the fruit contour, where this distance is computed from the time of flight (TOF) of the propagated waves; (3) light obstruction by barriers or blockades of light; (4) 2D and 3D machine vision systems ( Moreda et al., 2009 ).

Table 4

The external quality parameters of tropical fruits and vegetables.

Wang et al. ( Wang et al., 2017 ) evaluated mango size by RGB–D (depth) imaging and time-of-flight camera imaging system. The camera-to-fruit distance was determined using three methods for fruit sizing from images: stereo vision camera, RGB–D camera and a time-of-flight laser rangefinder ( Wang et al., 2017 ). The obtained length and width values were good with RMSE of 4.9mm and 4.3mm respectively. It is cost-effective and simple to use; however, it pertains non-occluded fruit only and cannot be utilized in direct sunlight ( Wang et al., 2017 ). Neupane et al. ( Neupane et al., 2022 ) replicated the work of Wang by suggesting the use of partly occluded fruit. To obtain the linear length of the fruits, bounding box dimensions of an instance segmentation model (Mask R-CNN) was applied to canopy images ( Neupane et al., 2022 ). The findings were good with RMSE values of 4.7 mm and 5.1 mm for Honey Gold and Keitt mango varieties, respectively ( Neupane et al., 2022 ). Sanchez et al. ( Sanchez et al., 2020 ) investigated spectroscopic and depth imaging techniques combined with machine vision to estimate the length, width, thickness, and volume of sweet potato and potato. When the correct size group was graded, the method had a high accuracy of 90% ( Sanchez et al., 2020 ).

Color is an external quality trait that depends on the maturity of produce and is subjective to internal features such as taste, perception, and pleasantness of fruits and vegetables ( Yahaya et al, 2017 ). Calorimeters evaluate color by measuring the typical surface area of the product and detects the color space values L*, a*, and b* which are based on the human color perception theory ( Aguilar-Hernández et al., 2021 ). The capability of infrared thermal imaging approaches was investigated in the measurement of pineapple color. In this investigation, the L*, a*, and b* mean values for calorimeter increased by (P < 0.05) ( Ali et al., 2022 ). The optical fiber sensors mounted with RGB LEDs were also used to evaluate the color of mangoes, giving R 2 = 0.879 ( Yahaya et al., 2011 ).

External defects include the evidence of rot, bruising, crushing, shriveling, and wilting due to water loss which impact market value and the price of the fruits and vegetables ( Raj and Suji, 2019 ). These defects can be recognized and monitored through the appearance of the crop by qualified personnel relying on subjective evaluation, which may result in human errors ( Ali et al., 2023 ). Sahu et al. ( Sahu and Potdar, 2017 ) proposed a digital image analysis algorithm for detecting exterior defects in mango fruit. Surface defects such as scars and black patches were used to detect defective mango fruits, and were recognized by extracting the contours of damaged areas ( Sahu and Potdar, 2017 ). The damaged area was then filled to identify its location in the image as the basis for discrimination. Sahu and colleagues achieved good accuracy but advocated the use of optimal and adaptive threshold approaches for segmenting mango fruits from image backgrounds ( Sahu and Potdar, 2017 ).

2.2. Internal quality of tropical fruits and vegetables

The internal qualities of fruits and vegetables are also termed hidden qualities and are determined by texture, nutrients, internal defects, and flavor, as presented in Table 5 ( Shewfelt, 2014 ). Different fruits and vegetables usually have different textures, which are characterized by their firmness, crispness, and crunchiness ( Fillion and Kilcast, 2002 ). The assessment of fruit and vegetable firmness, a vital quality characteristic related to texture, can be achieved through sensory measurements ( Magwaza and Opara, 2015 ). The texture is measured with a penetrometer by putting a probe tip installed on the texture analyzer into fruit tissue at a specific speed and depth so as to exert the most force ( Ali et al., 2017 ). Uarrota et al. ( Uarrota and Pedreschi, 2022 ) used a non-destructive texture analyzer to determine the firmness of avocado under different storage conditions. Enough data were required to construct the best model allowing an extension to the model firmness of avocado ( Uarrota and Pedreschi, 2022 ). Kasim et al. ( Kasim et al., 2021 ) compared laboratory-based (305-1713 nm) and portable-based (740-1070 nm) NIR spectrometers to determine mango firmness ( Kasim et al., 2021 ). The results showed that portable and laboratory-based NIR instruments performed similar in respect of R 2 p. Compared to the laboratory-based instrument, the RMSEP of the portable NIR was higher ( Kasim et al., 2021 ).

Table 5

The internal quality parameters of tropical fruits and vegetables.

Nutritional value, such as the sugar content related with vitamins and minerals, comprises the main constituents of soluble solids content (SSC), total soluble solids (TSS), and total acidity (TA) ( Leiva-Valenzuela et al., 2013 ). Aziz et al. ( Aziz et al., 2021 ) evaluated the relationship between TSS and the capacitance of papaya using capacitance-sensing techniques ( Aziz et al., 2021 ). A refractometer was used as part of a destructive technique to predict the reference values of moisture and TSS content. Capacitive sensing was then tested as non-destructive approach for the evaluation of output voltage and capacitance of papaya ( Aziz et al., 2021 ). Aziz observed a good correlation between destructive and non-destructive techniques, with R 2 of 0.9434 and 0.9177 for moisture and TSS content, respectively ( Aziz et al., 2021 ). The usefulness of NIR spectroscopy was demonstrated in the determination of starch and soluble solid contents of papaya ( Purwanto et al., 2015 ). Srivichien and colleagues tested the nitrates in pineapples using Vis–NIR (600-1200 nm) spectroscopy, yielding an R value of 0.95 ( Srivichien et al., 2015 ). However, due to the big size and the change in nitrate levels, many scans were needed on different areas of pineapple ( Srivichien et al., 2015 ). In the study to predict starch content of sweet potatoes and potatoes, hyperspectral imaging was applied by Su et al. ( Su and Sun, 2019 ). Su developed partial least squares regression (PLSR) models at full-wavelength referring to spectral profiles and observed reference values, resulting in a high accuracy and an R 2 P of 0.963 ( Su and Sun, 2019 ).

Internal defects are detected as internal injury such as rot and water core inside the flesh of the fruits and vegetables due to postharvest problems( Ruiz-Altisent et al., 2010 ). Flavor or taste is defined by the sugar (sweetness), acidity (sourness), bitterness, and saltiness perceived by the tongue and nose ( Zhu et al., 2020 ). It is, therefore, measured subjectively through oral testing or smelling, or by the conventional technical quantification of compounds such as liquid and gas chromatography ( Yahaya et al, 2017 ). Korean universities conducted research on the taste and odor properties of broccoli using electronic sensors ( Hong et al., 2022 ). For electronic tongue analysis, thermal processing boosted sourness and umami tastes while decreasing saltiness, sweetness, and bitterness ( Hong et al., 2022 ). Therefore, the capability of non-destructive spectral measurement methods to assess inside parameters is important to maintain the flesh quality of tropical fruits and vegetables.

3. Non-destructive spectral measurements for the quality evaluation of tropical fruits and vegetables

Non-destructive techniques for quality monitoring of tropical fruits and vegetables refer to the process of inspecting their external and internal properties without causing damage or changing their physical and internal status ( El-Mesery et al., 2019 ). The potential for employing spectral measurement approaches in the quality control of fruits and vegetables is growing enormously ( Escárate et al., 2022 ). The reason is that these approaches are non-destructive, fast and accurate, capable for both quantitative and qualitative analysis, thereby requiring minimal sample preparation ( Cozzolino, 2022 ). We divided non-destructive spectral measurements into two categories: (1) spectral-based approaches (FTIR, NIR, and Raman spectroscopy) and (2) imaging-based approaches (HSI), as shown in Figure 1 .

An external file that holds a picture, illustration, etc.
Object name is fpls-14-1240361-g001.jpg

The schematic diagram of commonly used non-destructive spectral measurements.

3.1. Spectral-based approaches

Spectral measurement refers to effective techniques used to study the quality parameters of various agricultural materials including tropical fruits and vegetables by investigating light, sound, or particles that are emitted, absorbed, or scattered during measurement ( Pathare and Rahman, 2022 ). Spectroscopic techniques based on FTIR, NIR, and Raman have been successful and popular in the detection of quality parameters of fruits and vegetables ( Dasenaki and Thomaidis, 2019 ). Various research works have used spectral techniques focusing on fruits and vegetables, such as in the fast determination of the sugar and acid composition of citrus ( Clark, 2016 ), assessment of primary sugars and amino acids in raw potato tubers ( Ayvaz et al., 2015 ), and determination of nutrients and moisture content of fruits and vegetables ( Sirisomboon, 2018 ). Quality parameters of tropical crops can be assessed by one of—or a sequence of—the above complementary techniques, which are distinguished depending on the infrared region (IR) they occupy and the molecular vibrations they detect ( Bureau et al., 2019 ). The infrared region of the electromagnetic spectrum, presented in Figure 2 , is separated into three sections, namely near-infrared (NIR), mid-infrared (MIR), and far-infrared (FIR) ( Yeap and Hirasawa, 2019 ). Mango maturity has been predicted using the near-infrared (NIR) spectral region of 1200-2200 nm ( Jha et al., 2014 ). The mid-infrared (MIR) spectral range of from 2500 to 25000 nm has been used in the prediction of banana maturity and geographical origin by Zhang et al. ( Zhang et al., 2021 ), and in the measurement of soluble solids, total acids, and total anthocyanin in berries ( Clark et al., 2018 ). Far-infrared (FIR) ranges have often been reported to be between 25000 and 300000 nm ( Larkin, 2017 ). However, FIR applications are not clearly defined and are limited due to challenges in developing FIR instrumentation; furthermore, the band assignments of low-frequency vibrational modes are not straightforward ( Ozaki, 2021 ). These spectral ranges are based on their relationship to the visible spectrum, which falls between 380 and 780 nm ( Su and Sun, 2018 ).

An external file that holds a picture, illustration, etc.
Object name is fpls-14-1240361-g002.jpg

Modified diagram showing the infrared regions of the electromagnetic spectrum ( Yeap and Hirasawa, 2019 ), ( Aboud et al., 2019 ).

3.1.1. Fourier transform infrared spectroscopy

FTIR is a form of vibrational spectroscopy that uses light interference to identify the chemical composition of scanned samples by producing infrared absorption or emission spectra ( Larkin, 2017 ). On the electromagnetic spectrum, FTIR operates in the MIR region (2500 to 25000nm) and generates fruit or vegetable chemical profile by capturing the principle vibrational and rotational stretching modes of molecules ( Lohumi et al., 2015 ). FTIR spectroscopy comprises of an infrared light source, interferometer, sample, and detector, shown in Figure 3 . The principal part is the interferometer which is made up of three components: the beam splitter, collimator, and the two mirror (fixed and movable mirror) ( Patrizi and Cumis, 2019 ). When the radiation from the light source passes through the collimator, strikes the beam splitter which ideally divide it into two beams. The first beam hits the static mirror, and is reflected back; while the second hits the movable mirror where it enters through the sample toward the detector ( Blum and Harald, 2012 ).

An external file that holds a picture, illustration, etc.
Object name is fpls-14-1240361-g003.jpg

Modified diagram of FTIR spectroscopy taking banana as sample ( Patrizi and Cumis, 2019 ).

The FTIR associated with attenuated total reflection (ATR-FTIR) has recently gained importance ( Chan and Kazarian, 2016 ). The ATR works under the principle of total internal reflectance where infrared light interacts with the sample of high refractive index only at the point where infrared light is reflected ( Ryu et al., 2021 ). Unlike transmission methods, the ATR-FTIR technique can be used to study solid, liquid, and paste samples with minimal sample preparation ( Glassford et al., 2013 ).The combination of ATR-FTIR and chemometrics was promising in the assessment of added sugar content, (ASC), total soluble solids (TSS) and real juice content (RJC) of fresh and commercial mango juice ( Jha and Gunasekaran, 2010 ). PLS and MLR models resulted into accuracy of 0.99 and 0.98 respectively ( Jha and Gunasekaran, 2010 ). Canteri et al. ( Canteri et al., 2019 ) have used ATR-FTIR to evaluate the cell wall compositions of 29 species of fruits and vegetables as freeze-dried powders and alcohol-insoluble solids. The results were accurate, with determination coefficient R 2 ≥ 0.9 ( Canteri et al., 2019 ). Recently, Sinanoglou et al. ( Sinanoglou et al., 2023 ) conducted the evaluation of both peel and fresh banana ripening stage by ATR-FTIR, along with image analysis, discriminant and statistical analysis ( Sinanoglou et al., 2023 ). The computed features were accurate enough to separate ripening stages; however, monitoring of the banana ripening process was highly reliant on the instrument employed for image analysis such as digital cameras, smartphones, and electronic noses ( Sinanoglou et al., 2023 ).

3.1.2. Near-Infrared spectroscopy

NIR is used to rapidly ascertain the chemical constitution of materials according to overtones and harmonic or combination bands of specific functional groups ( Kusumaningrum et al., 2018 ). Those overtones and combinations of vibrational bands characterized by C–H, O–H, and N–H are gained by NIR in the wavelength region of 780-2500nm ( Ozaki et al., 2006 ). Tsuchikawa et al. ( Tsuchikawa et al., 2022 ) described NIR as a spectroscopic method that is suitable for samples of high water content, including fruits and vegetables ( Tsuchikawa et al., 2022 ). NIR spectroscopy consists of a light source, sample accessory, monochromator (grating), detector, and optical components such as lenses and optical fibers, as shown in Figure 4 ( Lee et al., 2011 ).

An external file that holds a picture, illustration, etc.
Object name is fpls-14-1240361-g004.jpg

Modified diagram of NIR spectroscopy, taking avocado as sample ( Chandrasekaran et al., 2019 ).

The illumination of NIR light to the sample occurs in three ways: reflectance, interactance and transmittance ( Wang et al., 2014 ). According to Hong and colleagues, reflectance employs high light energy, has no contact with the fruit surface, and the source and sensor are placed at a specified angle ( Hong and Chia, 2021 ). Specular reflectance and diffuse reflectance are two types of reflectance measurement. Specular reflectance, which occurs when the incident and reflected angles are same, detects nothing from the inside part of the fruit ( Hong and Chia, 2021 ); While the capacity of diffuse reflectance to constrain light dispersion into solid samples allows the acquisition of interior fruit information ( Tang et al., 2022 ). Mango TSS, firmness, TA, and ripeness index (RPI) were effectively measured by NIR diffuse reflectance, with R 2 of 0.9; 0.82; 0.74; and 0.8, respectively. The effect of changes in physicochemical properties of mango during ripening, on the other hand was highlighted ( Rungpichayapichet et al., 2016 ). Kusumiyati et al. ( Kusumiyati and Suhandy, 2021 ) also evaluated TSS and Vitamin C using the same fruit and NIR spectra acquisition mode. The diffuse reflectance spectra were documented and found to be in relation with TSS, vitamin C ( Kusumiyati and Suhandy, 2021 ).

Delwiche et al. ( Delwiche et al., 2008 ) demonstrated the use of near infrared interactance (750-1088nm) to determine mango ripeness, SSC and other sugars. The mango sample was placed in contact with the probe in which the top of mango upwardly points the probe. The R 2 was 0.77; 0.75; 0.67; and 0.70 for SSC, sucrose, glucose, and fructose, respectively. Sugars such as sucrose indicates mango sweetness, fructose and glucose increases during ripening while acidity decreases ( Delwiche et al., 2008 ). Transmission mode in which the light source and sensor are opposite to each other, employs low light intensity to reflect the inner parameters and is performed with no contact on the fruit ( Nicolaï et al., 2007 ). Transmission might be done partially or fully. Though, the difference between partial transmission and diffuse reflectance remains undetermined since both evaluate the radiation that partly enters the sample and diffusely reproduced to the sensor ( Hong and Chia, 2021 ). The fruit with large seed such as mango was reported to be hard to measure in the full transmission due the low signal to noise ratio ( Greensill and Walsh, 2000 ). Subedi at al. ( Subedi and Walsh, 2011 ) detected the TSS and DM of mesocarp tissue of banana and mango by partial transmittance. Mango DM gave R 2 cv =0.75 while banana performance negatively influenced by the thickness of the peel. The TSS results on mango was good in ripe and poor in ripening stage with R 2 cv > 0.75 and R 2 p < 0.75 respectively. The results were consistent with those of Rungpichayapichet et al. ( Rungpichayapichet et al., 2016 ) and were found to be caused by the physiological factors of Mango, banana, and other tropical fruits which can change their starch content as they ripe ( Subedi and Walsh, 2011 ).

Several studies have highlighted the potentials of NIR spectroscopy to monitor the internal and external characteristics of tropical fruits and vegetables, including the following: maturity prediction of avocado and mango ( Olarewaju et al., 2016 ; S. N. Jha et al., 2014 ), total soluble solids and pH of banana ( Ali et al., 2018 ), and variety identification in sweet potatoes ( Su et al., 2019 ). However, the irregular thick skin of pineapple and chemical complexity of large seeded mango was the main difficulty to Guthrie et al. ( Guthrie and Walsh, 1997 ) in the measurement of SSC by NIR reflectance (760-2500nm). The penetration depth of NIR light into a thick-rind avocado 38 mm in diameter and 10 mm in thickness was investigated for the maturity evaluation of avocado using an NIR spectrometer (800–2400 nm) ( Olarewaju et al., 2016 ). The models for estimating oil content, were acceptable, however were not accurate, with an RPD value of less than 1.0 and an R 2 value of 0.58 ( Olarewaju et al., 2016 ). Arendse et al. ( Arendse et al., 2018 ) informed the limited accuracy of NIR for internal quality assessment of fruits and vegetables with thick rinds such as banana, avocado and pineapple due to inadequate penetration depth ( Arendse et al., 2018 ). Therefore, future studies can consider the appropriate selection of NIR optical geometry and wavelength range to improve the prediction accuracy of thick rind tropical crops ( Pratiwi et al., 2023 ).

NIR spectral data inevitably holds overlay information of numerous organic compounds at global wavelengths, making the use of global spectroscopic regions problematic rather than specific wave bands ( Lin and Yibin, 2009 ). Therefore, a combination of algorithms and chemometrics with NIR spectroscopy is now being used to meet this demand, balance data redundancy and complexity, and collect spectral information ( Guan et al., 2019 ; Yang et al., 2021 ). Portable NIR spectroscopy was used to assess mango firmness during ripening (400–1130 nm) ( Mishra et al., 2020 ). Pre-processing was done Savitzky–Golay filter, and iPLSR model was found to provide better predictive modeling, with an R 2 p of 0.75 and an RMSEC of 5.92 Hz 2 g 2/3 compared to the standard PLSR model, which had an R 2 p of 0.67 and an RMSEC of 6.88 Hz 2 g 2/3 . For the firmness in mango fruit, spectral intervals 743-770 nm and 870-905 nm were found to be the accurate predictors ( Mishra et al., 2020 ).

3.1.3. Raman spectroscopy

Raman is another form of vibrational spectroscopy that uses laser beams to interact with materials and operates in the infrared region of the electromagnetic spectrum from 2500 to 25000 nm ( Siesler et al., 2008 ). Though Raman and MIR spectroscopy methods use high levels of energy to detect molecular vibrations, Raman spectroscopy excels at equal vibrations of nonpolar sets, while MIR spectroscopy excels at the unequal vibrations of polar sets ( Campanella et al., 2021 ). Raman spectroscopy consists of a monochromatic laser, wavelength separator, and a detector, as presented in Figure 5 ( Qin et al., 2019 ). When the laser beam illuminates the sample, the photons that constitute the light are absorbed, transmitted, or scattered by the sample in different directions before reaching the detector ( Larkin, 2017 ). Absorption and transmission are linked with the infrared spectra (IR), while scattering is associated with the Raman spectra ( Jones et al., 2019 ). Rostron et al. ( Rostron et al., 2016 ) defined scattered photons in two different ways namely Rayleigh (elastic) scattering and Raman (inelastic) scattering ( Larkin, 2017 ). Rayleigh (elastic) scattering occurs when the photons scattered are equal to those illuminated to the sample; while Raman (inelastic) scattering is due to the transfer of energy between photons and the sample under testing ( Lu, 2017 ).

An external file that holds a picture, illustration, etc.
Object name is fpls-14-1240361-g005.jpg

Modified diagram of Raman spectroscopy, taking mango as sample ( Lohumi et al., 2015 ).

Raman spectroscopy is suitable for investigating carotenoids in various plants, including carrots ( Lawaetz et al., 2016 ), tomatoes ( Hara et al., 2018 ), plant cells ( Baranska et al., 2011 ), and mango ( Bicanic et al., 2010 ). Furthermore, Raman has been applied as a clean and fast approach to assess cassava starch adulteration ( Cardoso and Jesus Poppi, 2021 ). Two chemometrics models, namely one-class support vector machines (OC-SVMs) and soft independent modelling by class analogy (SIMCA), were used and compared statistically. The OC-SVM results outperform those of SIMCA, with an accuracy of 86.9% ( Cardoso and Jesus Poppi, 2021 ). Surface-enhanced Raman spectroscopy (SERS) was used as a method that applies Raman spectroscopy in conjunction with nanotechnology for the fast analysis of pesticide residues in mango ( Pham et al., 2022 ). SERS results were good indicating that the residues in mango sample were in the suitable range ( Pham et al., 2022 ). Morey et al. ( Morey et al., 2020 ) used spatially offset Raman spectroscopy for potato varieties quality categorization and prediction of tuber cultivation source. This approach is fast since it can be used directly after potato harvesting ( Morey et al., 2020 ).

3.2. Imaging-based approaches

Spectral imaging techniques are among the most effective detection methods because of their potential to obtain both spectral and spatial dimensions of produce simultaneously during measurement ( Liu et al., 2017 ). Regarding spatial dimensions, external attributes such as size, shape, appearance, and color can be evaluated, while with spectral analysis, internal features such as chemical composition can be measured ( Pu et al., 2015 ). A number of imaging techniques use two-dimensional geometry according to the fusion and luminance of color maps ( Lu et al., 2014 ), while others involve the use of three-dimensional sensors such as RGB and hyperspectral images ( Barnea et al., 2016 ) to provide a high fruit and vegetable recognition accuracy ( Nyarko et al., 2018 ).

3.2.1. Hyperspectral imaging techniques

In agriculture and food systems, hyperspectral imaging is a powerful system that joins two aspects of imaging and spectroscopy to attain a three-dimensional (3D) hypercube data form and analyzes a broad spectrum at each pixel instead of assigning only main RGB colors (red, green, and blue) ( Khan et al., 2021 ). The hypercube consists of 3D images characterized by 2D spatial and 1D spectral dimension or wavelength ( Tang et al., 2022 ). Hyperspectral imaging employs more than ten contiguous wavelengths or narrow bands in which each pixel has a full continuous spectrum ( Elmasry et al., 2019 ). To take sample images, the hyperspectral imaging set up can be in the reflectance, transmittance, and interactance which differs in their lighting configuration during crops measurements ( Pan et al., 2017 ). The reflectance geometry is appropriate for assessing the external quality of products, whereas the transmittance performs better in measuring the internal components in relatively translucent membranes ( Li et al., 2018 ). The HSI system comprises of four main components: (1) an imaging unit, (2) illumination (light source), (3) a sample stage, and (4) a computer, as presented in Figure 6 ( Pu et al., 2015 ). The light source is divided into illumination and excitation sources for spectral imaging applications. Broadband lights are commonly used as an illumination source for reflectance and transmittance, whereas narrowband lights are for the excitation source ( Qin et al., 2013 ). The lighting devices produce light that illuminates the sample. The camera transports chemical information as well as light from the light source. The wavelength dispersion device, which can be a grating or a prism, divides the light into different wavelengths and directs the dispersed light to the sensor ( Wu and Sun, 2013 ). Aozora et al. ( Aozora et al., 2022 ) studied the efficiency of hyperspectral imaging (935–1720 nm) in the evaluation of water activity in dehydrated pineapple. The accuracy of the tested model showed good accuracy, with 0.72 and 0.0054 for Rp of and RMSEP respectively ( Aozora et al., 2022 ).

An external file that holds a picture, illustration, etc.
Object name is fpls-14-1240361-g006.jpg

Modified diagram of Hyperspectral imaging, taking pineapple as sample ( Li et al., 2018 ).

3.2.1.1. Hyperspectral imaging Image generation modes

HSI generates image in three ways: whisk broom (point scanner), push broom (line scanner), and tunable filter (area scanner) ( ElMasry and Sun, 2010 ). The point scan excites only a single spot on the object’s surface and the single pixel is recorded. The spectrum is taken at both positions by moving the sample symmetrically in two spatial dimensions, in order to get the full HSI image ( Qin, 2012 ). However, to obtain good results this technique involves double scanning of the sample and hardware relocation which takes a lot of time to complete the measurement ( Qin, 2012 ). The line scanner excites a line on the object and records the whole line of an image using a 2D dispersing element and 2D detector array. The object is moved line by line and the whole set of spatial–spectral data is gained. This approach has a higher acquisition rate but lower sectioning ability ( Qin, 2010 ). The area scan employs spectral scanning techniques to stimulate the broad area on the surface of the fruit or vegetable, which is held fixed and a scan with full spatial information is achieved consecutively across the entire spectral range. This method is appropriate for applications where sample mobility is not necessary ( Lu et al., 2017 ).

The hyperspectral imaging together with chemometrics models is an appealing option for dealing with large sets of complex, high-dimensional data ( Lorente et al., 2012 ). Chu et al. ( Chu et al., 2022 ) confirmed the efficacy of the HSI reflectance (386-1016 nm) wavelength region in combination with variable selection algorithms and chemometrics for predicting green banana maturity level and characterization of banana quality during maturation ( Chu et al., 2022 ). The line scanning approach was adopted and the calibration models used were partial least squares (PLS) and interval PLS methods ( Chu et al., 2022 ). These models obtained acceptable values R 2 = 0.64 and 0.59 for SSC and TA, respectively, whereas the models for chlorophyll and ΔE* were suitable only for sample screening with R 2 = 0.34 and 0.30, respectively ( Chu et al., 2022 ). Chu reported the inclusion of more samples and different cultivars of banana for model improvement ( Chu et al., 2022 ). Kämper et al. ( Kämper et al., 2020 ) used Vis–NIR–HSI to measure nutrients in avocado fruit. PLSR was used to obtain the ratio of unsaturated to saturated fatty acids in avocado fruit with (R 2 = 0.79, RPD = 2.06) and (R 2 = 0.62, RPD = 1.48) for flesh images and skin images respectively ( Kämper et al., 2020 ). The robust models for flesh images were R 2 = 0.67; 0.61; and 0.53, of oleic-to-linoleic acid ratio, boron (B) and calcium concentration (Ca) respectively, while for skin images was R 2 = 0.60 of boron ( Kämper et al., 2020 ).

4. Advancement in non-destructive spectral measurements for tropical fruit and vegetable quality assessment

The rapid advancement of technology in the agricultural field has resulted in the combination of artificial intelligence with non-destructive spectral measurements for fruits and vegetables quality measurement ( Hasanzadeh et al., 2022 ). Artificial intelligence models such as artificial neural networks (ANNs), genetic algorithms (GAs), fuzzy logic (FL), and adaptive neuro-fuzzy inference system (ANFIS) can assess multiple characteristics simultaneously ( Homayoonfal et al., 2022 ). Salehi reviewed development of models used in the determination of fruits and vegetables quality ( Salehi, 2020 ). ANNs, GAs, FL, and ANFIS detected defects, moisture content, and chilling injury of oranges, cherries, pomegranates, apples, peaches, avocados, button mushrooms, tomatoes, and potatoes ( Salehi, 2020 ). Despite the fact that these models are typically constrained by normality, linearity, homogeneity, and variable independence, the ANFIS model outperforms others and can be successfully used in relevant research ( Salehi, 2020 ).

Machine learning (ML) is a branch of artificial intelligence and an integral part of the development of many sensing technologies that are responsible for information retrieval, signal processing, and data analysis ( Li et al., 2021 ). In recent decades, traditional algorithms such as linear discriminant analysis (LDA), support vector machines (SVMs), K-nearest neighbors (K-NN), naïve Bayes, extreme learning machines (ELMs), decision trees (DTs), and K-means clustering have been deployed ( Fadchar and Dela Cruz, 2020 ). For instance, Rivera et al. ( Rivera et al., 2014 ) used NIR–HSI and machine learning for the early detection of mechanical damage in mango. LDA, K-NN, naïve Bayes, ELMs, and DTs were used for categorization. Bayes failed, however (K-NN, ELM, DT, and LDA Title altered) results was more than 90%. The highest performance, achieved by K-NN, was 97.9% ( Rivera et al., 2014 ).

The evolution of deep learning (DL) as a breakthrough machine learning method has been trending since 2017 due to the manual feature extraction of traditional machine learning methods ( Yang and Xu, 2021 ) and limited performance of chemometrics models, such as spectral variability caused by sample and spectrometer heterogeneity, changing environmental conditions, and infrared spectral data with high noise, which hinder feature extraction using chemometrics models ( Zhang et al., 2021 ). Deep learning is a subset of machine learning that use many neural network layers to extract complex feature representations with numerous levels of abstraction ( Lecun et al., 2015 ). According to Kamilaris et al. ( Kamilaris and Prenafeta-Boldú, 2018 ), convolutional neural network (CNN) and recurrent neural network (RNN) have been implemented for crop-type classification, counting produces, and locating their placement in the image using bounding boxes ( Kamilaris and Prenafeta-Boldú, 2018 ). However, the RNN was found to perform better than the CNN because it considers not only space but also the time which helps to capture the time dimension ( Kamilaris and Prenafeta-Boldú, 2018 ). Deep learning and machine learning technology-based spectral analysis has been used in the classification of three types of fruits (apple, lemon, and mango) by type of damage, type of goods, and whether the sample is raw in market, supermarket, wholesaler, and retailer applications ( Bobde et al., 2021 ).

Garillos-Manliguez et al. ( Garillos-Manliguez and Chiang, 2021 ) estimated six maturity stages of papaya fruit, from the unripe stage to the overripe stage, by feature concatenation of data obtained from visible light and HSI imaging ( Garillos-Manliguez and Chiang, 2021 ). AlexNet, VGG16, VGG19, ResNet50, ResNeXt50, MobileNet, and MobileNetV2 architectures was then modified to apply multimodal data cubes made of RGB and hyperspectral data ( Garillos-Manliguez and Chiang, 2021 ). Regarding classification of the six stages, these multimodal variations can reach F1 scores of up to 0.90 and a 1.45% top-2 error rate. However, due to the small size of the images and the great depth of the CNNs, resulting in highly tightly tuned training variables, overfitting may arise. On the other hand, increasing image size results in insufficient memory faults ( Garillos-Manliguez and Chiang, 2021 ).

Banana fruit was graded by Mesa et al. ( Mesa and Chiang, 2021 ) using multi-input deep learning model with RGB and HSI. These models were able to categorize tier-based bananas by 98.45% and an F1 score of 0.97 with only few samples ( Mesa and Chiang, 2021 ). However, this technique is expensive and time consuming due to the use of two cameras. The next studies instead, should consider the use of more improved camera systems with features that can extract both RGB and HSI simultaneously ( Mesa and Chiang, 2021 ). Another study by Ucat and Cruz explored the use of image processing with a deep learning to grade banana according to their specifications ( Ucat and Dela Cruz, 2019 ). The trained, validated, and test data by CNN model was more than 90% in all four classes of bananas (). The suggested CNN grading system in the tensor flow model can be commercially developed ( Ucat and Dela Cruz, 2019 ).

Portable spectrometers and real-time online detection devices have recently developed for fruits and vegetables quality assessment. Portable devices are handheld, light weight, compact size and they are applied for in-field measurements ( Sohaib et al., 2020 ). The combination of portable NIR device with MSC-PCA+LDA model was used to evaluate pineapple quality. These models were recommended to be developed in mobile phone while PLS regression model provided 85% accuracy ( Amuah et al., 2019 ). Subedi et al. ( Subedi and Walsh, 2020 ) evaluated three hand held portable near infrared spectroscopy (F750, Micro NIR and Scio v1.2) in the detection of dry matter content (DMC) in avocado fruit. The second derivative spectra were recorded for the intact and skin removed avocado fruit for reflectance and interactance optical geometry. The best results of prediction obtained from the F750 instrument using the interactance mode at 720-975 nm with R 2 p of 0.71 and 0.88 for intact and skin removed fruits respectively ( Subedi and Walsh, 2020 ). Real time monitoring device was designed as sensor which can function in all post-harvesting states to control the shelf life of fruits and vegetables such as lettuce. The device found to be the feasible for controlling the behavior of the crop during the post handling chain ( Torres-Sánchez et al., 2020 ). Fruits and vegetables including banana, orange and apple were well sorted according to their external appearance by using real time online system with artificial intelligence ( Tata et al., 2022 ). For quality categorization, machine learning models such as CNN and image processing were performed. This real time system was created in android and can be deployed in market robots where checking of huge number of products is required ( Tata et al., 2022 ).

5. Conclusion and future prospects

Non-destructive spectral measurement has emerged as a prominent solution in the agricultural sector. With the introduction of spectral measurements, there has been rapid progress in analyzing both the internal and external characteristics of tropical fruits and vegetables in a low-cost, accurate, real-time, and fast manner ( Ali et al., 2017 ). Techniques based on FTIR, NIR, and Raman spectroscopy require simple steps to prepare samples prior to analysis ( Abbas et al., 2020 ). In contrast to other imaging techniques such as computer vision, acoustic approaches, electric noses, and fluorescence, HSI uses spectral and spatial data to assess different parameters concurrently ( Lu et al., 2020 ). The spectral measurements presented in this review have shown potential applications for a diverse range of tropical fruits and vegetables for the monitoring and detection of quality attributes such as SSC, TSS, TA, color, size, defects, and texture, which is particularly important for fruit and vegetable processors, food safety agencies, and consumer demands.

Significant advancements in non-destructive spectral measurement technology have occurred recently, including the development of portable spectrometers for real-time and field applications. The combination of spectral measurements and chemometric techniques is a powerful tool for multivariate data analysis, mainly in the improvement of models needed for classification and estimation of quality. A practical case study of Metlenkin et al. ( Metlenkin et al., 2022 ) in the identification and classification of Hass avocado defects before and after storage by HSI and chemometrics. The PLSDA and SIMCA were selected as chemometric methods for multivariate data discrimination and classification. To increase the final model accuracy the calibration was performed by selecting the region of interest. The results revealed the high potential of SIMCA during both modelling and test validation with 100% accuracy. Furthermore, the integration of spectral measurements with deep learning and machine learning technology is rapidly expanding in order to improve quality control accuracy while overcoming the challenges associated with chemometrics such as spectral variability, spectrometer heterogeneity, changing environmental conditions, and infrared spectral data with high noise. The revolution in agriculture and the adaptation of numerous tropical plants to regions outside of their natural range have muddied their classification, and little is known about what properly defines and distinguishes tropical fruits and vegetables from their temperate counterparts. Therefore, there is confusion associated with those studies that reported the classification of tropical fruits and vegetables as an important factor to consider when examining the distinctive quality indicators of these crops. Taking into accounts all of the merits and demerits of non-destructive spectral measurements for the quality monitoring of tropical fruits and vegetables, the use of an adequate number of samples, different cultivars of the fruit and increasing the quality attributes to predict can help to develop robust models that emphasize the variability of tropical fruits and vegetables in terms of size and shape, skin thickness, and growing conditions.

Author contributions

Conceptualization: UA, B-KC. Methodology: UA, TB, MF, MK and IB. Investigation: UA, TB and B-KC. Writing and reviewing: UA, TB, MF and B-KC. Supervision: B-KC. All authors contributed to the article and approved the submitted version.

Funding Statement

This work was supported by Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry (IPET) through Smart Agri Products Flow Storage Technology Development Program, funded by Ministry of Agriculture, Food and Rural Affairs (MAFRA) (322051-05).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

  • Abbas O., Pissard A., Baeten V. (2020). “ Near-infrared, mid-infrared, and raman spectroscopy ,” in Chemical analysis of food (Amstardam: Elsevier; ), 77–134. [ Google Scholar ]
  • Abbaszadeh R., Rajabipour A., Ahmadi H., Mahjoob M. J., Delshad M. (2013). Prediction of watermelon quality based on vibration spectrum . Postharvest Biol. Technol. 86 , 291–293. doi:  10.1016/j.postharvbio.2013.07.013 [ CrossRef ] [ Google Scholar ]
  • Aboonajmi M., Jahangiri M., Hassan-Beygi a. S. R. (2015). A review on application of acoustic analysis in quality evaluation of agro-food products . J. Food Process. Preservation 39 ( 6 ), 3175–3885. doi:  10.1111/jfpp.12444 [ CrossRef ] [ Google Scholar ]
  • Aboud S. A., Altemimi A. B., Al-hiiphy A. R.S., Yi-chen L., Cacciola F. (2019). A comprehensive review on infrared heating . Molecules 2 , 1–20. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Adedeji A. A., Ekramirad N., Rady A., Hamidisepehr A., Donohue K. D., Villanueva R. T., et al.(2020) Non-destructive technologies for detecting insect infestation in fruits and vegetables under postharvest conditions: A critical review Foods 9 ( 7 ), 1–285doi:  10.3390/foods9070927 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Aguilar-Hernández M. G., Núñez-Gómez Dámaris, Forner-Giner MaríaÁngeles, Hernández F., Pastor-Pérez JoaquínJ., Legua P. (2021). Quality parameters of spanish lemons with commercial interest . Foods 10 ( 1 ), 1–135. doi:  10.3390/foods10010062 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Ahmad K., Afridi M., Khan N. A., Sarwar A. (2021). Quality deterioration of postharvest fruits and vegetables in developing country Pakistan: A mini overview . Asian J. Agric. Food Sci. 9 ( 2 ), 83–90. doi:  10.24203/ajafs.v9i2.6615 [ CrossRef ] [ Google Scholar ]
  • Ali M. M., Hashim N., Aziz S. A., Lasekan O. (2022). Quality prediction of different pineapple (Ananas comosus) varieties during storage using infrared thermal imaging technique . Food Control 138 , 1–9. doi:  10.1016/j.foodcont.2022.108988 [ CrossRef ] [ Google Scholar ]
  • Ali M. M., Hashim N., Bejo S. K., Jahari M., Shahabudin N. A. (2023). Innovative non-destructive technologies for quality monitoring of pineapples: recent advances and applications . Trends Food Sci. Technol. 133 , 176–188. doi:  10.1016/j.tifs.2023.02.005 [ CrossRef ] [ Google Scholar ]
  • Ali M. M., Hashim N., Bejo S. K., Shamsudin R. (2017). Rapid and nondestructive techniques for internal and external quality evaluation of watermelons: A review . Scientia Hortic. 225 , 689–699. doi:  10.1016/j.scienta.2017.08.012 [ CrossRef ] [ Google Scholar ]
  • Ali M. M., Janius R. B., Nawi N. M., Hashim N. (2018). Prediction of total soluble solids and PH in banana using near infrared spectroscopy . J. Eng. Sci. Technol. 13 ( 1 ), 254–645. [ Google Scholar ]
  • Altendorf (2019). Major tropical fruits . Stat. Compendium Rome 01 , 18. [ Google Scholar ]
  • Amuah C. L. Y., Teye E., Lamptey F. P., Nyandey K., Opoku-Ansah J., Adueming. P. O. W. (2019). Feasibility study of the use of handheld NIR spectrometer for simultaneous authentication and quantification of quality parameters in intact pineapple fruits . J. Spectrosc. , 1–9. doi:  10.1155/2019/5975461 [ CrossRef ] [ Google Scholar ]
  • Aozora D. W., Tantinantrakun A., Thompson A. K., Teerachaichayut S. (2022). Near infrared hyperspectral imaging for predicting water activity of dehydrated pineapples . Res. Militaris 12 ( 2022 ), 11–33. doi: 10.3390/foods12142793 [ CrossRef ] [ Google Scholar ]
  • Arendse E., Fawole O. A., Magwaza L. S., Opara. U. L. (2018). Non-Destructive prediction of internal and external quality attributes of fruit with thick rind: A review . J. Food Eng. 217 , 11–23. doi:  10.1016/j.jfoodeng.2017.08.009 [ CrossRef ] [ Google Scholar ]
  • Arendse E., Nieuwoudt H., Magwaza L. S., Nturambirwe J. F. I., Fawole O. A., Opara. U. L. (2021). Recent advancements on vibrational spectroscopic techniques for the detection of authenticity and adulteration in horticultural products with a specific focus on oils, juices and powders . Food Bioprocess Technol. 14 ( 1 ), 1–225. doi:  10.1007/s11947-020-02505-x [ CrossRef ] [ Google Scholar ]
  • Ayvaz H., Santos A. M., Moyseenko J., Kleinhenz M., Rodriguez-Saona L. E. (2015). Application of a portable infrared instrument for simultaneous analysis of sugars, asparagine and glutamine levels in raw potato tubers . Plant Foods Hum. Nutr. 70 ( 2 ), 215–205. doi:  10.1007/s11130-015-0484-7 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Aziz N. A. A., Jusoh M. Z., Rosman R. (2021). “ Relationship of total soluble solid (TSS) and capacitance value of papaya fruit using capacitive sensing technique ,” in ISCI 2021 - 2021 IEEE symposium on computers and informatics, Kuala Lumpur, Malaysia, 51–57. doi:  10.1109/ISCI51925.2021.9633402 [ CrossRef ] [ Google Scholar ]
  • Bahadur L., Singh A. D., Singh S. K. (2020). A review on successful protected cultivation of banana (Musa) . Plant Arch. 20 , 1570–1573. [ Google Scholar ]
  • Baranska M., Baranski R., Grzebelus E., ROman M. (2011). In situ detection of a single carotenoid crystal in a plant cell using raman microspectroscopy . Vibrational Spectrosc. 56 ( 2 ), 166–695doi:  10.1016/j.vibspec.2011.02.003 [ CrossRef ] [ Google Scholar ]
  • Barnea E., Mairon R., Ohad B.-S. (2016). Colour-Agnostic shape-Based 3D fruit detection for crop harvesting robots . Biosyst. Eng. 146 , 57–70. doi:  10.1016/j.biosystemseng.2016.01.013 [ CrossRef ] [ Google Scholar ]
  • Benichou M., Ayour J., Sagar M., Alahyane A., Elateri I., Aitoubahou A. (2018). Postharvest technologies for shelf life enhancement of temperate fruits . Postharvest Biol. Technol. Temperate Fruits , 77–100. doi: 10.1007/978-3-319-76843-4_4 [ CrossRef ] [ Google Scholar ]
  • Bhargava A., Bansal A. (2021). Fruits and vegetables quality evaluation using computer vision: A review . J. King Saud Univ. - Comput. Inf. Sci. 33 ( 3 ), 243–575. doi:  10.1016/j.jksuci.2018.06.002 [ CrossRef ] [ Google Scholar ]
  • Bicanic D., Dimitrovski D., Luterotti S., Twisk C., Buijnsters J. G., Dóka Ottó. (2010). Estimating rapidly and precisely the concentration of beta carotene in mango homogenates by measuring the amplitude of optothermal signals, chromaticity indices and the intensities of raman peaks . Food Chem. 121 ( 3 ), 832–385. doi:  10.1016/j.foodchem.2009.12.093 [ CrossRef ] [ Google Scholar ]
  • Blum M. M., Harald J. (2012). Historical perspective and modern applications of attenuated total reflectance - fourier transform infrared spectroscopy (ATR-FTIR) . Drug Testing Anal. 4 ( 3–4 ), 298–302. doi:  10.1002/dta.374 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Bobde S., Jaiswal S., Kulkarni P., Patil O., Khode P., Jha R. (2021). “ Fruit quality recognition using deep learning algorithm ,” in 2021 International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON), Pune, India, 1–5. doi:  10.1109/SMARTGENCON51891.2021.9645793 [ CrossRef ] [ Google Scholar ]
  • Bureau S., Cozzolino D., Clark C. J. (2019). Contributions of fourier-transform mid infrared (FT-MIR) spectroscopy to the study of fruit and vegetables: A review . Postharvest Biol. Technol. 148 , 1–14. doi:  10.1016/j.postharvbio.2018.10.003 [ CrossRef ] [ Google Scholar ]
  • Caceres-Hernandez D., Gutierrez R., Kung K., Rodriguez J., Lao O., Contreras K., et al.. (2023). Recent advances in automatic feature detection and classification of fruits including with a special emphasis on watermelon (Citrillus lanatus): A review . Neurocomputing 526 , 62–79. doi:  10.1016/j.neucom.2023.01.005 [ CrossRef ] [ Google Scholar ]
  • Campanella B., Palleschi V., Legnaioli S. (2021). Introduction to vibrational spectroscopies . ChemTexts 7 ( 1 ), 1–21. doi:  10.1007/s40828-020-00129-4 [ CrossRef ] [ Google Scholar ]
  • Canteri M. H. G., Renard C. M. G. C., Bourvellec C. Le, Bureau S. (2019). ATR-FTIR spectroscopy to determine cell wall composition: application on a large diversity of fruits and vegetables . Carbohydr. Polymers 212 , 186–196. doi:  10.1016/j.carbpol.2019.02.021 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Cardoso K. V. G., Jesus Poppi R. (2021). Cleaner and faster method to detect adulteration in cassava starch using raman spectroscopy and one-class support vector machine . Food Control 125 , 107917. doi:  10.1016/j.foodcont.2021.107917 [ CrossRef ] [ Google Scholar ]
  • Chakraborty K., Saha J., Raychaudhuri U., Chakraborty R. (2014). Tropical fruit wines: A mini review . Natural Products 7 ( 10 ), 219–285. [ Google Scholar ]
  • Chan K. L., Kazarian S. G. (2016). Attenuated total reflection fourier-transform infrared (ATR-FTIR) imaging of tissues and live cells . Chem. Soc. Rev. 45 ( 7 ), 1850–1645. doi: 10.1039/C5CS00515A [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Chandrasekaran I., Panigrahi S. S., Ravikanth L., Singh C. B. (2019). Potential of near-Infrared (NIR) spectroscopy and hyperspectral imaging for quality and safety assessment of fruits: an overview . Food Analytical Methods 12 ( 11 ), 2438–2585. doi:  10.1007/s12161-019-01609-1 [ CrossRef ] [ Google Scholar ]
  • Chu X., Miao Pu, Zhang K., Wei H., Fu H., Liu H., et al.. (2022). Green banana maturity classification and quality evaluation using hyperspectral imaging . Agric. (Switzerland) 12 ( 4 ), 1–185. doi:  10.3390/agriculture12040530 [ CrossRef ] [ Google Scholar ]
  • Clark C. J. (2016). Fast determination by fourier-transform infrared spectroscopy of sugar-acid composition of citrus juices for determination of industry maturity standards . New Z. J. Crop Hortic. Sci. 44 ( 1 ), 69–82. doi:  10.1080/01140671.2015.1131725 [ CrossRef ] [ Google Scholar ]
  • Clark C. J., Cooney J. M., Hopkins W. A., Currie A. (2018). Global mid-infrared prediction models facilitate simultaneous analysis of juice composition from berries of actinidia, ribes, rubus and vaccinium species . Food Analytical Methods 11 ( 11 ), 3147–3605. doi:  10.1007/s12161-018-1296-9 [ CrossRef ] [ Google Scholar ]
  • Cortés V., Blasco J., Aleixos N., Cubero S., Talens P. (2019). Monitoring strategies for quality control of agricultural products using visible and near-infrared spectroscopy: A review . Trends Food Sci. Technol. 85 , 138–148. doi:  10.1016/j.tifs.2019.01.015 [ CrossRef ] [ Google Scholar ]
  • Cozzolino D. (2022). An overview of the successful application of vibrational spectroscopy techniques to quantify nutraceuticals in fruits and plants . Foods 11 ( 3 ), 1–11. doi:  10.3390/foods11030315 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Cubero S., Aleixos N., Moltó E., Gómez-Sanchis J., Blasco J. (2011). Advances in machine vision applications for automatic inspection and quality evaluation of fruits and vegetables . Food Bioprocess Technol. 4 ( 4 ), 487–5045. doi:  10.1007/s11947-010-0411-8 [ CrossRef ] [ Google Scholar ]
  • Cubero S., Lee W. S., Aleixos N., Albert F., Blasco. J. (2016). Automated systems based on machine vision for inspecting citrus fruits from the field to postharvest—a review . Food Bioprocess Technol. 9 ( 10 ), 1623–1395. doi:  10.1007/s11947-016-1767-1 [ CrossRef ] [ Google Scholar ]
  • Dasenaki M. E., Thomaidis N. S. (2019). Quality and authenticity control of fruit juices-a review . Molecules 24 , 1–35. doi:  10.3390/molecules24061014 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Delwiche S. R., Mekwatanakarn W., Wang C. Y. (2008). Soluble solids and simple sugars measurement in intact mango using near infrared spectroscopy . HortTechnology 18 ( 3 ), 410–165. doi:  10.21273/horttech.18.3.410 [ CrossRef ] [ Google Scholar ]
  • Dubey S. R., Jalal XXXA. S. (2012). Robust approach for fruit and vegetable classification . Proc. Eng. 38 , 3449–3453. doi:  10.1016/j.proeng.2012.06.398 [ CrossRef ] [ Google Scholar ]
  • Egidio V. Di, Sinelli N., Limbo S., Torri L., Franzetti L., Casiraghi E. (2009). Evaluation of shelf-life of fresh-cut pineapple using FT-NIR and FT-IR spectroscopy . Postharvest Biol. Technol. 54 ( 2 ), 87–925. doi:  10.1016/j.postharvbio.2009.06.006 [ CrossRef ] [ Google Scholar ]
  • Elik A., Yanik D. K., Istanbullu Y., Guzelsoy N. A., Yavu A., Gogus. F. (2019). Strategies to reduce post-harvest losses for fruits and vegetables . Int. J. Sci. Technological Res. 5 ( 3 ), 29–395. doi:  10.7176/jstr/5-3-04 [ CrossRef ] [ Google Scholar ]
  • Elmasry G., Mandour N., Al-Rejaie S., Belin E., Rousseau D. (2019). Recent applications of multispectral imaging in seed phenotyping and quality monitoring—An overview . Sensors (Switzerland) 19 ( 5 ), 1–325. doi:  10.3390/s19051090 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • ElMasry G., Sun D.-W. (2010). “ Principles of hyperspectral imaging technology ,” in Hyperspectral imaging for food quality analysis and control (San Diego: Elsevier; ), 3–43. [ Google Scholar ]
  • El-Mesery H. S., Mao H., Abomohra A. El F. (2019). Applications of non-destructive technologies for agricultural and food products quality inspection . Sensors (Switzerland) 19 ( 4 ), 1–235. doi:  10.3390/s19040846 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Emelike N. J. T., Akusu O. M. (2019). Quality attributes of jams and marmalades produced from some selected tropical fruits . J. Food Process Technol. 10 ( 5 ), 790. doi:  10.4172/2157-7110.1000790 [ CrossRef ] [ Google Scholar ]
  • Escárate P., Farias G., Naranjo P., Zoffoli J. P. (2022). Estimation of soluble solids for stone fruit varieties based on near-infrared spectra using machine learning techniques . Sensors 22 ( 16 ), 1–115. doi:  10.3390/s22166081 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Etana M. B. (2019). A detailed review on common causes of postharvest loss and quality deterioration of fruits and vegetables in Ethiopia . J. Biology Agric. Healthcare 9 ( 7 ), 48–52. doi:  10.7176/jbah/9-7-07 [ CrossRef ] [ Google Scholar ]
  • Fadchar N. A., Dela Cruz J. C. (2020). “ A non-destructive approach of young coconut maturity detection using acoustic vibration and neural network ,” in Proceedings - 2020 16th IEEE International Colloquium on Signal Processing and Its Applications, CSPA, Langkawi, Malaysia, 136–140. doi:  10.1109/CSPA48992.2020.9068723 [ CrossRef ] [ Google Scholar ]
  • FAO (2022). Agricultural production statistics 2000–2021. Agricultural production statistics 2000–2021 (Rome: FAO; ). doi:  10.4060/cc3751en [ CrossRef ] [ Google Scholar ]
  • Fernandes F. A. N., Rodrigues S., Law C. L., Mujumdar A. S. (2011). Drying of exotic tropical fruits: A comprehensive review . Food Bioprocess Technol. 4 ( 2 ), 163–855. doi:  10.1007/s11947-010-0323-7 [ CrossRef ] [ Google Scholar ]
  • Fillion L., Kilcast D. (2002). Consumer perception of crispness and crunchiness in fruits and vegetables . Food Qual. Preference 13 ( 1 ), 23–295. doi:  10.1016/S0950-3293(01)00053-2 [ CrossRef ] [ Google Scholar ]
  • Flores K., Sanchez M. T., Perez-Marin D. C., Lopez M. D., Guerrero J. E., Garrido-Varo A. (2008). Prediction of total soluble solid content in intact and cut melons and watermelons using near infrared spectroscopy . J. Near Infrared Spectrosc. 16 ( 2 ), 91–98. doi: 10.1255/jnirs.771 [ CrossRef ] [ Google Scholar ]
  • Gabriëls S. H. E. J., Mishra P., Mensink M. G. J., Spoelstra P., Woltering E. J. (2020). Non-destructive measurement of internal browning in mangoes using visible and near-infrared spectroscopy supported by artificial neural network analysis . Postharvest Biol. Technol. 166 , 111206. doi:  10.1016/j.postharvbio.2020.111206 [ CrossRef ] [ Google Scholar ]
  • Ganiron T. U. (2014). Size properties of mangoes using image analysis . Int. J. Bio-Science Bio-Technology 6 ( 2 ), 31–42. doi:  10.14257/ijbsbt.2014.6.2.03 [ CrossRef ] [ Google Scholar ]
  • Garillos-Manliguez C. A., Chiang J. Y. (2021). Multimodal deep learning and visible-light and hyperspectral imaging for fruit maturity estimation . Sensors (Switzerland) 21 ( 4 ), 1–185. doi:  10.3390/s21041288 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Glassford S. E., Byrne B., Kazarian S. G. (2013). Recent applications of ATR FTIR spectroscopy and imaging to proteins . Biochim. Biophys. Acta - Proteins Proteomics. 1834 ( 12 ), 2849–2858. doi:  10.1016/j.bbapap.2013.07.015 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Golmohammadi A., Afkari-Sayyah A. H. (2013). Long-term storage effects on the physical properties of the potato . Int. J. Food Properties 16 ( 1 ), 104–135. doi:  10.1080/10942912.2010.529978 [ CrossRef ] [ Google Scholar ]
  • Greensill C. V., Walsh K. B. (2000). Remote acceptance probe and illumination configuration for spectral assessment of internal attributes of intact fruit . Measurement Sci. Technol. 11 ( 12 ), 1674–1845. doi:  10.1088/0957-0233/11/12/304 [ CrossRef ] [ Google Scholar ]
  • Guan X., Liu J., Huang K., Kuang J., Liu D. (2019). Evaluation of moisture content in processed apple chips using NIRS and wavelength selection techniques . Infrared Phys. Technol. 98 , 305–310. doi:  10.1016/j.infrared.2019.01.010 [ CrossRef ] [ Google Scholar ]
  • Guthrie J., Walsh K. (1997). Non-invasive assessment of pineapple and mango fruit quality using near infra-red spectroscopy . Aust. J. Exp. Agric. 37 ( 2 ), 253–263. doi:  10.1071/EA96026 [ CrossRef ] [ Google Scholar ]
  • Hara R., Ishigaki M., Kitahama Y., Ozaki Y., Genkawa T. (2018). Excitation wavelength selection for quantitative analysis of carotenoids in tomatoes using raman spectroscopy . Food Chem. 258 , 308–313. doi:  10.1016/j.foodchem.2018.03.089 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Hasanzadeh B., Abbaspour-Gilandeh Y., Soltani-Nazarloo A., Hernández-Hernández M., Gallardo-Bernal Iván, Hernández-Hernández JoséL. (2022). Non-destructive detection of fruit quality parameters using hyperspectral imaging, multiple regression analysis and artificial intelligence . Horticulturae 8 ( 7 ), 1–16. doi:  10.3390/horticulturae8070598 [ CrossRef ] [ Google Scholar ]
  • Homayoonfal M., Malekjani N., Baeghbali V., Ansarifar E., Hedayati S., Jafari S. M. (2022). Optimization of spray drying process parameters for the food bioactive ingredients . Crit. Rev. Food Sci. Nutr. , 1–41. doi: 10.1080/10408398.2022.2156976 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Hong, Chia K. S. (2021). A review on recent near infrared spectroscopic measurement setups and their challenges . Measurement: journal of the international measurement confederation . 171 , 108732. doi:  10.1016/j.measurement.2020.108732 [ CrossRef ] [ Google Scholar ]
  • Hong S. J., Yoon S., Lee J., Jo S. M., Jeong H., Lee Y., et al.. (2022). A comprehensive study for taste and odor characteristics using electronic sensors in broccoli floret with different methods of thermal processing . J. Food Process. Preservation 46 ( 4 ), 1–125. doi:  10.1111/jfpp.16435 [ CrossRef ] [ Google Scholar ]
  • Indiarto R. (2020). Post-harvest handling technologies of tropical fruits: A review . Int. J. Emerging Trends Eng. Res. 8 ( 7 ), 3951–3957. doi:  10.30534/ijeter/2020/165872020 [ CrossRef ] [ Google Scholar ]
  • James J. B., Ngarmsak T, Rolle R. S. (2010). Processing of fresh-cut tropical fruits and vegetables: A technical guide . RAP Publication 2010/16. [ Google Scholar ]
  • Jha S. N., Gunasekaran S. (2010). Authentication of sweetness of mango juice using fourier transform infrared-attenuated total reflection spectroscopy . J. Food Eng. 101 ( 3 ), 337–342. doi:  10.1016/j.jfoodeng.2010.07.019 [ CrossRef ] [ Google Scholar ]
  • Jha S. N., Narsaiah K., Jaiswal P., Bhardwaj R., Gupta M., Kumar R., et al.. (2014). Nondestructive prediction of maturity of mango using near infrared spectroscopy . J. Food Eng. 124 , 152–157. doi:  10.1016/j.jfoodeng.2013.10.012 [ CrossRef ] [ Google Scholar ]
  • Jha S. N., Matsuoka T. (2000). Non-Destructive techniques for quality evaluation of intact fruits and vegetables . Food Sci. Technol. Res. 6 ( 4 ), 248–515. doi:  10.3136/fstr.6.248 [ CrossRef ] [ Google Scholar ]
  • Jodhani K. A., Nataraj M. (2021). Synergistic effect of aloe gel (Aloe vera L.) and lemon (Citrus limon L.) peel extract edible coating on shelf life and quality of banana (Musa spp.) . J. Food Measurement Characterization 15 ( 3 ), 2318–2285. doi:  10.1007/s11694-021-00822-z [ CrossRef ] [ Google Scholar ]
  • Jones R. R., Hooper D. C., Zhang L., Wolverson D., Valev V. K. (2019). Raman techniques: fundamentals and frontiers . Nanoscale Res. Lett. 14 ( 1 ), 1–34. doi:  10.1186/s11671-019-3039-2 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Kamilaris A., Prenafeta-Boldú F. X. (2018). Deep learning in agriculture: A survey . Comput. Electron. Agric. 147 , 70–90. doi:  10.1016/j.compag.2018.02.016 [ CrossRef ] [ Google Scholar ]
  • Kämper W., Trueman S. J., Tahmasbian I., Hosseini Bai S. (2020). Rapid determination of nutrient concentrations in hass avocado fruit by vis/NIR hyperspectral imaging of flesh or skin . Remote Sens. 12 ( 20 ), 1–195. doi:  10.3390/rs12203409 [ CrossRef ] [ Google Scholar ]
  • Kasim N. F. M., Mishra P., Schouten R. E., Woltering E. J., Boer M. P. (2021). Assessing firmness in mango comparing broadband and miniature spectrophotometers . Infrared Phys. Technol. 115 , 103733. doi:  10.1016/j.infrared.2021.103733 [ CrossRef ] [ Google Scholar ]
  • Khan M. H., Saleem Z., Ahmad M., Sohaib A., Ayaz H., Mazzara M., et al.. (2021). Hyperspectral imaging-based unsupervised adulterated red chili content transformation for classification: identification of red chili adulterants . Neural Computing Appl. 33 ( 21 ), 14507–14215. doi:  10.1007/s00521-021-06094-4 [ CrossRef ] [ Google Scholar ]
  • Kirezieva K., Jacxsens L., Uyttendaele M., Martinus A. J. S., Boekel V., Luning P. A. (2013). Assessment of food safety management systems in the global fresh produce chain . Food Res. Int. 52 ( 1 ), 230–425. doi:  10.1016/j.foodres.2013.03.023 [ CrossRef ] [ Google Scholar ]
  • Kusumaningrum D., Lee H., Lohumi S., Mo C., Kim M. S., Kwan Cho B. (2018). Non-destructive technique for determining the viability of soybean (Glycine max) seeds using FT-NIR spectroscopy . J. Sci. Food Agric. 98 ( 5 ), 1734–1425doi:  10.1002/jsfa.8646 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Kusumiyati A. A. M., Suhandy D. (2021). Fast, simultaneous and contactless assessment of intact mango fruit by means of near infrared spectroscopy . AIMS Agric. Food 6 ( 1 ), 172–845. doi:  10.3934/AGRFOOD.2021011 [ CrossRef ] [ Google Scholar ]
  • Kyriacou M. C., Rouphael Y. (2018). Towards a new definition of quality for fresh fruits and vegetables . Scientia Hortic. 234 , 463–469. doi:  10.1016/j.scienta.2017.09.046 [ CrossRef ] [ Google Scholar ]
  • Lan W., Renard C. M. G. C., Jaillais B., Leca A., Bureau S. (2020). Fresh, freeze-dried or cell wall samples: which is the most appropriate to determine chemical, structural and rheological variations during apple processing using ATR-FTIR spectroscopy ? Food Chem. 330 , 127357. doi:  10.1016/j.foodchem.2020.127357 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Larkin P. (2017). Infrared and raman spectroscopy: principles and spectral interpretation (Amsterdam: Elsevier; ). [ Google Scholar ]
  • Lawaetz A. J., Christensen S. M. U., Clausen S. K., Jørnsgaard B., Rasmussen SørenK., Andersen S. B., et al.. (2016). Fast, cross cultivar determination of total carotenoids in intact carrot tissue by raman spectroscopy and partial least squares calibration . Food Chem. 204 , 7–13. doi:  10.1016/j.foodchem.2016.02.107 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Lecun Y., Bengio Y., Hinton G. (2015). Deep learning . Nature 521 , 436–444. doi:  10.1038/nature14539 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Lee J.-D., Shannon J.G., Choung M.-G. (2011). Application of nondestructive measurement to improve soybean quality by near infrared reflectance spectroscopy . Soybean Appl. Technol. 16 , 287–304. doi: 10.5772/15842 [ CrossRef ] [ Google Scholar ]
  • Leiva-Valenzuela G. A., Lu R., Aguilera JoséM. (2013). Prediction of firmness and soluble solids content of blueberries using hyperspectral reflectance imaging . J. Food Eng. 115 ( 1 ), 91–985. doi:  10.1016/j.jfoodeng.2012.10.001 [ CrossRef ] [ Google Scholar ]
  • Li Y. C., Khan F., Jan S. R. U., Verma S., Menon V. G., Kavita, et al.. (2021). A comprehensive survey on machine learning-based big data analytics for ioT-enabled smart healthcare system . Mobile Networks Appl. 26 ( 1 ), 234–525. doi:  10.1007/s11036-020-01700-6 [ CrossRef ] [ Google Scholar ]
  • Li J. L., Sun Da W., Cheng J. Hu. (2016). Recent advances in nondestructive analytical techniques for determining the total soluble solids in fruits: A review . Compr. Rev. Food Sci. Food Saf. 15 ( 5 ), 897–9115. doi:  10.1111/1541-4337.12217 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Li R. Li, Wang M., Liu Y., Zhang B., Zhou J. (2018). Hyperspectral imaging and their applications in the nondestructive quality assessment of fruits and vegetables . Hyperspectral Imaging Agriculture Food Environ. 27–63. doi: 10.1007/intechopen.72250 [ CrossRef ] [ Google Scholar ]
  • Lin H., Yibin Y. (2009). Theory and application of near infrared spectroscopy in assessment of fruit quality: A review . Sens. Instrumentation Food Qual. Saf. 3 ( 2 ), 130–415. doi:  10.1007/s11694-009-9079-z [ CrossRef ] [ Google Scholar ]
  • Liu Y., Pu H., Sun Da W. (2017). Hyperspectral imaging technique for evaluating food quality and safety during various processes: A review of recent applications . Trends Food Sci. Technol. 69 , 25–35. doi:  10.1016/j.tifs.2017.08.013 [ CrossRef ] [ Google Scholar ]
  • Lohumi S., Lee S., Lee H., Cho B. K. (2015). A review of vibrational spectroscopic techniques for the detection of food authenticity and adulteration . Trends Food Sci. Technol. 46 ( 1 ), 85–985. doi:  10.1016/j.tifs.2015.08.003 [ CrossRef ] [ Google Scholar ]
  • López-Maestresalas A., Keresztes J. C., Goodarzi M., Arazuri S., Jarén C., Saeys W. (2016). Non-destructive detection of blackspot in potatoes by vis-NIR and SWIR hyperspectral imaging . Food Control 70 , 229–241. doi:  10.1016/j.foodcont.2016.06.001 [ CrossRef ] [ Google Scholar ]
  • Lorente D., Aleixos N., Gómez-Sanchis J., Cubero S., García-Navarrete O. L., Blasco J. (2012). Recent advances and applications of hyperspectral imaging for fruit and vegetable quality assessment . Food Bioprocess Technol. 5 ( 4 ), 1121–1142. doi:  10.1007/s11947-011-0725-1 [ CrossRef ] [ Google Scholar ]
  • Lu (2017). Light scattering technology for food property, quality and safety assessment (Boca Raton, USA: Crc Press; ), 1–43. [ Google Scholar ]
  • Lu N. S., Hu Y., Fu H. (2014). Detecting citrus fruits with highlight on tree based on fusion of multi-map . Optik 125 ( 8 ), 1903–1975. doi:  10.1016/j.ijleo.2013.04.135 [ CrossRef ] [ Google Scholar ]
  • Lu Y., Huang Y., Lu R. (2017). Innovative hyperspectral imaging-based techniques for quality evaluation of fruits and vegetables: A review . Appl. Sci. (Switzerland) 7 ( 2 ). doi:  10.3390/app7020189 [ CrossRef ] [ Google Scholar ]
  • Lu Y., Saeys W., Kim M., Peng Y., Lu R. (2020). Hyperspectral imaging technology for quality and safety evaluation of horticultural products: A review and celebration of the past 20-year progress . Postharvest Biol. Technol. 170 , 111318. doi:  10.1016/j.postharvbio.2020.111318 [ CrossRef ] [ Google Scholar ]
  • Magwaza L. S., Opara U. L. (2015). Analytical methods for determination of sugars and sweetness of horticultural products-A review . Scientia Hortic. 184 , 179–192. doi:  10.1016/j.scienta.2015.01.001 [ CrossRef ] [ Google Scholar ]
  • Magwaza L. S., Tesfay S. Z. (2015). A review of destructive and non-destructive methods for determining avocado fruit maturity . Food Bioprocess Technol. 8 ( 10 ), 1995–20115. doi:  10.1007/s11947-015-1568-y [ CrossRef ] [ Google Scholar ]
  • Mendy T. K., Misran A., Mahmud T. M. M., Ismail S. I. (2019). Application of aloe vera coating delays ripening and extend the shelf life of papaya fruit . Scientia Hortic. 246 , 769–776. doi:  10.1016/j.scienta.2018.11.054 [ CrossRef ] [ Google Scholar ]
  • Mesa A. R., Chiang J. Y. (2021). Multi-input deep learning model with rgb and hyperspectral imaging for banana grading . Agric. (Switzerland) 11 ( 8 ), 1–18. doi:  10.3390/agriculture11080687 [ CrossRef ] [ Google Scholar ]
  • Metlenkin D. A., Platov Y. T., Platova R. A., Zhirkova E. V., Teneva O. T. (2022). Non-destructive identification of defects and classification of hass avocado fruits with the use of a hyperspectral image . Agron. Res. 20 ( 2 ), 326–340. doi:  10.15159/AR.22.027 [ CrossRef ] [ Google Scholar ]
  • Mishra P., Woltering E., Harchioui N. El. (2020). Improved Prediction of ‘Kent’ Mango Firmness during Ripening by near-Infrared Spectroscopy Supported by Interval Partial Least Square Regression . Infrared Phys. Technol. 110 , 103459. doi:  10.1016/j.infrared.2020.103459 [ CrossRef ] [ Google Scholar ]
  • Moreda G. P., Ortiz-Cañavate J., García-Ramos F. J., Ruiz-Altisent M. (2009). Non-destructive technologies for fruit and vegetable size determination - A review . J. Food Eng. 92 ( 2 ), 119–136. doi:  10.1016/j.jfoodeng.2008.11.004 [ CrossRef ] [ Google Scholar ]
  • Morey R., Ermolenkov A., Payne W. Z., Scheuring D. C., Koym J. W., Isabel Vales M., et al.. (2020). Non-invasive identification of potato varieties and prediction of the origin of tuber cultivation using spatially offset raman spectroscopy . Analytical Bioanalytical Chem. 412 ( 19 ), 4585–4945. doi:  10.1007/s00216-020-02706-5 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Mukhametzyanov R. R., Zaretskaya A. S., Dzhancharova G. K., Platonovskiy N. G., Ivantsova N. N. (2022). “ Russia as a subject of the world market for staple tropical fruits ,” in Proceedings of the international scientific and practical conference strategy of development of regional ecosystems education-science-industry (ISPCR 2021, Springer Nature , Veliky Novgorod, Russia : ) 208 (Ispcr 2021) , 594–602. doi:  10.2991/aebmr.k.220208.084 [ CrossRef ] [ Google Scholar ]
  • Ndlovu P. F., Magwaza L. S., Tesfay S. Z., Mphahlele R. R. (2022). Destructive and rapid non-invasive methods used to detect adulteration of dried powdered horticultural products: A review . Food Res. Int. 157 , 111198. doi:  10.1016/j.foodres.2022.111198 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Neupane C., Koirala A., Walsh K. B. (2022). In-orchard sizing of mango fruit: 1. Comparison of machine vision based methods for on-the-go estimation . Horticulturae 8 ( 12 ), 1–17. doi:  10.3390/horticulturae8121223 [ CrossRef ] [ Google Scholar ]
  • Nicolaï B. M., Beullens K., Bobelyn E., Peirs A., Saeys W., Theron K. I., et al.. (2007). Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review . Postharvest Biol. Technol. 46 ( 2 ), 99–1185. doi:  10.1016/j.postharvbio.2007.06.024 [ CrossRef ] [ Google Scholar ]
  • Nyarko E. K., Vidović I., Radočaj K., Cupec. R. (2018). A nearest neighbor approach for fruit recognition in RGB-D images based on detection of convex surfaces . Expert Syst. Appl. 114 , 454–466. doi:  10.1016/j.eswa.2018.07.048 [ CrossRef ] [ Google Scholar ]
  • Okere E. E., Arendse E., Tsige A. A., Perold W. J., Opara U. L. (2022). Pomegranate quality evaluation using non-destructive approaches: A review . Agric. (Switzerland) 12 ( 12 ), 1–255. doi:  10.3390/agriculture12122034 [ CrossRef ] [ Google Scholar ]
  • Olarewaju O. O., Bertling I., Magwaza L. S. (2016). Non-Destructive evaluation of avocado fruit maturity using near infrared spectroscopy and PLS regression models . Scientia Hortic. 199 , 229–236. doi:  10.1016/j.scienta.2015.12.047 [ CrossRef ] [ Google Scholar ]
  • Ozaki Y. (2021). Infrared spectroscopy—Mid-infrared, near-infrared, and far-infrared/terahertz spectroscopy . Analytical Sci. 37 ( 9 ), 1193–1212. doi:  10.2116/analsci.20R008 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Ozaki Y., Christy A. A., McClure W.F. (2006). Near-infrared spectroscopy in food science and technology (Hoboken, USA: John Wiley & Sons; ). [ Google Scholar ]
  • Pan L., Sun Ye, Xiao H., Gu X., Hu P., Wei Y., et al.. (2017). Hyperspectral imaging with different illumination patterns for the hollowness classification of white radish . Postharvest Biol. Technol. 126 , 40–49. doi:  10.1016/j.postharvbio.2016.12.006 [ CrossRef ] [ Google Scholar ]
  • Pandiselvam R., Kaavya R., Monteagudo S. I., Divya V., Jain S., Khanashyam A. C., et al.. (2022). Contemporary developments and emerging trends in the application of spectroscopy techniques: A particular reference to coconut (Cocos nucifera L.) . Molecules 27 ( 10 ), 1–22. doi:  10.3390/molecules27103250 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Pathare P. B., Rahman M. S. (2022). Nondestructive quality assessment techniques for fresh fruits and vegetables . In Springer Nature . doi:  10.1007/978-981-19-5422-1 [ CrossRef ] [ Google Scholar ]
  • Patrizi B., De Cumis M. S., Viciani S., D’Amato F. (2019). Dioxin and related compound detection: perspectives for optical monitoring . Int. J. Mol. Sci. 20 ( 11 ), 2671. doi:  10.3390/ijms20112671 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Pham U. T., Phan Q. H. T., Nguyen L. P., Luu P. D., Doan T. D., Trinh Ha T., et al.. (2022). Rapid quantitative determination of multiple pesticide residues in mango fruits by surface-enhanced raman spectroscopy . Processes 10 ( 3 ), 1–14. doi:  10.3390/pr10030442 [ CrossRef ] [ Google Scholar ]
  • Phey O., Hashim N., Maringgal B. (2020). Quality evaluation of mango using non-destructive approaches: A review . J. Agric. Food Eng. 1 ( 1 ), 1–85. doi:  10.37865/jafe.2020.0003 [ CrossRef ] [ Google Scholar ]
  • Porat R., Lichter A., Terry L. A., Harker R., Buzby J. (2018). Postharvest losses of fruit and vegetables during retail and in consumers’ Homes: quantifications, causes, and means of prevention . Postharvest Biol. Technol. 139 , 135–149. doi:  10.1016/j.postharvbio.2017.11.019 [ CrossRef ] [ Google Scholar ]
  • Pratiwi E. Z. D., Pahlawan M. F.R., Rahmi D. N., Amanah H. Z., Masithoh R. E. (2023). Non-destructive evaluation of soluble solid content in fruits with various skin thicknesses using visible–shortwave near-infrared spectroscopy . Open Agric. 8 ( 1 ), 1–125. doi:  10.1515/opag-2022-0183 [ CrossRef ] [ Google Scholar ]
  • Pu Y. Y., Feng Y. Ze, Sun Da W. (2015). Recent progress of hyperspectral imaging on quality and safety inspection of fruits and vegetables: A review . Compr. Rev. Food Sci. Food Saf. 14 ( 2 ), 176–885. doi:  10.1111/1541-4337.12123 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Purwanto Y. A., Budiastra I.W., Darmawati E., Arifiya N. (2015). Measurement of starch and soluble solid content in papaya using near infrared spectroscopy . Available Online Www.Jocpr.Com J. Chem. Pharm. Res. 7 ( 6 ), 112–165. [ Google Scholar ]
  • Qin J. (2010). “ Hyperspectral imaging instruments ,” in Hyperspectral imaging for food quality analysis and control (England: Elsevier; ), 129–172. [ Google Scholar ]
  • Qin (2012). “ Hyperspectral and multispectral imaging in the food and beverage industries ,” in Computer vision technology in the food and beverage industries (Delhi: Elsevier; ), 27–63. [ Google Scholar ]
  • Qin M. S.K., Chaoa K., Dhakala S., Chob B.-K., Lohumib C. M. S., Pengd Y., et al.. (2019). Advances in raman spectroscopy and imaging techniques for quality and safety inspection of horticultural products . Postharvest Biol. Technol. 149 , 101–117. doi:  10.1016/j.postharvbio.2018.11.004 [ CrossRef ] [ Google Scholar ]
  • Qin K. C., Kim M. S., Lu R., Burks T. F. (2013). Hyperspectral and multispectral imaging for evaluating food safety and quality . J. Food Eng. 118 ( 2 ), 157–715. doi:  10.1016/j.jfoodeng.2013.04.001 [ CrossRef ] [ Google Scholar ]
  • Rahman A., Cho B. K. (2016). Assessment of seed quality using non-Destructive measurement techniques: A review . Seed Sci. Res. 26 ( 4 ), 285–3055. doi:  10.1017/S0960258516000234 [ CrossRef ] [ Google Scholar ]
  • Raj T. S., Suji H. A. (2019). Post-harvest quality of fresh produce . Adv. Agri. Sci. 129–143. doi: 10.22271/ed.book21 [ CrossRef ] [ Google Scholar ]
  • Rajkumar P., Wang N., EImasry G., Raghavan G. S. V., Gariepy Y. (2012). Studies on banana fruit quality and maturity stages using hyperspectral imaging . J. Food Eng. 108 ( 1 ), 194–200. doi:  10.1016/j.jfoodeng.2011.05.002 [ CrossRef ] [ Google Scholar ]
  • Retamales J. B. (2011). World Temperate Fruit Production: Characteristics and Challenges | Produção Mundial de Frutas de Clima Temperado : Caracteristicas e Desafios . Rev. Bras. Fruticultura 33 (SPEC. ISSU) 33 , 121–130. doi: 10.1590/S0100-29452011000500015 [ CrossRef ] [ Google Scholar ]
  • Rivera N. Ve´lez, Gómez-Sanchis J., Chanona-Pérez J., Carrasco J. José, Millán-Giraldo Mónica, Lorente D., et al.. (2014). Early detection of mechanical damage in mango using NIR hyperspectral images and machine learning . Biosyst. Eng. 122 , 91–98. doi:  10.1016/j.biosystemseng.2014.03.009 [ CrossRef ] [ Google Scholar ]
  • Rostron P., Gaber S., Gaber D. (2016). Raman Spectroscopy, Review . Int. J. Eng. Tech. Res. (IJETR) 6 , 2454–4698. [ Google Scholar ]
  • Ruiz-Altisent M., Ruiz-Garcia L., Moreda G. P., Lu R., Hernandez-Sanchez N., Correa E. C., et al.. (2010). Sensors for product characterization and quality of specialty crops-A review . Comput. Electron. Agric. 74 ( 2 ), 176–945. doi:  10.1016/j.compag.2010.07.002 [ CrossRef ] [ Google Scholar ]
  • Rungpichayapichet P., Mahayothee B., Nagle M., Khuwijitjaru P., Müller J. (2016). Robust NIRS models for non-destructive prediction of postharvest fruit ripeness and quality in mango . Postharvest Biol. Technol. 111 , 31–40. doi:  10.1016/j.postharvbio.2015.07.006 [ CrossRef ] [ Google Scholar ]
  • Ryu M., Ng S. H., Anand V., Lundgaard S., Hu J., Katkus T., et al.. (2021). Attenuated total reflection at THz wavelengths: prospective use of total internal reflection and polariscopy . Appl. Sci. 11 ( 16 ), 76325. doi: 10.3390/app11167632 [ CrossRef ] [ Google Scholar ]
  • Sahu D., Potdar R. M. (2017). Defect identification and maturity detection of mango fruits using image analysis . Am. J. Artif. Intell. 1 ( 1 ), 5–145. doi:  10.11648/j.ajai.20170101.12 [ CrossRef ] [ Google Scholar ]
  • Salehi F. (2020). Recent advances in the modeling and predicting quality parameters of fruits and vegetables during postharvest storage: A review . Int. J. Fruit Sci. 20 ( 3 ), 506–520. doi:  10.1080/15538362.2019.1653810 [ CrossRef ] [ Google Scholar ]
  • Sanchez P. D. C., Hashim N., Shamsudin R., Nor M. Z. M. (2020). Applications of imaging and spectroscopy techniques for non-destructive quality evaluation of potatoes and sweet potatoes: A review . Trends Food Sci. Technol. 96 , 208–221. doi:  10.1016/j.tifs.2019.12.027 [ CrossRef ] [ Google Scholar ]
  • Sarkar T., Chandra B., Viswavidyalaya K., Mani A. (2018) Maturity indices of tropical and sub-tropical fruit crops 38 MATURITY INDICES OF TROPICAL AND SUB-TROPICAL FRUIT CROPS . Available at: https://www.researchgate.net/publication/329266894 .
  • Sebben J. Antônio, Espindola J. da S., Ranzan L., Moura N. F. de, Trierweiler L. F., Trierweiler J. Otávio. (2018). Development of a quantitative approach using raman spectroscopy for carotenoids determination in processed sweet potato . Food Chem. 245 , 12–31. doi:  10.1016/j.foodchem.2017.11.086 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Shewfelt R. L. (2014). “ Measuring quality and maturity ,” in Postharvest handling (New York: Elsevier; ), 387–410. [ Google Scholar ]
  • Si W., Xiong J., Huang Y., Jiang X., Hu D. (2022). Quality assessment of fruits and vegetables based on spatially resolved spectroscopy: A review . Foods 11 ( 9 ), 1–215. doi:  10.3390/foods11091198 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Siesler H. W., Kawata S., Michael Heise H., Ozaki Y. (2008). Near-infrared spectroscopy: principles, instruments, applications (Weinheim, German: John Wiley & Sons; ). [ Google Scholar ]
  • Silva C. E. de F., Abud A. K. de S. (2017). Tropical fruit pulps: processing, product standardization and main control parameters for quality assurance . Braz. Arch. Biol. Technol. 60 , 1–19. doi:  10.1590/1678-4324-2017160209 [ CrossRef ] [ Google Scholar ]
  • Sinanoglou V. J., Tsiaka T., Aouant K., Mouka E., Ladika G., Kritsi E., et al.. (2023). Quality assessment of banana ripening stages by combining analytical methods and image analysis . Applied Sciences (Switzerland) 13 ( 6 ), 3533. [ Google Scholar ]
  • Sirisomboon P. (2018). NIR spectroscopy for quality evaluation of fruits and vegetables . Materials Today: Proc. 5 ( 10 ), 22481–22486. doi:  10.1016/j.matpr.2018.06.619 [ CrossRef ] [ Google Scholar ]
  • Sohaib A. Z., Qureshi W. S., Arslan M., Malik A. U., Alasmary W., Alanazi. E. (2020). Towards fruit maturity estimation using NIR spectroscopy . Infrared Phys. Technology. 111 , 1–17. doi:  10.1016/j.infrared.2020.103479 [ CrossRef ] [ Google Scholar ]
  • Srivichien S., Terdwongworakul A., Teerachaichayut S. (2015). Quantitative prediction of nitrate level in intact pineapple using vis-NIRS . J. Food Eng. 150 , 29–34. doi:  10.1016/j.jfoodeng.2014.11.004 [ CrossRef ] [ Google Scholar ]
  • Su W. H., Bakalis S., Sun Da W. (2019). Chemometrics in tandem with near infrared (NIR) hyperspectral imaging and fourier transform mid infrared (FT-MIR) microspectroscopy for variety identification and cooking loss determination of sweet potato . Biosyst. Eng. 180 , 70–86. doi:  10.1016/j.biosystemseng.2019.01.005 [ CrossRef ] [ Google Scholar ]
  • Su W. H., Sun Da W. (2018). Fourier transform infrared and raman and hyperspectral imaging techniques for quality determinations of powdery foods: A review . Compr. Rev. Food Sci. Food Saf. 17 ( 1 ), 104–225. doi:  10.1111/1541-4337.12314 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Su W.-H., Sun D.-W. (2019). Rapid determination of starch content of potato and sweet potato by using NIR hyperspectral imaging . Hortscience 54 , S38. [ Google Scholar ]
  • Subedi P. P., Walsh K. B. (2011). Assessment of sugar and starch in intact banana and mango fruit by SWNIR spectroscopy . Postharvest Biol. Technol. 62 ( 3 ), 238–245. doi:  10.1016/j.postharvbio.2011.06.014 [ CrossRef ] [ Google Scholar ]
  • Subedi P. P., Walsh K. B. (2020). Assessment of avocado fruit dry matter content using portable near infrared spectroscopy: method and instrumentation optimisation . Postharvest Biol. Technol. 161 , 1–10. doi:  10.1016/j.postharvbio.2019.111078 [ CrossRef ] [ Google Scholar ]
  • Tang T., Zhang M., Mujumdar A. S. (2022). Intelligent detection for fresh-cut fruit and vegetable processing: imaging technology . Compr. Rev. Food Sci. Food Saf. 21 ( 6 ), 5171–5985. doi:  10.1111/1541-4337.13039 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Tata J. S., Kalidindi N. K. V., Katherapaka H., Julakal S. K., Banothu. M. (2022). Real-time quality assurance of fruits and vegetables with artificial intelligence . J. Physics: Conf. Ser. 2325 ( 1 ), 1–13. doi:  10.1088/1742-6596/2325/1/012055 [ CrossRef ] [ Google Scholar ]
  • Torres-Sánchez R., Martínez-Zafra MaríaT., Castillejo N., Guillamón-Frutos A., Artés-Hernández F. (2020). Real-time monitoring system for shelf life estimation of fruit and vegetables . Sensors (Switzerland) 20 ( 7 ), 1–21. doi:  10.3390/s20071860 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Tsuchikawa S., Ma Te, Inagaki T. (2022). Application of near-infrared spectroscopy to agriculture and forestry . Analytical Sci. 38 ( 4 ), 635–425. doi:  10.1007/s44211-022-00106-6 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Uarrota V. G., Pedreschi R. (2022). Mathematical Modelling of Hass Avocado Firmness by Using Destructive and Non-Destructive Devices at Different Maturity Stages and under Two Storage Conditions . Folia Horticulturae. 34 ( 2 ), 139–150. doi: 10.2478/fhort-2022-0011 [ CrossRef ] [ Google Scholar ]
  • Ucat R. C., Dela Cruz J. C. (2019). “ Postharvest grading classification of cavendish banana using deep learning and tensorflow ,” in 2019 international symposium on multimedia and communication technology, ISMAC 2019 1 , 6. doi:  10.1109/ISMAC.2019.8836129 [ CrossRef ] [ Google Scholar ]
  • Wang K., Li Z., Li J., Lin H. (2021). Raman spectroscopic techniques for nondestructive analysis of agri-foods: A state-of-the-art review . Trends Food Sci. Technol. 118 , 490–504. doi:  10.1016/j.tifs.2021.10.010 [ CrossRef ] [ Google Scholar ]
  • Wang Z., Walsh K. B., Verma B. (2017). On-tree mango fruit size estimation using RGB-D images . Sensors (Switzerland) 17 ( 12 ), 1–155. doi:  10.3390/s17122738 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Wang A., Hu D., Xie L. (2014). Comparison of detection modes in terms of the necessity of visible region (VIS) and influence of the peel on soluble solids content (SSC) determination of navel orange using VIS-SWNIR spectroscopy . J. Food Eng. 126 , 126–132. doi:  10.1016/j.jfoodeng.2013.11.011 [ CrossRef ] [ Google Scholar ]
  • Wang M. Hu, Zhai G. (2018). Application of deep learning architectures for accurate and rapid detection of internal mechanical damage of blueberry using hyperspectral transmittance data . Sensors (Switzerland) 18 ( 4 ), 1–145. doi:  10.3390/s18041126 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Wu Di, Sun Da W. (2013). Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: A review - part I: fundamentals . Innovative Food Sci. Emerging Technol. 19 , 1–14. doi:  10.1016/j.ifset.2013.04.014 [ CrossRef ] [ Google Scholar ]
  • Xu H., Ren J., Lin J., Mao S., Xu Z., Chen Z., et al.. (2023). The impact of high-Quality data on the assessment results of visible/near-Infrared hyperspectral imaging and development direction in the food fields: A review J. Food Measurement Characterization 17 ( 3 ), 2988–3004. doi:  10.1007/s11694-023-01822-x [ CrossRef ] [ Google Scholar ]
  • Yahaya, Mardziah O. K., Omar A. F. (2017). Spectroscopy of tropical fruits: sala mango and B10 carambola (Penerbit USM) (Penang, Malaysia: Penerbit USM; ). [ Google Scholar ]
  • Yahaya O. K. M., Matjafri M. Z., Aziz A. A., Omar A. F. (2011). Non-destructive quality evaluation of fruit by color based on RGB LEDs system . 2014 2nd Int. Conf. Electronic Design ICED 2014 1001 , 230–233. doi:  10.1109/ICED.2014.7015804 [ CrossRef ] [ Google Scholar ]
  • Yang, Xu Y. (2021). Applications of deep-learning approaches in horticultural research: A review . Horticulture Res. 8 ( 1 ), 1–31. doi:  10.1038/s41438-021-00560-9 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Yang J., Yin C., Miao Xu, Meng X., Liu Z., Hu L. (2021). Rapid discrimination of adulteration in radix astragali combining diffuse reflectance mid-infrared fourier transform spectroscopy with chemometrics . Spectrochimica Acta - Part A: Mol. Biomolecular Spectrosc. 248 , 119251. doi:  10.1016/j.saa.2020.119251 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Ye D., Sun L., Tan W., Che W., Yang M. (2018). Detecting and classifying minor bruised potato based on hyperspectral imaging . Chemometrics Intelligent Lab. Syst. 177 , 129–139. doi:  10.1016/j.chemolab.2018.04.002 [ CrossRef ] [ Google Scholar ]
  • Yeap K. Ho, Hirasawa K. (2019). Introductory chapter: electromagnetism . Electromagnetic Fields Waves 356 , 3–10. doi: 10.5772/intechopen.85155 [ CrossRef ] [ Google Scholar ]
  • Zainalabidin F. A., Sagrin M. S., Azmi W. N. W., Ghazali A. S. (2019). Optimum postharvest handling-effect of temperature on quality and shelf life of tropical fruits and vegetables . J. Trop. Resour. Sustain. Sci. (JTRSS) 7 ( 1 ), 23–305. doi:  10.47253/jtrss.v7i1.505 [ CrossRef ] [ Google Scholar ]
  • Zakaria L. (2023). Fusarium species associated with diseases of major tropical fruit crops . Horticulturae 9 ( 3 ), 322. doi:  10.3390/horticulturae9030322 [ CrossRef ] [ Google Scholar ]
  • Zhang B., Huang W., Li J., Zhao C., Fan S., Wu J., et al.. (2014). Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: A review . Food Res. Int. 62 , 326–343. doi:  10.1016/j.foodres.2014.03.012 [ CrossRef ] [ Google Scholar ]
  • Zhang L., Huang Y., Sun F., Chen Da, Netzel M., Heather E., et al.. (2021). The effect of maturity and tissue on the ability of mid infrared spectroscopy to predict the geographical origin of banana (Musa cavendish) . Int. J. Food Sci. Technol. 56 ( 6 ), 2621–2275. doi:  10.1111/ijfs.14960 [ CrossRef ] [ Google Scholar ]
  • Zhang J. Y., Lin T., Ying Y. (2021). Food and agro-product quality evaluation based on spectroscopy and deep learning: A review . Trends Food Sci. Technol. 112 , 431–441. doi:  10.1016/j.tifs.2021.04.008 [ CrossRef ] [ Google Scholar ]
  • Zhu D., Ren X., Wei L., Cao X., Ge Y., Liu He, et al.. (2020). Collaborative analysis on difference of apple fruits flavour using electronic nose and electronic tongue . Scientia Hortic. 260 , 108879. doi:  10.1016/j.scienta.2019.108879 [ CrossRef ] [ Google Scholar ]

We will keep fighting for all libraries - stand with us!

Internet Archive Audio

quality analysis of fruits and vegetables

  • This Just In
  • Grateful Dead
  • Old Time Radio
  • 78 RPMs and Cylinder Recordings
  • Audio Books & Poetry
  • Computers, Technology and Science
  • Music, Arts & Culture
  • News & Public Affairs
  • Spirituality & Religion
  • Radio News Archive

quality analysis of fruits and vegetables

  • Flickr Commons
  • Occupy Wall Street Flickr
  • NASA Images
  • Solar System Collection
  • Ames Research Center

quality analysis of fruits and vegetables

  • All Software
  • Old School Emulation
  • MS-DOS Games
  • Historical Software
  • Classic PC Games
  • Software Library
  • Kodi Archive and Support File
  • Vintage Software
  • CD-ROM Software
  • CD-ROM Software Library
  • Software Sites
  • Tucows Software Library
  • Shareware CD-ROMs
  • Software Capsules Compilation
  • CD-ROM Images
  • ZX Spectrum
  • DOOM Level CD

quality analysis of fruits and vegetables

  • Smithsonian Libraries
  • FEDLINK (US)
  • Lincoln Collection
  • American Libraries
  • Canadian Libraries
  • Universal Library
  • Project Gutenberg
  • Children's Library
  • Biodiversity Heritage Library
  • Books by Language
  • Additional Collections

quality analysis of fruits and vegetables

  • Prelinger Archives
  • Democracy Now!
  • Occupy Wall Street
  • TV NSA Clip Library
  • Animation & Cartoons
  • Arts & Music
  • Computers & Technology
  • Cultural & Academic Films
  • Ephemeral Films
  • Sports Videos
  • Videogame Videos
  • Youth Media

Search the history of over 866 billion web pages on the Internet.

Mobile Apps

  • Wayback Machine (iOS)
  • Wayback Machine (Android)

Browser Extensions

Archive-it subscription.

  • Explore the Collections
  • Build Collections

Save Page Now

Capture a web page as it appears now for use as a trusted citation in the future.

Please enter a valid web address

  • Donate Donate icon An illustration of a heart shape

Handbook of analysis and quality control for fruit and vegetable products

Bookreader item preview, share or embed this item, flag this item for.

  • Graphic Violence
  • Explicit Sexual Content
  • Hate Speech
  • Misinformation/Disinformation
  • Marketing/Phishing/Advertising
  • Misleading/Inaccurate/Missing Metadata

inherent cut text on leaf 705

[WorldCat (this item)]

plus-circle Add Review comment Reviews

103 Previews

DOWNLOAD OPTIONS

No suitable files to display here.

PDF access not available for this item.

IN COLLECTIONS

Uploaded by station50.cebu on May 18, 2023

SIMILAR ITEMS (based on metadata)

The Conversation

Prescriptions for fruits and vegetables can improve the health of people with diabetes and other ailments, new study finds

T he health of people with diabetes, hypertension and obesity improved when they could get free fruits and vegetables with a prescription from their doctors and other health professionals.

We found that these patients’ blood sugar levels, blood pressure and weight improved in our new study published in Circulation: Cardiovascular Quality and Outcomes.

The improvements we saw in clinical outcomes could have a meaningful impact on overall health. For example, systolic blood pressure, or blood pressure during heartbeats, decreased more than 8 millimeters of mercury, or mm Hg, while diastolic blood pressure, or blood pressure between heartbeats, decreased nearly 5 mm Hg. For context, this is about half the drop gained through medications that lower blood pressure .

Many U.S. health care providers have been experimenting with “ food is medicine ” programs, which provide free, healthy food to patients – sometimes for a year or more.

This is the largest analysis to date of produce prescription programs, which are one variety of these efforts. They let patients with diet-related illnesses get apples, broccoli, berries, cucumbers and other kinds of fruits and vegetables for free. In Los Angeles, Boise, Houston, Minneapolis and other places where the programs we studied were located, participants selected the produce of their choice at grocery stores or farmers markets using electronic cards or vouchers. They typically received about US$65 per month for four to 10 months.

We pooled data from 22 U.S. produce prescription locations operated by Wholesome Wave , a nonprofit that promotes access to affordable, healthy food. None of the pilots had previously been evaluated. All 4,000 participants either had, or were at risk for, poor cardiometabolic health and were recruited from clinics serving low-income neighborhoods.

Participants in these programs ate more fruits and vegetables. They were also one-third less likely to experience food insecurity – not having enough food to meet basic needs and lead a healthy life.

Why it matters

More than 300,000 Americans die annually of cardiovascular disease and diabetes cases tied to what they eat.

The people in the estimated 13.5 million U.S. households experiencing food insecurity are more likely than others to have cardiometabolic health problems , such as diabetes or heart disease. They also have shorter life expectancy and higher medical costs .

Most Americans, regardless of their income, don’t follow a healthy diet . However, research shows that lower-income Americans tend to eat food that’s slightly worse for their health than those who can afford to spend more.

The 2022 White House Conference on Hunger, Nutrition and Health brought together experts who outlined a national strategy to eradicate food insecurity and reduce diet-related illnesses. It ended with a strategy calling for , among other things, more produce prescription programs.

The last White House conference on hunger and nutrition, which occurred over 50 years earlier, led to significant and lasting changes in U.S. food policies . The National School Lunch Program expanded and the Special Supplemental Nutrition Program for Women, Infants and Children , known as WIC, was created.

Within a year of the latest conference, two government agencies – the Indian Health Service and the Veterans Health Administration – announced produce prescription pilots. Eight state Medicaid programs have received or applied for federal waivers that would allow Medicaid to pay for produce prescriptions for up to six months for some people. However, these programs remain unavailable to most Americans who might benefit.

What’s next

We are evaluating “food is medicine” pilots funded by the Flexible Services Program in Massachusetts’ Medicaid program. We are also running a large, randomized controlled trial, in which one group of patients with cancer will get free home-delivered meals and another will receive standard care.

The Research Brief is a short take on interesting academic work.

This article is republished from The Conversation , a nonprofit news site dedicated to sharing ideas from academic experts.

  • Food is medicine: How US policy is shifting toward nutrition for better health
  • A hospital that prescribes free nutritious food to families who need more than medical care

Kurt Hager volunteers as a steering committee member for the National Produce Prescription Collaborative.

Fang Fang Zhang receives funding from the Rockefeller Foundation and East Bay Community Foundation for this work.

Prescriptions for fruits and vegetables can improve the health of people with diabetes and other ailments, new study finds

Book cover

Advances in Interdisciplinary Engineering pp 591–599 Cite as

Disease Detection and Quality Analysis of Fruits and Vegetables

  • Shweta Tyagi 13 ,
  • Anmol Uppal 13 ,
  • Rishi Kumar 14 &
  • Seema Sharma 13  
  • Conference paper
  • First Online: 13 April 2021

665 Accesses

Part of the Lecture Notes in Mechanical Engineering book series (LNME)

The growing requirement for object recognition techniques which are more “robust and efficient”, is in great demand and highly researched field. Checking the quality of any fruit or vegetable is tedious task and time consuming. The diseases present in the fruits and vegetables decreases the quality and the productivity. There is more involvement of scientists, mall owners and labor to identify the defected part in vegetables and fruits. This whole process consumes a lot of time which in turn damage the rest of production and result cataclysmic for farmers. To reduce the problem, we propose a method for the same as Disease Detection and Quality Analysis of Fruits and Vegetables. Implementing this proposed method can automate quality analysis and disease detection, furthermore consuming less time which in turn can be very beneficial and make it work more efficiently. It has tremendous real-life deployments which includes usage with wide of range processes and systems. This method can be used by the industrial systems or even in the smart home systems, which has even a wider scope. The industries which can use this system are cold storage market and farming industry to analyze fruits and vegetable disease. In addition, the method is entirely powerful against other systems because of the great level of automation of disease detection, which helps the medical research domain. Execution of this application will be a new addition automated and smart procedures.

  • Food and vegetable diseases
  • Medical research
  • Quality of fruits and vegetables
  • Disease detection
  • Automate quality analysis

This is a preview of subscription content, log in via an institution .

Buying options

  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
  • Available as EPUB and PDF
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
  • Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Cubero S, Aleixos N, Moltó E et al (2011) Advances in machine vision applications for automatic inspection and quality evaluation of fruits and vegetables. Food Bioprocess Technol 4:487–504

Article   Google Scholar  

Oji R (2012) An automatic algorithm for object recognition and detection based on ASIFT keypoints. Sig Image Process: An Int J (SIPIJ) 3(5):29–39

Google Scholar  

Fergus R, Perona P, Zisserman A (2007) Weakly supervised scale-invariant learning of models for visual recognition. Int J Comput Vision 71:273–303

Tripathi MK, Maktedar DD (2016) Recent machine learning based approaches for disease detection and classification of agricultural products. In: International conference on computing communication control and automation, Pune

Bhargava A, Bansal A (2018) Fruits and vegetables quality evaluation using computer vision: a review. J King Saud Univ—Comput Inf Sci. In press corrected proof

Singla D, Singh A, Gupta R (2018) Texture analysis of fruits for its deteriorated classification. In: Woungang I, Dhurandher S (eds) International conference on wireless, intelligent, and distributed environment for communication

Patil M, Langar G, Jain P, Panchal N (2020) Disease detection using artificial intelligence and machine learning. Int J Adv Sci Res Eng Trends, July

Ghazalli SA (2019) Image analysis techniques for ripeness detection of palm oil fresh fruit bunches. ELEKTRIKA-J Electr Eng 18(3):57–62

Shectman E, Irani M (2007) Matching local self-similarities across images and videos. In: IEEE International conference on computer vision and pattern recognition, pp 1–8. IEEE

Ming-Hong C, Zhang G, Xia H, Luo Q (2009) PIAGENG 2009 image processing and photonics for agricultural engineering. In: International conference on photonics and image in agriculture engineering (PIAGENG 2009), Zhangjiajie, China.

Andre C, Rankine D, Taylor M, Nielsen D, Cohen J (2016) Increasing accuracy and automation of fractional vegetation cover estimation from digital photographs. Remote Sens 8(7):474

Abbott JA (1999) Quality measurement of fruits and vegetables. Postharvest Biol Technol 15(3):207–225

Butz P, Hofmann C, Tauscher B (2005) Recent developments in noninvasive techniques for fresh fruit and vegetable internal quality analysis. J Food Sci 70(9):R131–R141

Blasco J, Aleixos N, Molto E (2003) Machine vision system for quality grading of fruit. Biosys Eng 85(4):415–423

Zhang B, Huang W, Li J, Zhao C, Fan S, Wu J, Liu C (2014) Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables. Food Res Int 62:326–343

Jiangbo L, Xiuqin R, Yibin Y (2011) Detection of common defects on oranges using hyperspectral reflectance imaging. Comput Electron Agri 78(1):38–48

Viola P, Jones M (2004) Robust real-time object detection. Int J Comput Vision 57(2):137–154

Papageorgiou C, Poggio T (2000) A trainable system for object detection. Int J Comput Vision 38(1):15–33

Download references

Author information

Authors and affiliations.

Department of Computer Science and Engineering, ASET, Amity University, Noida, India

Shweta Tyagi, Anmol Uppal & Seema Sharma

Department of CIS, Universiti Teknologi Petronas, Seri Iskandar, Malaysia

Rishi Kumar

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Shweta Tyagi .

Editor information

Editors and affiliations.

Department of Mechanical Engineering, Amity School of Engineering and Technology, Noida, India

Niraj Kumar

Budapest University of Technology and Economics, Budapest, Hungary

Szalay Tibor

Rahul Sindhwani

Changwon National University, Changwon, Korea (Republic of)

Priyank Srivastava

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper.

Tyagi, S., Uppal, A., Kumar, R., Sharma, S. (2021). Disease Detection and Quality Analysis of Fruits and Vegetables. In: Kumar, N., Tibor, S., Sindhwani, R., Lee, J., Srivastava, P. (eds) Advances in Interdisciplinary Engineering. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-9956-9_58

Download citation

DOI : https://doi.org/10.1007/978-981-15-9956-9_58

Published : 13 April 2021

Publisher Name : Springer, Singapore

Print ISBN : 978-981-15-9955-2

Online ISBN : 978-981-15-9956-9

eBook Packages : Intelligent Technologies and Robotics Intelligent Technologies and Robotics (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • Publish with us

Policies and ethics

  • Find a journal
  • Track your research

REVIEW article

Advancement of non-destructive spectral measurements for the quality of major tropical fruits and vegetables: a review.

Umuhoza Aline

  • 1 Department of Agricultural Machinery Engineering, Chungnam National University, Daejeon, Republic of Korea
  • 2 Department of Smart Agricultural Systems, Chungnam National University, Daejeon, Republic of Korea
  • 3 Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD, United States

The quality of tropical fruits and vegetables and the expanding global interest in eating healthy foods have resulted in the continual development of reliable, quick, and cost-effective quality assurance methods. The present review discusses the advancement of non-destructive spectral measurements for evaluating the quality of major tropical fruits and vegetables. Fourier transform infrared (FTIR), Near-infrared (NIR), Raman spectroscopy, and hyperspectral imaging (HSI) were used to monitor the external and internal parameters of papaya, pineapple, avocado, mango, and banana. The ability of HSI to detect both spectral and spatial dimensions proved its efficiency in measuring external qualities such as grading 516 bananas, and defects in 10 mangoes and 10 avocados with 98.45%, 97.95%, and 99.9%, respectively. All of the techniques effectively assessed internal characteristics such as total soluble solids (TSS), soluble solid content (SSC), and moisture content (MC), with the exception of NIR, which was found to have limited penetration depth for fruits and vegetables with thick rinds or skins, including avocado, pineapple, and banana. The appropriate selection of NIR optical geometry and wavelength range can help to improve the prediction accuracy of these crops. The advancement of spectral measurements combined with machine learning and deep learning technologies have increased the efficiency of estimating the six maturity stages of papaya fruit, from the unripe to the overripe stages, with F1 scores of up to 0.90 by feature concatenation of data developed by HSI and visible light. The presented findings in the technological advancements of non-destructive spectral measurements offer promising quality assurance for tropical fruits and vegetables.

1 Introduction

Tropical fruits and vegetables are agricultural crops that are typically grown in tropical regions where the climate is warm, with temperatures ranging from 20 to 35 0 C ( Bahadur et al., 2020 ). Tropical regions are found amidst the tropics of Cancer and Capricorn, and encompass equatorial zones in Oceania, Asia, Africa, Central and South America, and the Caribbean ( Zakaria, 2023 ). Crops grown naturally in such weather conditions provide essential minerals, water, fiber, and vitamins that contribute significantly to the well-being of humans by safeguarding against ailments such as diabetes, hypertension, and cancer ( Emelike and Akusu, 2019 ).

The agricultural revolution and the adaptation of numerous tropical plants to regions outside of their natural range have muddied their classification, and little is known about what properly defines and distinguishes tropical fruits and vegetables from their temperate counterparts ( Indiarto, 2020 ). Fernandes et al. ( Fernandes et al., 2011 ) described crop classification according to size, acidity, seed type, and bearing. Included among alkaline crops are apples, bananas, peaches, cherries, persimmon, and litchi ( Fernandes et al., 2011 ). Acidic crops include strawberry, orange, kiwi, pineapple, lemon, star fruit, and logan, whereas sub-acidic examples are mango, pear, blackberry, papaya, blueberry, cherimoya, and mulberry ( Fernandes et al., 2011 ). Chakraborty et al. ( Chakraborty et al., 2014 ) agreed and structured the classification of tropical fruits based on that of Fernandes. Sarkar et al. ( Sarkar et al., 2018 ) reported classification system according to maturity stage by means of ethylene gas emission and respiration rate, including both climacteric and non-climacteric tropical produce ( Sarkar et al., 2018 ). Tropical climacteric produce such as avocado, apple, pear, mango, papaya, broccoli, banana, kiwi, and tomato undergoes maturation in correlation with an escalation in their respiration rate and the release of ethylene gas ( Indiarto, 2020 ), whereas tropical non-climacteric crops such as grape, berry, citrus, litchi, strawberry, raspberry, pumpkin, watermelon, cucumber, and pineapple do not undergo an elevation in their respiration rate as they reach maturity ( Indiarto, 2020 ). The contrasting report of Retamales et al. ( Retamales, 2011 ) centers around the production of temperate crops worldwide. In this report, apple, raspberry, pear, peach, kiwi, blueberry, strawberry and plum were considered as temperate fruits ( Retamales, 2011 ). In addition, Benichou et al. ( Benichou et al., 2018 ) have also classified temperate fruits as tree (apple, plum, pear and peach), vine (grape and kiwi), and small fruits such as raspberry, blueberry and currant ( Benichou et al., 2018 ).

Papaya, pineapple, avocado, mango, and banana are considered to be major tropical fruits globally ( Mukhametzyanov et al., 2022 ). According to a market review prediction for the years 2013 to 2022 by the Food and Agriculture Organization of the United Nations (FAO), the most exported tropical fruits globally from Central America and the Caribbean, South America and Asia, Africa, and others in millions of tons were papaya, pineapple, avocado and mango with 3.7, 3.2, 2.3, and 2.1, respectively ( Altendorf, 2019 ). On the other hand, recent data have shown that global vegetable production increased by 68% between 2000 and 2021 ( FAO, 2022 ). Because of the continuous and emergent demand for tropical fruits and vegetables worldwide, the present emphasis is on quality assurance in relation to end-user inclinations and commercial standards ( Silva and Abud, 2017 ). The quality of tropical fruits and vegetables is characterized by both external and internal parameters ( Jha and Matsuoka, 2000 ). External parameters namely color, defects, size and shape depend on not only the appearance of the product, but also on the standards set ( Cubero et al., 2016 ), whereas internal parameters such as nutritional value, internal defects, flavor, and texture are subjective to physicochemical composition and climate change ( Zainalabidin et al., 2019 ). The quality of fruits and vegetables influences consumer preference and is directly or indirectly linked with further value-addition and processing technologies ( James et al., 2010 ).

Several studies have identified postharvest losses as the most prominent factor among the origins of crop quality deterioration ( Porat et al., 2018 ; Etana, 2019 ; Ahmad et al., 2021 ). Adding to that, high temperature and relative humidity are mentioned in the biological and chemical degradation of produce freshness, which affects sweetness, flavor, weight, turgor, and nutritional value ( Elik et al., 2019 ). However, past reports indicated that low-temperature cooling systems and edible coating materials can be used to maintain and monitor the quality of these crops ( Mendy et al., 2019 ; Jodhani and Nataraj, 2021 ). Conventional methods relying on the quantification of different quality traits such as dry matter content, oil content, and moisture content have also been reported in the study of quality parameters of fruits and vegetables; however, these methods were found to be undesirable, destructive, time-consuming, and labor-intensive ( Magwaza and Tesfay, 2015 ; Kyriacou and Rouphael, 2018 ). Therefore, the application of non-destructive bio-sensing methods as a promising alternative for evaluating the value of tropical produce has been adopted ( Ndlovu et al., 2022 ; Okere et al., 2022 ).

Computer vision and popular pre-trained convolutional neural network (CNN) models have been used as recognition systems to sort and grade different fruits and vegetables, especially in supermarkets, regarding their variety and species ( Dubey and Jalal, 2012 ). However, computer vision can only assess external quality attributes due to the lack of spectral information ( Rahman and Cho, 2016 ; Bhargava and Bansal, 2021 ). Acoustic emission technology involves the mechanical destruction of produce when subjected to mechanical or thermal stimulus ( Aboonajmi et al., 2015 ) and is not appropriate for all categories of fruits and vegetables ( Adedeji et al, 2020 ). Extensive works have been published on the evaluation of fruits and vegetables by spectral measurements such as Fourier transform infrared (FTIR) spectroscopy ( Egidio et al., 2009 ), Near-infrared (NIR), Raman spectroscopy ( Pandiselvam et al., 2022 ), and hyperspectral imaging (HSI) ( Wang and Zhai, 2018 ). Generally, these reports have concentrated on the utilization of spectral measurements for determining targeted quality parameters of a particular fruit or vegetable variety. For instance, visible and near-infrared spectroscopy was used to investigate the internal browning in mango fruits ( Gabriëls et al., 2020 ). Ali et al. ( Ali et al., 2023 ) investigated FTIR, NIR, and machine vision in the quality monitoring of pineapples. Metlenkin et al. ( Metlenkin et al., 2022 ) distinguished Hass avocado fruits by defects using hyperspectral imaging (HSI). The question revolves around the practical utilization of these approaches and the challenges associated with improving data processing speed and in-line implementation ( Cortés et al., 2019 ; Si et al., 2022 ). Quick hardware and software are required to fulfill the demands of swift analysis for extensive hyperspectral datasets ( Xu et al., 2023 ) and machine learning algorithms, especially those relying on deep learning act as black boxes rather than using interpretability models for high-stakes decisions ( Caceres-Hernandez et al., 2023 ).

The present review highlights the current advances in non-destructive spectral measurements for quality assessment, specifically for major tropical fruits and vegetables. The quality parameters of these tropical produces are covered first. The discussion on each of the spectral measurements, the tropical crops used, and the specific findings obtained from various studies, which are summarized in Table 1 , follows and can deliver valuable information on the capabilities and efficiency of these techniques. In addition, the merits and demerits of each of these spectral measurements, which are presented in Table 2 , will guide future researchers in selecting the proper evaluation method when evaluating the quality of tropical produces. To facilitate comprehension and quick understanding of key terminologies involved, the list of abbreviations and definitions contained in the paper is presented in Table 3 .

www.frontiersin.org

Table 1 A comparison of the application of various non-destructive spectral measurements in the quality assessment of tropical fruits and vegetables.

www.frontiersin.org

Table 2 Merits and demerits of non-destructive spectral measurements in the quality control of tropical fruits and vegetables.

www.frontiersin.org

Table 3 List of abbreviations and acronyms used in the paper.

2 Quality inspection of Tropical fruits and vegetables

Quality inspection is the process of evaluating specific parameters of fruits and vegetables to ensure required quality standards ( Phey et al., 2020 ). The intention of quality inspection is to detect any internal or external characteristics that can aid in identifying both standard quality parameters and defects or non-conformities that can affect the safety of fruits and vegetables or their usability in particular functions such as diets, trade, and industrial chains ( Kirezieva et al., 2013 ).

2.1 External quality of tropical fruits and vegetables

The appearance of fruits and vegetables is a sensory attribute that directly influences the perceived worth of the produce for consumers ( Zhang et al., 2014 ). The external quality of tropical crops is indicated by a number of factors, including size, shape, color, and external defects, as shown in Table 4 ( Ganiron, 2014 ). The size and shape are two complementary factors that differ depending on the variety of the plant and are both assessed in relation to market grading standards ( Abbaszadeh et al., 2013 ). The size is determined by measuring area, perimeter, length, and width, which is more complex due to the morphological irregularities of tropical crops natural state ( Cubero et al., 2011 ). Moreda et al. ( Moreda et al., 2009 ) described some non-invasive systems for assessing the size of fruits and vegetables. The systems are based on (1) measuring the volume of the gap between the fruit and the outer casing of an embracing gauge; (2) measuring the distance between a radiation source and the fruit contour, where this distance is computed from the time of flight (TOF) of the propagated waves; (3) light obstruction by barriers or blockades of light; (4) 2D and 3D machine vision systems ( Moreda et al., 2009 ).

www.frontiersin.org

Table 4 The external quality parameters of tropical fruits and vegetables.

Wang et al. ( Wang et al., 2017 ) evaluated mango size by RGB–D (depth) imaging and time-of-flight camera imaging system. The camera-to-fruit distance was determined using three methods for fruit sizing from images: stereo vision camera, RGB–D camera and a time-of-flight laser rangefinder ( Wang et al., 2017 ). The obtained length and width values were good with RMSE of 4.9mm and 4.3mm respectively. It is cost-effective and simple to use; however, it pertains non-occluded fruit only and cannot be utilized in direct sunlight ( Wang et al., 2017 ). Neupane et al. ( Neupane et al., 2022 ) replicated the work of Wang by suggesting the use of partly occluded fruit. To obtain the linear length of the fruits, bounding box dimensions of an instance segmentation model (Mask R-CNN) was applied to canopy images ( Neupane et al., 2022 ). The findings were good with RMSE values of 4.7 mm and 5.1 mm for Honey Gold and Keitt mango varieties, respectively ( Neupane et al., 2022 ). Sanchez et al. ( Sanchez et al., 2020 ) investigated spectroscopic and depth imaging techniques combined with machine vision to estimate the length, width, thickness, and volume of sweet potato and potato. When the correct size group was graded, the method had a high accuracy of 90% ( Sanchez et al., 2020 ).

Color is an external quality trait that depends on the maturity of produce and is subjective to internal features such as taste, perception, and pleasantness of fruits and vegetables ( Yahaya et al, 2017 ). Calorimeters evaluate color by measuring the typical surface area of the product and detects the color space values L*, a*, and b* which are based on the human color perception theory ( Aguilar-Hernández et al., 2021 ). The capability of infrared thermal imaging approaches was investigated in the measurement of pineapple color. In this investigation, the L*, a*, and b* mean values for calorimeter increased by (P < 0.05) ( Ali et al., 2022 ). The optical fiber sensors mounted with RGB LEDs were also used to evaluate the color of mangoes, giving R 2 = 0.879 ( Yahaya et al., 2011 ).

External defects include the evidence of rot, bruising, crushing, shriveling, and wilting due to water loss which impact market value and the price of the fruits and vegetables ( Raj and Suji, 2019 ). These defects can be recognized and monitored through the appearance of the crop by qualified personnel relying on subjective evaluation, which may result in human errors ( Ali et al., 2023 ). Sahu et al. ( Sahu and Potdar, 2017 ) proposed a digital image analysis algorithm for detecting exterior defects in mango fruit. Surface defects such as scars and black patches were used to detect defective mango fruits, and were recognized by extracting the contours of damaged areas ( Sahu and Potdar, 2017 ). The damaged area was then filled to identify its location in the image as the basis for discrimination. Sahu and colleagues achieved good accuracy but advocated the use of optimal and adaptive threshold approaches for segmenting mango fruits from image backgrounds ( Sahu and Potdar, 2017 ).

2.2 Internal quality of tropical fruits and vegetables

The internal qualities of fruits and vegetables are also termed hidden qualities and are determined by texture, nutrients, internal defects, and flavor, as presented in Table 5 ( Shewfelt, 2014 ). Different fruits and vegetables usually have different textures, which are characterized by their firmness, crispness, and crunchiness ( Fillion and Kilcast, 2002 ). The assessment of fruit and vegetable firmness, a vital quality characteristic related to texture, can be achieved through sensory measurements ( Magwaza and Opara, 2015 ). The texture is measured with a penetrometer by putting a probe tip installed on the texture analyzer into fruit tissue at a specific speed and depth so as to exert the most force ( Ali et al., 2017 ). Uarrota et al. ( Uarrota and Pedreschi, 2022 ) used a non-destructive texture analyzer to determine the firmness of avocado under different storage conditions. Enough data were required to construct the best model allowing an extension to the model firmness of avocado ( Uarrota and Pedreschi, 2022 ). Kasim et al. ( Kasim et al., 2021 ) compared laboratory-based (305-1713 nm) and portable-based (740-1070 nm) NIR spectrometers to determine mango firmness ( Kasim et al., 2021 ). The results showed that portable and laboratory-based NIR instruments performed similar in respect of R 2 p. Compared to the laboratory-based instrument, the RMSEP of the portable NIR was higher ( Kasim et al., 2021 ).

www.frontiersin.org

Table 5 The internal quality parameters of tropical fruits and vegetables.

Nutritional value, such as the sugar content related with vitamins and minerals, comprises the main constituents of soluble solids content (SSC), total soluble solids (TSS), and total acidity (TA) ( Leiva-Valenzuela et al., 2013 ). Aziz et al. ( Aziz et al., 2021 ) evaluated the relationship between TSS and the capacitance of papaya using capacitance-sensing techniques ( Aziz et al., 2021 ). A refractometer was used as part of a destructive technique to predict the reference values of moisture and TSS content. Capacitive sensing was then tested as non-destructive approach for the evaluation of output voltage and capacitance of papaya ( Aziz et al., 2021 ). Aziz observed a good correlation between destructive and non-destructive techniques, with R 2 of 0.9434 and 0.9177 for moisture and TSS content, respectively ( Aziz et al., 2021 ). The usefulness of NIR spectroscopy was demonstrated in the determination of starch and soluble solid contents of papaya ( Purwanto et al., 2015 ). Srivichien and colleagues tested the nitrates in pineapples using Vis–NIR (600-1200 nm) spectroscopy, yielding an R value of 0.95 ( Srivichien et al., 2015 ). However, due to the big size and the change in nitrate levels, many scans were needed on different areas of pineapple ( Srivichien et al., 2015 ). In the study to predict starch content of sweet potatoes and potatoes, hyperspectral imaging was applied by Su et al. ( Su and Sun, 2019 ). Su developed partial least squares regression (PLSR) models at full-wavelength referring to spectral profiles and observed reference values, resulting in a high accuracy and an R 2 P of 0.963 ( Su and Sun, 2019 ).

Internal defects are detected as internal injury such as rot and water core inside the flesh of the fruits and vegetables due to postharvest problems( Ruiz-Altisent et al., 2010 ). Flavor or taste is defined by the sugar (sweetness), acidity (sourness), bitterness, and saltiness perceived by the tongue and nose ( Zhu et al., 2020 ). It is, therefore, measured subjectively through oral testing or smelling, or by the conventional technical quantification of compounds such as liquid and gas chromatography ( Yahaya et al, 2017 ). Korean universities conducted research on the taste and odor properties of broccoli using electronic sensors ( Hong et al., 2022 ). For electronic tongue analysis, thermal processing boosted sourness and umami tastes while decreasing saltiness, sweetness, and bitterness ( Hong et al., 2022 ). Therefore, the capability of non-destructive spectral measurement methods to assess inside parameters is important to maintain the flesh quality of tropical fruits and vegetables.

3 Non-destructive spectral measurements for the quality evaluation of tropical fruits and vegetables

Non-destructive techniques for quality monitoring of tropical fruits and vegetables refer to the process of inspecting their external and internal properties without causing damage or changing their physical and internal status ( El-Mesery et al., 2019 ). The potential for employing spectral measurement approaches in the quality control of fruits and vegetables is growing enormously ( Escárate et al., 2022 ). The reason is that these approaches are non-destructive, fast and accurate, capable for both quantitative and qualitative analysis, thereby requiring minimal sample preparation ( Cozzolino, 2022 ). We divided non-destructive spectral measurements into two categories: (1) spectral-based approaches (FTIR, NIR, and Raman spectroscopy) and (2) imaging-based approaches (HSI), as shown in Figure 1 .

www.frontiersin.org

Figure 1 The schematic diagram of commonly used non-destructive spectral measurements.

3.1 Spectral-based approaches

Spectral measurement refers to effective techniques used to study the quality parameters of various agricultural materials including tropical fruits and vegetables by investigating light, sound, or particles that are emitted, absorbed, or scattered during measurement ( Pathare and Rahman, 2022 ). Spectroscopic techniques based on FTIR, NIR, and Raman have been successful and popular in the detection of quality parameters of fruits and vegetables ( Dasenaki and Thomaidis, 2019 ). Various research works have used spectral techniques focusing on fruits and vegetables, such as in the fast determination of the sugar and acid composition of citrus ( Clark, 2016 ), assessment of primary sugars and amino acids in raw potato tubers ( Ayvaz et al., 2015 ), and determination of nutrients and moisture content of fruits and vegetables ( Sirisomboon, 2018 ). Quality parameters of tropical crops can be assessed by one of—or a sequence of—the above complementary techniques, which are distinguished depending on the infrared region (IR) they occupy and the molecular vibrations they detect ( Bureau et al., 2019 ). The infrared region of the electromagnetic spectrum, presented in Figure 2 , is separated into three sections, namely near-infrared (NIR), mid-infrared (MIR), and far-infrared (FIR) ( Yeap and Hirasawa, 2019 ). Mango maturity has been predicted using the near-infrared (NIR) spectral region of 1200-2200 nm ( Jha et al., 2014 ). The mid-infrared (MIR) spectral range of from 2500 to 25000 nm has been used in the prediction of banana maturity and geographical origin by Zhang et al. ( Zhang et al., 2021 ), and in the measurement of soluble solids, total acids, and total anthocyanin in berries ( Clark et al., 2018 ). Far-infrared (FIR) ranges have often been reported to be between 25000 and 300000 nm ( Larkin, 2017 ). However, FIR applications are not clearly defined and are limited due to challenges in developing FIR instrumentation; furthermore, the band assignments of low-frequency vibrational modes are not straightforward ( Ozaki, 2021 ). These spectral ranges are based on their relationship to the visible spectrum, which falls between 380 and 780 nm ( Su and Sun, 2018 ).

www.frontiersin.org

Figure 2 Modified diagram showing the infrared regions of the electromagnetic spectrum ( Yeap and Hirasawa, 2019 ), ( Aboud et al., 2019 ).

3.1.1 Fourier transform infrared spectroscopy

FTIR is a form of vibrational spectroscopy that uses light interference to identify the chemical composition of scanned samples by producing infrared absorption or emission spectra ( Larkin, 2017 ). On the electromagnetic spectrum, FTIR operates in the MIR region (2500 to 25000nm) and generates fruit or vegetable chemical profile by capturing the principle vibrational and rotational stretching modes of molecules ( Lohumi et al., 2015 ). FTIR spectroscopy comprises of an infrared light source, interferometer, sample, and detector, shown in Figure 3 . The principal part is the interferometer which is made up of three components: the beam splitter, collimator, and the two mirror (fixed and movable mirror) ( Patrizi and Cumis, 2019 ). When the radiation from the light source passes through the collimator, strikes the beam splitter which ideally divide it into two beams. The first beam hits the static mirror, and is reflected back; while the second hits the movable mirror where it enters through the sample toward the detector ( Blum and Harald, 2012 ).

www.frontiersin.org

Figure 3 Modified diagram of FTIR spectroscopy taking banana as sample ( Patrizi and Cumis, 2019 ).

The FTIR associated with attenuated total reflection (ATR-FTIR) has recently gained importance ( Chan and Kazarian, 2016 ). The ATR works under the principle of total internal reflectance where infrared light interacts with the sample of high refractive index only at the point where infrared light is reflected ( Ryu et al., 2021 ). Unlike transmission methods, the ATR-FTIR technique can be used to study solid, liquid, and paste samples with minimal sample preparation ( Glassford et al., 2013 ).The combination of ATR-FTIR and chemometrics was promising in the assessment of added sugar content, (ASC), total soluble solids (TSS) and real juice content (RJC) of fresh and commercial mango juice ( Jha and Gunasekaran, 2010 ). PLS and MLR models resulted into accuracy of 0.99 and 0.98 respectively ( Jha and Gunasekaran, 2010 ). Canteri et al. ( Canteri et al., 2019 ) have used ATR-FTIR to evaluate the cell wall compositions of 29 species of fruits and vegetables as freeze-dried powders and alcohol-insoluble solids. The results were accurate, with determination coefficient R 2 ≥ 0.9 ( Canteri et al., 2019 ). Recently, Sinanoglou et al. ( Sinanoglou et al., 2023 ) conducted the evaluation of both peel and fresh banana ripening stage by ATR-FTIR, along with image analysis, discriminant and statistical analysis ( Sinanoglou et al., 2023 ). The computed features were accurate enough to separate ripening stages; however, monitoring of the banana ripening process was highly reliant on the instrument employed for image analysis such as digital cameras, smartphones, and electronic noses ( Sinanoglou et al., 2023 ).

3.1.2 Near-Infrared spectroscopy

NIR is used to rapidly ascertain the chemical constitution of materials according to overtones and harmonic or combination bands of specific functional groups ( Kusumaningrum et al., 2018 ). Those overtones and combinations of vibrational bands characterized by C–H, O–H, and N–H are gained by NIR in the wavelength region of 780-2500nm ( Ozaki et al., 2006 ). Tsuchikawa et al. ( Tsuchikawa et al., 2022 ) described NIR as a spectroscopic method that is suitable for samples of high water content, including fruits and vegetables ( Tsuchikawa et al., 2022 ). NIR spectroscopy consists of a light source, sample accessory, monochromator (grating), detector, and optical components such as lenses and optical fibers, as shown in Figure 4 ( Lee et al., 2011 ).

www.frontiersin.org

Figure 4 Modified diagram of NIR spectroscopy, taking avocado as sample ( Chandrasekaran et al., 2019 ).

The illumination of NIR light to the sample occurs in three ways: reflectance, interactance and transmittance ( Wang et al., 2014 ). According to Hong and colleagues, reflectance employs high light energy, has no contact with the fruit surface, and the source and sensor are placed at a specified angle ( Hong and Chia, 2021 ). Specular reflectance and diffuse reflectance are two types of reflectance measurement. Specular reflectance, which occurs when the incident and reflected angles are same, detects nothing from the inside part of the fruit ( Hong and Chia, 2021 ); While the capacity of diffuse reflectance to constrain light dispersion into solid samples allows the acquisition of interior fruit information ( Tang et al., 2022 ). Mango TSS, firmness, TA, and ripeness index (RPI) were effectively measured by NIR diffuse reflectance, with R 2 of 0.9; 0.82; 0.74; and 0.8, respectively. The effect of changes in physicochemical properties of mango during ripening, on the other hand was highlighted ( Rungpichayapichet et al., 2016 ). Kusumiyati et al. ( Kusumiyati and Suhandy, 2021 ) also evaluated TSS and Vitamin C using the same fruit and NIR spectra acquisition mode. The diffuse reflectance spectra were documented and found to be in relation with TSS, vitamin C ( Kusumiyati and Suhandy, 2021 ).

Delwiche et al. ( Delwiche et al., 2008 ) demonstrated the use of near infrared interactance (750-1088nm) to determine mango ripeness, SSC and other sugars. The mango sample was placed in contact with the probe in which the top of mango upwardly points the probe. The R 2 was 0.77; 0.75; 0.67; and 0.70 for SSC, sucrose, glucose, and fructose, respectively. Sugars such as sucrose indicates mango sweetness, fructose and glucose increases during ripening while acidity decreases ( Delwiche et al., 2008 ). Transmission mode in which the light source and sensor are opposite to each other, employs low light intensity to reflect the inner parameters and is performed with no contact on the fruit ( Nicolaï et al., 2007 ). Transmission might be done partially or fully. Though, the difference between partial transmission and diffuse reflectance remains undetermined since both evaluate the radiation that partly enters the sample and diffusely reproduced to the sensor ( Hong and Chia, 2021 ). The fruit with large seed such as mango was reported to be hard to measure in the full transmission due the low signal to noise ratio ( Greensill and Walsh, 2000 ). Subedi at al. ( Subedi and Walsh, 2011 ) detected the TSS and DM of mesocarp tissue of banana and mango by partial transmittance. Mango DM gave R 2 cv =0.75 while banana performance negatively influenced by the thickness of the peel. The TSS results on mango was good in ripe and poor in ripening stage with R 2 cv > 0.75 and R 2 p < 0.75 respectively. The results were consistent with those of Rungpichayapichet et al. ( Rungpichayapichet et al., 2016 ) and were found to be caused by the physiological factors of Mango, banana, and other tropical fruits which can change their starch content as they ripe ( Subedi and Walsh, 2011 ).

Several studies have highlighted the potentials of NIR spectroscopy to monitor the internal and external characteristics of tropical fruits and vegetables, including the following: maturity prediction of avocado and mango ( Olarewaju et al., 2016 ; S. N. Jha et al., 2014 ), total soluble solids and pH of banana ( Ali et al., 2018 ), and variety identification in sweet potatoes ( Su et al., 2019 ). However, the irregular thick skin of pineapple and chemical complexity of large seeded mango was the main difficulty to Guthrie et al. ( Guthrie and Walsh, 1997 ) in the measurement of SSC by NIR reflectance (760-2500nm). The penetration depth of NIR light into a thick-rind avocado 38 mm in diameter and 10 mm in thickness was investigated for the maturity evaluation of avocado using an NIR spectrometer (800–2400 nm) ( Olarewaju et al., 2016 ). The models for estimating oil content, were acceptable, however were not accurate, with an RPD value of less than 1.0 and an R 2 value of 0.58 ( Olarewaju et al., 2016 ). Arendse et al. ( Arendse et al., 2018 ) informed the limited accuracy of NIR for internal quality assessment of fruits and vegetables with thick rinds such as banana, avocado and pineapple due to inadequate penetration depth ( Arendse et al., 2018 ). Therefore, future studies can consider the appropriate selection of NIR optical geometry and wavelength range to improve the prediction accuracy of thick rind tropical crops ( Pratiwi et al., 2023 ).

NIR spectral data inevitably holds overlay information of numerous organic compounds at global wavelengths, making the use of global spectroscopic regions problematic rather than specific wave bands ( Lin and Yibin, 2009 ). Therefore, a combination of algorithms and chemometrics with NIR spectroscopy is now being used to meet this demand, balance data redundancy and complexity, and collect spectral information ( Guan et al., 2019 ; Yang et al., 2021 ). Portable NIR spectroscopy was used to assess mango firmness during ripening (400–1130 nm) ( Mishra et al., 2020 ). Pre-processing was done Savitzky–Golay filter, and iPLSR model was found to provide better predictive modeling, with an R 2 p of 0.75 and an RMSEC of 5.92 Hz 2 g 2/3 compared to the standard PLSR model, which had an R 2 p of 0.67 and an RMSEC of 6.88 Hz 2 g 2/3 . For the firmness in mango fruit, spectral intervals 743-770 nm and 870-905 nm were found to be the accurate predictors ( Mishra et al., 2020 ).

3.1.3 Raman spectroscopy

Raman is another form of vibrational spectroscopy that uses laser beams to interact with materials and operates in the infrared region of the electromagnetic spectrum from 2500 to 25000 nm ( Siesler et al., 2008 ). Though Raman and MIR spectroscopy methods use high levels of energy to detect molecular vibrations, Raman spectroscopy excels at equal vibrations of nonpolar sets, while MIR spectroscopy excels at the unequal vibrations of polar sets ( Campanella et al., 2021 ). Raman spectroscopy consists of a monochromatic laser, wavelength separator, and a detector, as presented in Figure 5 ( Qin et al., 2019 ). When the laser beam illuminates the sample, the photons that constitute the light are absorbed, transmitted, or scattered by the sample in different directions before reaching the detector ( Larkin, 2017 ). Absorption and transmission are linked with the infrared spectra (IR), while scattering is associated with the Raman spectra ( Jones et al., 2019 ). Rostron et al. ( Rostron et al., 2016 ) defined scattered photons in two different ways namely Rayleigh (elastic) scattering and Raman (inelastic) scattering ( Larkin, 2017 ). Rayleigh (elastic) scattering occurs when the photons scattered are equal to those illuminated to the sample; while Raman (inelastic) scattering is due to the transfer of energy between photons and the sample under testing ( Lu, 2017 ).

www.frontiersin.org

Figure 5 Modified diagram of Raman spectroscopy, taking mango as sample ( Lohumi et al., 2015 ).

Raman spectroscopy is suitable for investigating carotenoids in various plants, including carrots ( Lawaetz et al., 2016 ), tomatoes ( Hara et al., 2018 ), plant cells ( Baranska et al., 2011 ), and mango ( Bicanic et al., 2010 ). Furthermore, Raman has been applied as a clean and fast approach to assess cassava starch adulteration ( Cardoso and Jesus Poppi, 2021 ). Two chemometrics models, namely one-class support vector machines (OC-SVMs) and soft independent modelling by class analogy (SIMCA), were used and compared statistically. The OC-SVM results outperform those of SIMCA, with an accuracy of 86.9% ( Cardoso and Jesus Poppi, 2021 ). Surface-enhanced Raman spectroscopy (SERS) was used as a method that applies Raman spectroscopy in conjunction with nanotechnology for the fast analysis of pesticide residues in mango ( Pham et al., 2022 ). SERS results were good indicating that the residues in mango sample were in the suitable range ( Pham et al., 2022 ). Morey et al. ( Morey et al., 2020 ) used spatially offset Raman spectroscopy for potato varieties quality categorization and prediction of tuber cultivation source. This approach is fast since it can be used directly after potato harvesting ( Morey et al., 2020 ).

3.2 Imaging-based approaches

Spectral imaging techniques are among the most effective detection methods because of their potential to obtain both spectral and spatial dimensions of produce simultaneously during measurement ( Liu et al., 2017 ). Regarding spatial dimensions, external attributes such as size, shape, appearance, and color can be evaluated, while with spectral analysis, internal features such as chemical composition can be measured ( Pu et al., 2015 ). A number of imaging techniques use two-dimensional geometry according to the fusion and luminance of color maps ( Lu et al., 2014 ), while others involve the use of three-dimensional sensors such as RGB and hyperspectral images ( Barnea et al., 2016 ) to provide a high fruit and vegetable recognition accuracy ( Nyarko et al., 2018 ).

3.2.1 Hyperspectral imaging techniques

In agriculture and food systems, hyperspectral imaging is a powerful system that joins two aspects of imaging and spectroscopy to attain a three-dimensional (3D) hypercube data form and analyzes a broad spectrum at each pixel instead of assigning only main RGB colors (red, green, and blue) ( Khan et al., 2021 ). The hypercube consists of 3D images characterized by 2D spatial and 1D spectral dimension or wavelength ( Tang et al., 2022 ). Hyperspectral imaging employs more than ten contiguous wavelengths or narrow bands in which each pixel has a full continuous spectrum ( Elmasry et al., 2019 ). To take sample images, the hyperspectral imaging set up can be in the reflectance, transmittance, and interactance which differs in their lighting configuration during crops measurements ( Pan et al., 2017 ). The reflectance geometry is appropriate for assessing the external quality of products, whereas the transmittance performs better in measuring the internal components in relatively translucent membranes ( Li et al., 2018 ). The HSI system comprises of four main components: (1) an imaging unit, (2) illumination (light source), (3) a sample stage, and (4) a computer, as presented in Figure 6 ( Pu et al., 2015 ). The light source is divided into illumination and excitation sources for spectral imaging applications. Broadband lights are commonly used as an illumination source for reflectance and transmittance, whereas narrowband lights are for the excitation source ( Qin et al., 2013 ). The lighting devices produce light that illuminates the sample. The camera transports chemical information as well as light from the light source. The wavelength dispersion device, which can be a grating or a prism, divides the light into different wavelengths and directs the dispersed light to the sensor ( Wu and Sun, 2013 ). Aozora et al. ( Aozora et al., 2022 ) studied the efficiency of hyperspectral imaging (935–1720 nm) in the evaluation of water activity in dehydrated pineapple. The accuracy of the tested model showed good accuracy, with 0.72 and 0.0054 for Rp of and RMSEP respectively ( Aozora et al., 2022 ).

www.frontiersin.org

Figure 6 Modified diagram of Hyperspectral imaging, taking pineapple as sample ( Li et al., 2018 ).

3.2.1.1 Hyperspectral imaging Image generation modes

HSI generates image in three ways: whisk broom (point scanner), push broom (line scanner), and tunable filter (area scanner) ( ElMasry and Sun, 2010 ). The point scan excites only a single spot on the object’s surface and the single pixel is recorded. The spectrum is taken at both positions by moving the sample symmetrically in two spatial dimensions, in order to get the full HSI image ( Qin, 2012 ). However, to obtain good results this technique involves double scanning of the sample and hardware relocation which takes a lot of time to complete the measurement ( Qin, 2012 ). The line scanner excites a line on the object and records the whole line of an image using a 2D dispersing element and 2D detector array. The object is moved line by line and the whole set of spatial–spectral data is gained. This approach has a higher acquisition rate but lower sectioning ability ( Qin, 2010 ). The area scan employs spectral scanning techniques to stimulate the broad area on the surface of the fruit or vegetable, which is held fixed and a scan with full spatial information is achieved consecutively across the entire spectral range. This method is appropriate for applications where sample mobility is not necessary ( Lu et al., 2017 ).

The hyperspectral imaging together with chemometrics models is an appealing option for dealing with large sets of complex, high-dimensional data ( Lorente et al., 2012 ). Chu et al. ( Chu et al., 2022 ) confirmed the efficacy of the HSI reflectance (386-1016 nm) wavelength region in combination with variable selection algorithms and chemometrics for predicting green banana maturity level and characterization of banana quality during maturation ( Chu et al., 2022 ). The line scanning approach was adopted and the calibration models used were partial least squares (PLS) and interval PLS methods ( Chu et al., 2022 ). These models obtained acceptable values R 2 = 0.64 and 0.59 for SSC and TA, respectively, whereas the models for chlorophyll and ΔE* were suitable only for sample screening with R 2 = 0.34 and 0.30, respectively ( Chu et al., 2022 ). Chu reported the inclusion of more samples and different cultivars of banana for model improvement ( Chu et al., 2022 ). Kämper et al. ( Kämper et al., 2020 ) used Vis–NIR–HSI to measure nutrients in avocado fruit. PLSR was used to obtain the ratio of unsaturated to saturated fatty acids in avocado fruit with (R 2 = 0.79, RPD = 2.06) and (R 2 = 0.62, RPD = 1.48) for flesh images and skin images respectively ( Kämper et al., 2020 ). The robust models for flesh images were R 2 = 0.67; 0.61; and 0.53, of oleic-to-linoleic acid ratio, boron (B) and calcium concentration (Ca) respectively, while for skin images was R 2 = 0.60 of boron ( Kämper et al., 2020 ).

4 Advancement in non-destructive spectral measurements for tropical fruit and vegetable quality assessment

The rapid advancement of technology in the agricultural field has resulted in the combination of artificial intelligence with non-destructive spectral measurements for fruits and vegetables quality measurement ( Hasanzadeh et al., 2022 ). Artificial intelligence models such as artificial neural networks (ANNs), genetic algorithms (GAs), fuzzy logic (FL), and adaptive neuro-fuzzy inference system (ANFIS) can assess multiple characteristics simultaneously ( Homayoonfal et al., 2022 ). Salehi reviewed development of models used in the determination of fruits and vegetables quality ( Salehi, 2020 ). ANNs, GAs, FL, and ANFIS detected defects, moisture content, and chilling injury of oranges, cherries, pomegranates, apples, peaches, avocados, button mushrooms, tomatoes, and potatoes ( Salehi, 2020 ). Despite the fact that these models are typically constrained by normality, linearity, homogeneity, and variable independence, the ANFIS model outperforms others and can be successfully used in relevant research ( Salehi, 2020 ).

Machine learning (ML) is a branch of artificial intelligence and an integral part of the development of many sensing technologies that are responsible for information retrieval, signal processing, and data analysis ( Li et al., 2021 ). In recent decades, traditional algorithms such as linear discriminant analysis (LDA), support vector machines (SVMs), K-nearest neighbors (K-NN), naïve Bayes, extreme learning machines (ELMs), decision trees (DTs), and K-means clustering have been deployed ( Fadchar and Dela Cruz, 2020 ). For instance, Rivera et al. ( Rivera et al., 2014 ) used NIR–HSI and machine learning for the early detection of mechanical damage in mango. LDA, K-NN, naïve Bayes, ELMs, and DTs were used for categorization. Bayes failed, however (K-NN, ELM, DT, and LDA Title altered) results was more than 90%. The highest performance, achieved by K-NN, was 97.9% ( Rivera et al., 2014 ).

The evolution of deep learning (DL) as a breakthrough machine learning method has been trending since 2017 due to the manual feature extraction of traditional machine learning methods ( Yang and Xu, 2021 ) and limited performance of chemometrics models, such as spectral variability caused by sample and spectrometer heterogeneity, changing environmental conditions, and infrared spectral data with high noise, which hinder feature extraction using chemometrics models ( Zhang et al., 2021 ). Deep learning is a subset of machine learning that use many neural network layers to extract complex feature representations with numerous levels of abstraction ( Lecun et al., 2015 ). According to Kamilaris et al. ( Kamilaris and Prenafeta-Boldú, 2018 ), convolutional neural network (CNN) and recurrent neural network (RNN) have been implemented for crop-type classification, counting produces, and locating their placement in the image using bounding boxes ( Kamilaris and Prenafeta-Boldú, 2018 ). However, the RNN was found to perform better than the CNN because it considers not only space but also the time which helps to capture the time dimension ( Kamilaris and Prenafeta-Boldú, 2018 ). Deep learning and machine learning technology-based spectral analysis has been used in the classification of three types of fruits (apple, lemon, and mango) by type of damage, type of goods, and whether the sample is raw in market, supermarket, wholesaler, and retailer applications ( Bobde et al., 2021 ).

Garillos-Manliguez et al. ( Garillos-Manliguez and Chiang, 2021 ) estimated six maturity stages of papaya fruit, from the unripe stage to the overripe stage, by feature concatenation of data obtained from visible light and HSI imaging ( Garillos-Manliguez and Chiang, 2021 ). AlexNet, VGG16, VGG19, ResNet50, ResNeXt50, MobileNet, and MobileNetV2 architectures was then modified to apply multimodal data cubes made of RGB and hyperspectral data ( Garillos-Manliguez and Chiang, 2021 ). Regarding classification of the six stages, these multimodal variations can reach F1 scores of up to 0.90 and a 1.45% top-2 error rate. However, due to the small size of the images and the great depth of the CNNs, resulting in highly tightly tuned training variables, overfitting may arise. On the other hand, increasing image size results in insufficient memory faults ( Garillos-Manliguez and Chiang, 2021 ).

Banana fruit was graded by Mesa et al. ( Mesa and Chiang, 2021 ) using multi-input deep learning model with RGB and HSI. These models were able to categorize tier-based bananas by 98.45% and an F1 score of 0.97 with only few samples ( Mesa and Chiang, 2021 ). However, this technique is expensive and time consuming due to the use of two cameras. The next studies instead, should consider the use of more improved camera systems with features that can extract both RGB and HSI simultaneously ( Mesa and Chiang, 2021 ). Another study by Ucat and Cruz explored the use of image processing with a deep learning to grade banana according to their specifications ( Ucat and Dela Cruz, 2019 ). The trained, validated, and test data by CNN model was more than 90% in all four classes of bananas (). The suggested CNN grading system in the tensor flow model can be commercially developed ( Ucat and Dela Cruz, 2019 ).

Portable spectrometers and real-time online detection devices have recently developed for fruits and vegetables quality assessment. Portable devices are handheld, light weight, compact size and they are applied for in-field measurements ( Sohaib et al., 2020 ). The combination of portable NIR device with MSC-PCA+LDA model was used to evaluate pineapple quality. These models were recommended to be developed in mobile phone while PLS regression model provided 85% accuracy ( Amuah et al., 2019 ). Subedi et al. ( Subedi and Walsh, 2020 ) evaluated three hand held portable near infrared spectroscopy (F750, Micro NIR and Scio v1.2) in the detection of dry matter content (DMC) in avocado fruit. The second derivative spectra were recorded for the intact and skin removed avocado fruit for reflectance and interactance optical geometry. The best results of prediction obtained from the F750 instrument using the interactance mode at 720-975 nm with R 2 p of 0.71 and 0.88 for intact and skin removed fruits respectively ( Subedi and Walsh, 2020 ). Real time monitoring device was designed as sensor which can function in all post-harvesting states to control the shelf life of fruits and vegetables such as lettuce. The device found to be the feasible for controlling the behavior of the crop during the post handling chain ( Torres-Sánchez et al., 2020 ). Fruits and vegetables including banana, orange and apple were well sorted according to their external appearance by using real time online system with artificial intelligence ( Tata et al., 2022 ). For quality categorization, machine learning models such as CNN and image processing were performed. This real time system was created in android and can be deployed in market robots where checking of huge number of products is required ( Tata et al., 2022 ).

5 Conclusion and future prospects

Non-destructive spectral measurement has emerged as a prominent solution in the agricultural sector. With the introduction of spectral measurements, there has been rapid progress in analyzing both the internal and external characteristics of tropical fruits and vegetables in a low-cost, accurate, real-time, and fast manner ( Ali et al., 2017 ). Techniques based on FTIR, NIR, and Raman spectroscopy require simple steps to prepare samples prior to analysis ( Abbas et al., 2020 ). In contrast to other imaging techniques such as computer vision, acoustic approaches, electric noses, and fluorescence, HSI uses spectral and spatial data to assess different parameters concurrently ( Lu et al., 2020 ). The spectral measurements presented in this review have shown potential applications for a diverse range of tropical fruits and vegetables for the monitoring and detection of quality attributes such as SSC, TSS, TA, color, size, defects, and texture, which is particularly important for fruit and vegetable processors, food safety agencies, and consumer demands.

Significant advancements in non-destructive spectral measurement technology have occurred recently, including the development of portable spectrometers for real-time and field applications. The combination of spectral measurements and chemometric techniques is a powerful tool for multivariate data analysis, mainly in the improvement of models needed for classification and estimation of quality. A practical case study of Metlenkin et al. ( Metlenkin et al., 2022 ) in the identification and classification of Hass avocado defects before and after storage by HSI and chemometrics. The PLSDA and SIMCA were selected as chemometric methods for multivariate data discrimination and classification. To increase the final model accuracy the calibration was performed by selecting the region of interest. The results revealed the high potential of SIMCA during both modelling and test validation with 100% accuracy. Furthermore, the integration of spectral measurements with deep learning and machine learning technology is rapidly expanding in order to improve quality control accuracy while overcoming the challenges associated with chemometrics such as spectral variability, spectrometer heterogeneity, changing environmental conditions, and infrared spectral data with high noise. The revolution in agriculture and the adaptation of numerous tropical plants to regions outside of their natural range have muddied their classification, and little is known about what properly defines and distinguishes tropical fruits and vegetables from their temperate counterparts. Therefore, there is confusion associated with those studies that reported the classification of tropical fruits and vegetables as an important factor to consider when examining the distinctive quality indicators of these crops. Taking into accounts all of the merits and demerits of non-destructive spectral measurements for the quality monitoring of tropical fruits and vegetables, the use of an adequate number of samples, different cultivars of the fruit and increasing the quality attributes to predict can help to develop robust models that emphasize the variability of tropical fruits and vegetables in terms of size and shape, skin thickness, and growing conditions.

Author contributions

Conceptualization: UA, B-KC. Methodology: UA, TB, MF, MK and IB. Investigation: UA, TB and B-KC. Writing and reviewing: UA, TB, MF and B-KC. Supervision: B-KC. All authors contributed to the article and approved the submitted version.

This work was supported by Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry (IPET) through Smart Agri Products Flow Storage Technology Development Program, funded by Ministry of Agriculture, Food and Rural Affairs (MAFRA) (322051-05).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Abbas, O., Pissard, A., Baeten, V. (2020). “Near-infrared, mid-infrared, and raman spectroscopy,” in Chemical analysis of food (Amstardam: Elsevier), 77–134.

Google Scholar

Abbaszadeh, R., Rajabipour, A., Ahmadi, H., Mahjoob, M. J., Delshad, M. (2013). Prediction of watermelon quality based on vibration spectrum. Postharvest Biol. Technol. 86, 291–293. doi: 10.1016/j.postharvbio.2013.07.013

CrossRef Full Text | Google Scholar

Aboonajmi, M., Jahangiri, M., Hassan-Beygi, a. S. R. (2015). A review on application of acoustic analysis in quality evaluation of agro-food products. J. Food Process. Preservation 39 (6), 3175–3885. doi: 10.1111/jfpp.12444

Aboud, S. A., Altemimi, A. B., Al-hiiphy, A. R.S., Yi-chen, L., Cacciola, F. (2019). A comprehensive review on infrared heating. Molecules 2, 1–20.

Adedeji, A. A., Ekramirad, N., Rady, A., Hamidisepehr, A., Donohue, K. D., Villanueva, R. T., et al(2020)Non-destructive technologies for detecting insect infestation in fruits and vegetables under postharvest conditions: A critical review Foods 9 (7), 1–285doi: 10.3390/foods9070927

Aguilar-Hernández, M. G., Núñez-Gómez, Dámaris, Forner-Giner, MaríaÁngeles, Hernández, F., Pastor-Pérez, JoaquínJ., Legua, P. (2021). Quality parameters of spanish lemons with commercial interest. Foods 10 (1), 1–135. doi: 10.3390/foods10010062

Ahmad, K., Afridi, M., Khan, N. A., Sarwar, A. (2021). Quality deterioration of postharvest fruits and vegetables in developing country Pakistan: A mini overview. Asian J. Agric. Food Sci. 9 (2), 83–90. doi: 10.24203/ajafs.v9i2.6615

Ali, M. M., Hashim, N., Aziz, S. A., Lasekan, O. (2022). Quality prediction of different pineapple (Ananas comosus) varieties during storage using infrared thermal imaging technique. Food Control 138, 1–9. doi: 10.1016/j.foodcont.2022.108988

Ali, M. M., Hashim, N., Bejo, S. K., Jahari, M., Shahabudin, N. A. (2023). Innovative non-destructive technologies for quality monitoring of pineapples: recent advances and applications. Trends Food Sci. Technol. 133, 176–188. doi: 10.1016/j.tifs.2023.02.005

Ali, M. M., Hashim, N., Bejo, S. K., Shamsudin, R. (2017). Rapid and nondestructive techniques for internal and external quality evaluation of watermelons: A review. Scientia Hortic. 225, 689–699. doi: 10.1016/j.scienta.2017.08.012

Ali, M. M., Janius, R. B., Nawi, N. M., Hashim, N. (2018). Prediction of total soluble solids and PH in banana using near infrared spectroscopy. J. Eng. Sci. Technol. 13 (1), 254–645.

Altendorf (2019). Major tropical fruits. Stat. Compendium Rome 01, 18.

Amuah, C. L. Y., Teye, E., Lamptey, F. P., Nyandey, K., Opoku-Ansah, J., Adueming., P. O. W. (2019). Feasibility study of the use of handheld NIR spectrometer for simultaneous authentication and quantification of quality parameters in intact pineapple fruits. J. Spectrosc. , 1–9. doi: 10.1155/2019/5975461

Aozora, D. W., Tantinantrakun, A., Thompson, A. K., Teerachaichayut, S. (2022). Near infrared hyperspectral imaging for predicting water activity of dehydrated pineapples. Res. Militaris 12 (2022), 11–33. doi: 10.3390/foods12142793

Arendse, E., Fawole, O. A., Magwaza, L. S., Opara., U. L. (2018). Non-Destructive prediction of internal and external quality attributes of fruit with thick rind: A review. J. Food Eng. 217, 11–23. doi: 10.1016/j.jfoodeng.2017.08.009

Arendse, E., Nieuwoudt, H., Magwaza, L. S., Nturambirwe, J. F. I., Fawole, O. A., Opara., U. L. (2021). Recent advancements on vibrational spectroscopic techniques for the detection of authenticity and adulteration in horticultural products with a specific focus on oils, juices and powders. Food Bioprocess Technol. 14 (1), 1–225. doi: 10.1007/s11947-020-02505-x

Ayvaz, H., Santos, A. M., Moyseenko, J., Kleinhenz, M., Rodriguez-Saona, L. E. (2015). Application of a portable infrared instrument for simultaneous analysis of sugars, asparagine and glutamine levels in raw potato tubers. Plant Foods Hum. Nutr. 70 (2), 215–205. doi: 10.1007/s11130-015-0484-7

PubMed Abstract | CrossRef Full Text | Google Scholar

Aziz, N. A. A., Jusoh, M. Z., Rosman, R. (2021). “Relationship of total soluble solid (TSS) and capacitance value of papaya fruit using capacitive sensing technique,” in ISCI 2021 - 2021 IEEE symposium on computers and informatics, Kuala Lumpur, Malaysia, 51–57. doi: 10.1109/ISCI51925.2021.9633402

Bahadur, L., Singh, A. D., Singh, S. K. (2020). A review on successful protected cultivation of banana (Musa). Plant Arch. 20, 1570–1573.

Baranska, M., Baranski, R., Grzebelus, E., ROman, M. (2011). In situ detection of a single carotenoid crystal in a plant cell using raman microspectroscopy. Vibrational Spectrosc. 56 (2), 166–695doi: 10.1016/j.vibspec.2011.02.003

Barnea, E., Mairon, R., Ohad, B.-S. (2016). Colour-Agnostic shape-Based 3D fruit detection for crop harvesting robots. Biosyst. Eng. 146, 57–70. doi: 10.1016/j.biosystemseng.2016.01.013

Benichou, M., Ayour, J., Sagar, M., Alahyane, A., Elateri, I., Aitoubahou, A. (2018). Postharvest technologies for shelf life enhancement of temperate fruits. Postharvest Biol. Technol. Temperate Fruits , 77–100. doi: 10.1007/978-3-319-76843-4_4

Bhargava, A., Bansal, A. (2021). Fruits and vegetables quality evaluation using computer vision: A review. J. King Saud Univ. - Comput. Inf. Sci. 33 (3), 243–575. doi: 10.1016/j.jksuci.2018.06.002

Bicanic, D., Dimitrovski, D., Luterotti, S., Twisk, C., Buijnsters, J. G., Dóka, Ottó (2010). Estimating rapidly and precisely the concentration of beta carotene in mango homogenates by measuring the amplitude of optothermal signals, chromaticity indices and the intensities of raman peaks. Food Chem. 121 (3), 832–385. doi: 10.1016/j.foodchem.2009.12.093

Blum, M. M., Harald, J. (2012). Historical perspective and modern applications of attenuated total reflectance - fourier transform infrared spectroscopy (ATR-FTIR). Drug Testing Anal. 4 (3–4), 298–302. doi: 10.1002/dta.374

Bobde, S., Jaiswal, S., Kulkarni, P., Patil, O., Khode, P., Jha, R. (2021). “Fruit quality recognition using deep learning algorithm,” in 2021 International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON), Pune, India, 1–5. doi: 10.1109/SMARTGENCON51891.2021.9645793

Bureau, S., Cozzolino, D., Clark, C. J. (2019). Contributions of fourier-transform mid infrared (FT-MIR) spectroscopy to the study of fruit and vegetables: A review. Postharvest Biol. Technol. 148, 1–14. doi: 10.1016/j.postharvbio.2018.10.003

Caceres-Hernandez, D., Gutierrez, R., Kung, K., Rodriguez, J., Lao, O., Contreras, K., et al. (2023). Recent advances in automatic feature detection and classification of fruits including with a special emphasis on watermelon (Citrillus lanatus): A review. Neurocomputing 526, 62–79. doi: 10.1016/j.neucom.2023.01.005

Campanella, B., Palleschi, V., Legnaioli, S. (2021). Introduction to vibrational spectroscopies. ChemTexts 7 (1), 1–21. doi: 10.1007/s40828-020-00129-4

Canteri, M. H. G., Renard, C. M. G. C., Bourvellec, C. Le, Bureau, S. (2019). ATR-FTIR spectroscopy to determine cell wall composition: application on a large diversity of fruits and vegetables. Carbohydr. Polymers 212, 186–196. doi: 10.1016/j.carbpol.2019.02.021

Cardoso, K. V. G., Jesus Poppi, R. (2021). Cleaner and faster method to detect adulteration in cassava starch using raman spectroscopy and one-class support vector machine. Food Control 125, 107917. doi: 10.1016/j.foodcont.2021.107917

Chakraborty, K., Saha, J., Raychaudhuri, U., Chakraborty, R. (2014). Tropical fruit wines: A mini review. Natural Products 7 (10), 219–285.

Chan, K. L., Kazarian, S. G. (2016). Attenuated total reflection fourier-transform infrared (ATR-FTIR) imaging of tissues and live cells. Chem. Soc. Rev. 45 (7), 1850–1645. doi: 10.1039/C5CS00515A

Chandrasekaran, I., Panigrahi, S. S., Ravikanth, L., Singh, C. B. (2019). Potential of near-Infrared (NIR) spectroscopy and hyperspectral imaging for quality and safety assessment of fruits: an overview. Food Analytical Methods 12 (11), 2438–2585. doi: 10.1007/s12161-019-01609-1

Chu, X., Miao, Pu, Zhang, K., Wei, H., Fu, H., Liu, H., et al. (2022). Green banana maturity classification and quality evaluation using hyperspectral imaging. Agric. (Switzerland) 12 (4), 1–185. doi: 10.3390/agriculture12040530

Clark, C. J. (2016). Fast determination by fourier-transform infrared spectroscopy of sugar-acid composition of citrus juices for determination of industry maturity standards. New Z. J. Crop Hortic. Sci. 44 (1), 69–82. doi: 10.1080/01140671.2015.1131725

Clark, C. J., Cooney, J. M., Hopkins, W. A., Currie, A. (2018). Global mid-infrared prediction models facilitate simultaneous analysis of juice composition from berries of actinidia, ribes, rubus and vaccinium species. Food Analytical Methods 11 (11), 3147–3605. doi: 10.1007/s12161-018-1296-9

Cortés, V., Blasco, J., Aleixos, N., Cubero, S., Talens, P. (2019). Monitoring strategies for quality control of agricultural products using visible and near-infrared spectroscopy: A review. Trends Food Sci. Technol. 85, 138–148. doi: 10.1016/j.tifs.2019.01.015

Cozzolino, D. (2022). An overview of the successful application of vibrational spectroscopy techniques to quantify nutraceuticals in fruits and plants. Foods 11 (3), 1–11. doi: 10.3390/foods11030315

Cubero, S., Aleixos, N., Moltó, E., Gómez-Sanchis, J., Blasco, J. (2011). Advances in machine vision applications for automatic inspection and quality evaluation of fruits and vegetables. Food Bioprocess Technol. 4 (4), 487–5045. doi: 10.1007/s11947-010-0411-8

Cubero, S., Lee, W. S., Aleixos, N., Albert, F., Blasco., J. (2016). Automated systems based on machine vision for inspecting citrus fruits from the field to postharvest—a review. Food Bioprocess Technol. 9 (10), 1623–1395. doi: 10.1007/s11947-016-1767-1

Dasenaki, M. E., Thomaidis, N. S. (2019). Quality and authenticity control of fruit juices-a review . Molecules 24, 1–35. doi: 10.3390/molecules24061014

Delwiche, S. R., Mekwatanakarn, W., Wang, C. Y. (2008). Soluble solids and simple sugars measurement in intact mango using near infrared spectroscopy. HortTechnology 18 (3), 410–165. doi: 10.21273/horttech.18.3.410

Dubey, S. R., Jalal, XXXA. S. (2012). Robust approach for fruit and vegetable classification. Proc. Eng. 38, 3449–3453. doi: 10.1016/j.proeng.2012.06.398

Egidio, V. Di, Sinelli, N., Limbo, S., Torri, L., Franzetti, L., Casiraghi, E. (2009). Evaluation of shelf-life of fresh-cut pineapple using FT-NIR and FT-IR spectroscopy. Postharvest Biol. Technol. 54 (2), 87–925. doi: 10.1016/j.postharvbio.2009.06.006

Elik, A., Yanik, D. K., Istanbullu, Y., Guzelsoy, N. A., Yavu, A., Gogus., F. (2019). Strategies to reduce post-harvest losses for fruits and vegetables. Int. J. Sci. Technological Res. 5 (3), 29–395. doi: 10.7176/jstr/5-3-04

Elmasry, G., Mandour, N., Al-Rejaie, S., Belin, E., Rousseau, D. (2019). Recent applications of multispectral imaging in seed phenotyping and quality monitoring—An overview. Sensors (Switzerland) 19 (5), 1–325. doi: 10.3390/s19051090

ElMasry, G., Sun, D.-W. (2010). “Principles of hyperspectral imaging technology,” in Hyperspectral imaging for food quality analysis and control (San Diego: Elsevier), 3–43.

El-Mesery, H. S., Mao, H., Abomohra, A. El F. (2019). Applications of non-destructive technologies for agricultural and food products quality inspection. Sensors (Switzerland) 19 (4), 1–235. doi: 10.3390/s19040846

Emelike, N. J. T., Akusu, O. M. (2019). Quality attributes of jams and marmalades produced from some selected tropical fruits. J. Food Process Technol. 10 (5), 790. doi: 10.4172/2157-7110.1000790

Escárate, P., Farias, G., Naranjo, P., Zoffoli, J. P. (2022). Estimation of soluble solids for stone fruit varieties based on near-infrared spectra using machine learning techniques. Sensors 22 (16), 1–115. doi: 10.3390/s22166081

Etana, M. B. (2019). A detailed review on common causes of postharvest loss and quality deterioration of fruits and vegetables in Ethiopia. J. Biology Agric. Healthcare 9 (7), 48–52. doi: 10.7176/jbah/9-7-07

Fadchar, N. A., Dela Cruz, J. C. (2020). “A non-destructive approach of young coconut maturity detection using acoustic vibration and neural network,” in Proceedings - 2020 16th IEEE International Colloquium on Signal Processing and Its Applications, CSPA, Langkawi, Malaysia, 136–140. doi: 10.1109/CSPA48992.2020.9068723

FAO (2022). Agricultural production statistics 2000–2021. Agricultural production statistics 2000–2021 (Rome: FAO). doi: 10.4060/cc3751en

Fernandes, F. A. N., Rodrigues, S., Law, C. L., Mujumdar, A. S. (2011). Drying of exotic tropical fruits: A comprehensive review. Food Bioprocess Technol. 4 (2), 163–855. doi: 10.1007/s11947-010-0323-7

Fillion, L., Kilcast, D. (2002). Consumer perception of crispness and crunchiness in fruits and vegetables. Food Qual. Preference 13 (1), 23–295. doi: 10.1016/S0950-3293(01)00053-2

Flores, K., Sanchez, M. T., Perez-Marin, D. C., Lopez, M. D., Guerrero, J. E., Garrido-Varo, A. (2008). Prediction of total soluble solid content in intact and cut melons and watermelons using near infrared spectroscopy. J. Near Infrared Spectrosc. 16 (2), 91–98. doi: 10.1255/jnirs.771

Gabriëls, S. H. E. J., Mishra, P., Mensink, M. G. J., Spoelstra, P., Woltering, E. J. (2020). Non-destructive measurement of internal browning in mangoes using visible and near-infrared spectroscopy supported by artificial neural network analysis. Postharvest Biol. Technol. 166, 111206. doi: 10.1016/j.postharvbio.2020.111206

Ganiron, T. U. (2014). Size properties of mangoes using image analysis. Int. J. Bio-Science Bio-Technology 6 (2), 31–42. doi: 10.14257/ijbsbt.2014.6.2.03

Garillos-Manliguez, C. A., Chiang, J. Y. (2021). Multimodal deep learning and visible-light and hyperspectral imaging for fruit maturity estimation. Sensors (Switzerland) 21 (4), 1–185. doi: 10.3390/s21041288

Glassford, S. E., Byrne, B., Kazarian, S. G. (2013). Recent applications of ATR FTIR spectroscopy and imaging to proteins. Biochim. Biophys. Acta - Proteins Proteomics. 1834 (12), 2849–2858. doi: 10.1016/j.bbapap.2013.07.015

Golmohammadi, A., Afkari-Sayyah, A. H. (2013). Long-term storage effects on the physical properties of the potato. Int. J. Food Properties 16 (1), 104–135. doi: 10.1080/10942912.2010.529978

Greensill, C. V., Walsh, K. B. (2000). Remote acceptance probe and illumination configuration for spectral assessment of internal attributes of intact fruit. Measurement Sci. Technol. 11 (12), 1674–1845. doi: 10.1088/0957-0233/11/12/304

Guan, X., Liu, J., Huang, K., Kuang, J., Liu, D. (2019). Evaluation of moisture content in processed apple chips using NIRS and wavelength selection techniques. Infrared Phys. Technol. 98, 305–310. doi: 10.1016/j.infrared.2019.01.010

Guthrie, J., Walsh, K. (1997). Non-invasive assessment of pineapple and mango fruit quality using near infra-red spectroscopy. Aust. J. Exp. Agric. 37 (2), 253–263. doi: 10.1071/EA96026

Hara, R., Ishigaki, M., Kitahama, Y., Ozaki, Y., Genkawa, T. (2018). Excitation wavelength selection for quantitative analysis of carotenoids in tomatoes using raman spectroscopy. Food Chem. 258, 308–313. doi: 10.1016/j.foodchem.2018.03.089

Hasanzadeh, B., Abbaspour-Gilandeh, Y., Soltani-Nazarloo, A., Hernández-Hernández, M., Gallardo-Bernal, Iván, Hernández-Hernández, JoséL. (2022). Non-destructive detection of fruit quality parameters using hyperspectral imaging, multiple regression analysis and artificial intelligence. Horticulturae 8 (7), 1–16. doi: 10.3390/horticulturae8070598

Homayoonfal, M., Malekjani, N., Baeghbali, V., Ansarifar, E., Hedayati, S., Jafari, S. M. (2022). Optimization of spray drying process parameters for the food bioactive ingredients. Crit. Rev. Food Sci. Nutr. , 1–41. doi: 10.1080/10408398.2022.2156976

Hong, Chia, K. S. (2021). A review on recent near infrared spectroscopic measurement setups and their challenges. Measurement: journal of the international measurement confederation . 171, 108732. doi: 10.1016/j.measurement.2020.108732

Hong, S. J., Yoon, S., Lee, J., Jo, S. M., Jeong, H., Lee, Y., et al. (2022). A comprehensive study for taste and odor characteristics using electronic sensors in broccoli floret with different methods of thermal processing. J. Food Process. Preservation 46 (4), 1–125. doi: 10.1111/jfpp.16435

Indiarto, R. (2020). Post-harvest handling technologies of tropical fruits: A review. Int. J. Emerging Trends Eng. Res. 8 (7), 3951–3957. doi: 10.30534/ijeter/2020/165872020

James, J. B., Ngarmsak, T, Rolle, R. S. (2010). Processing of fresh-cut tropical fruits and vegetables: A technical guide . RAP Publication 2010/16.

Jha, S. N., Gunasekaran, S. (2010). Authentication of sweetness of mango juice using fourier transform infrared-attenuated total reflection spectroscopy. J. Food Eng. 101 (3), 337–342. doi: 10.1016/j.jfoodeng.2010.07.019

Jha, S. N., Narsaiah, K., Jaiswal, P., Bhardwaj, R., Gupta, M., Kumar, R., et al. (2014). Nondestructive prediction of maturity of mango using near infrared spectroscopy. J. Food Eng. 124, 152–157. doi: 10.1016/j.jfoodeng.2013.10.012

Jha, S. N., Matsuoka, T. (2000). Non-Destructive techniques for quality evaluation of intact fruits and vegetables. Food Sci. Technol. Res. 6 (4), 248–515. doi: 10.3136/fstr.6.248

Jodhani, K. A., Nataraj, M. (2021). Synergistic effect of aloe gel (Aloe vera L.) and lemon (Citrus limon L.) peel extract edible coating on shelf life and quality of banana (Musa spp.). J. Food Measurement Characterization 15 (3), 2318–2285. doi: 10.1007/s11694-021-00822-z

Jones, R. R., Hooper, D. C., Zhang, L., Wolverson, D., Valev, V. K. (2019). Raman techniques: fundamentals and frontiers. Nanoscale Res. Lett. 14 (1), 1–34. doi: 10.1186/s11671-019-3039-2

Kamilaris, A., Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Comput. Electron. Agric. 147, 70–90. doi: 10.1016/j.compag.2018.02.016

Kämper, W., Trueman, S. J., Tahmasbian, I., Hosseini Bai, S. (2020). Rapid determination of nutrient concentrations in hass avocado fruit by vis/NIR hyperspectral imaging of flesh or skin. Remote Sens. 12 (20), 1–195. doi: 10.3390/rs12203409

Kasim, N. F. M., Mishra, P., Schouten, R. E., Woltering, E. J., Boer, M. P. (2021). Assessing firmness in mango comparing broadband and miniature spectrophotometers. Infrared Phys. Technol. 115, 103733. doi: 10.1016/j.infrared.2021.103733

Khan, M. H., Saleem, Z., Ahmad, M., Sohaib, A., Ayaz, H., Mazzara, M., et al. (2021). Hyperspectral imaging-based unsupervised adulterated red chili content transformation for classification: identification of red chili adulterants. Neural Computing Appl. 33 (21), 14507–14215. doi: 10.1007/s00521-021-06094-4

Kirezieva, K., Jacxsens, L., Uyttendaele, M., Martinus, A. J. S., Boekel, V., Luning, P. A. (2013). Assessment of food safety management systems in the global fresh produce chain. Food Res. Int. 52 (1), 230–425. doi: 10.1016/j.foodres.2013.03.023

Kusumaningrum, D., Lee, H., Lohumi, S., Mo, C., Kim, M. S., Kwan Cho, B. (2018). Non-destructive technique for determining the viability of soybean (Glycine max) seeds using FT-NIR spectroscopy. J. Sci. Food Agric. 98 (5), 1734–1425doi: 10.1002/jsfa.8646

Kusumiyati, A. A. M., Suhandy, D. (2021). Fast, simultaneous and contactless assessment of intact mango fruit by means of near infrared spectroscopy. AIMS Agric. Food 6 (1), 172–845. doi: 10.3934/AGRFOOD.2021011

Kyriacou, M. C., Rouphael, Y. (2018). Towards a new definition of quality for fresh fruits and vegetables. Scientia Hortic. 234, 463–469. doi: 10.1016/j.scienta.2017.09.046

Lan, W., Renard, C. M. G. C., Jaillais, B., Leca, A., Bureau, S. (2020). Fresh, freeze-dried or cell wall samples: which is the most appropriate to determine chemical, structural and rheological variations during apple processing using ATR-FTIR spectroscopy? Food Chem. 330, 127357. doi: 10.1016/j.foodchem.2020.127357

Larkin, P. (2017). Infrared and raman spectroscopy: principles and spectral interpretation (Amsterdam: Elsevier).

Lawaetz, A. J., Christensen, S. M. U., Clausen, S. K., Jørnsgaard, B., Rasmussen, SørenK., Andersen, S. B., et al. (2016). Fast, cross cultivar determination of total carotenoids in intact carrot tissue by raman spectroscopy and partial least squares calibration. Food Chem. 204, 7–13. doi: 10.1016/j.foodchem.2016.02.107

Lecun, Y., Bengio, Y., Hinton, G. (2015). Deep learning. Nature 521, 436–444. doi: 10.1038/nature14539

Lee, J.-D., Shannon, J.G., Choung, M.-G. (2011). Application of nondestructive measurement to improve soybean quality by near infrared reflectance spectroscopy. Soybean Appl. Technol. 16, 287–304. doi: 10.5772/15842

Leiva-Valenzuela, G. A., Lu, R., Aguilera, JoséM. (2013). Prediction of firmness and soluble solids content of blueberries using hyperspectral reflectance imaging. J. Food Eng. 115 (1), 91–985. doi: 10.1016/j.jfoodeng.2012.10.001

Li, Y. C., Khan, F., Jan, S. R. U., Verma, S., Menon, V. G., Kavita, et al. (2021). A comprehensive survey on machine learning-based big data analytics for ioT-enabled smart healthcare system. Mobile Networks Appl. 26 (1), 234–525. doi: 10.1007/s11036-020-01700-6

Li, J. L., Sun, Da W., Cheng, J. Hu (2016). Recent advances in nondestructive analytical techniques for determining the total soluble solids in fruits: A review. Compr. Rev. Food Sci. Food Saf. 15 (5), 897–9115. doi: 10.1111/1541-4337.12217

Li, R. Li, Wang, M., Liu, Y., Zhang, B., Zhou, J. (2018). Hyperspectral imaging and their applications in the nondestructive quality assessment of fruits and vegetables. Hyperspectral Imaging Agriculture Food Environ. 27–63. doi: 10.1007/intechopen.72250

Lin, H., Yibin, Y. (2009). Theory and application of near infrared spectroscopy in assessment of fruit quality: A review. Sens. Instrumentation Food Qual. Saf. 3 (2), 130–415. doi: 10.1007/s11694-009-9079-z

Liu, Y., Pu, H., Sun, Da W. (2017). Hyperspectral imaging technique for evaluating food quality and safety during various processes: A review of recent applications. Trends Food Sci. Technol. 69, 25–35. doi: 10.1016/j.tifs.2017.08.013

Lohumi, S., Lee, S., Lee, H., Cho, B. K. (2015). A review of vibrational spectroscopic techniques for the detection of food authenticity and adulteration. Trends Food Sci. Technol. 46 (1), 85–985. doi: 10.1016/j.tifs.2015.08.003

López-Maestresalas, A., Keresztes, J. C., Goodarzi, M., Arazuri, S., Jarén, C., Saeys, W. (2016). Non-destructive detection of blackspot in potatoes by vis-NIR and SWIR hyperspectral imaging. Food Control 70, 229–241. doi: 10.1016/j.foodcont.2016.06.001

Lorente, D., Aleixos, N., Gómez-Sanchis, J., Cubero, S., García-Navarrete, O. L., Blasco, J. (2012). Recent advances and applications of hyperspectral imaging for fruit and vegetable quality assessment. Food Bioprocess Technol. 5 (4), 1121–1142. doi: 10.1007/s11947-011-0725-1

Lu (2017). Light scattering technology for food property, quality and safety assessment (Boca Raton, USA: Crc Press), 1–43.

Lu, N. S., Hu, Y., Fu, H. (2014). Detecting citrus fruits with highlight on tree based on fusion of multi-map. Optik 125 (8), 1903–1975. doi: 10.1016/j.ijleo.2013.04.135

Lu, Y., Huang, Y., Lu, R. (2017). Innovative hyperspectral imaging-based techniques for quality evaluation of fruits and vegetables: A review. Appl. Sci. (Switzerland) 7 (2). doi: 10.3390/app7020189

Lu, Y., Saeys, W., Kim, M., Peng, Y., Lu, R. (2020). Hyperspectral imaging technology for quality and safety evaluation of horticultural products: A review and celebration of the past 20-year progress. Postharvest Biol. Technol. 170, 111318. doi: 10.1016/j.postharvbio.2020.111318

Magwaza, L. S., Opara, U. L. (2015). Analytical methods for determination of sugars and sweetness of horticultural products-A review. Scientia Hortic. 184, 179–192. doi: 10.1016/j.scienta.2015.01.001

Magwaza, L. S., Tesfay, S. Z. (2015). A review of destructive and non-destructive methods for determining avocado fruit maturity. Food Bioprocess Technol. 8 (10), 1995–20115. doi: 10.1007/s11947-015-1568-y

Mendy, T. K., Misran, A., Mahmud, T. M. M., Ismail, S. I. (2019). Application of aloe vera coating delays ripening and extend the shelf life of papaya fruit. Scientia Hortic. 246, 769–776. doi: 10.1016/j.scienta.2018.11.054

Mesa, A. R., Chiang, J. Y. (2021). Multi-input deep learning model with rgb and hyperspectral imaging for banana grading. Agric. (Switzerland) 11 (8), 1–18. doi: 10.3390/agriculture11080687

Metlenkin, D. A., Platov, Y. T., Platova, R. A., Zhirkova, E. V., Teneva, O. T. (2022). Non-destructive identification of defects and classification of hass avocado fruits with the use of a hyperspectral image. Agron. Res. 20 (2), 326–340. doi: 10.15159/AR.22.027

Mishra, P., Woltering, E., Harchioui, N. El (2020). Improved Prediction of ‘Kent’ Mango Firmness during Ripening by near-Infrared Spectroscopy Supported by Interval Partial Least Square Regression. Infrared Phys. Technol. 110, 103459. doi: 10.1016/j.infrared.2020.103459

Moreda, G. P., Ortiz-Cañavate, J., García-Ramos, F. J., Ruiz-Altisent, M. (2009). Non-destructive technologies for fruit and vegetable size determination - A review. J. Food Eng. 92 (2), 119–136. doi: 10.1016/j.jfoodeng.2008.11.004

Morey, R., Ermolenkov, A., Payne, W. Z., Scheuring, D. C., Koym, J. W., Isabel Vales, M., et al. (2020). Non-invasive identification of potato varieties and prediction of the origin of tuber cultivation using spatially offset raman spectroscopy. Analytical Bioanalytical Chem. 412 (19), 4585–4945. doi: 10.1007/s00216-020-02706-5

Mukhametzyanov, R. R., Zaretskaya, A. S., Dzhancharova, G. K., Platonovskiy, N. G., Ivantsova, N. N. (2022). “Russia as a subject of the world market for staple tropical fruits,” in Proceedings of the international scientific and practical conference strategy of development of regional ecosystems education-science-industry (ISPCR 2021, Springer Nature , Veliky Novgorod, Russia ) 208 (Ispcr 2021), 594–602. doi: 10.2991/aebmr.k.220208.084

Ndlovu, P. F., Magwaza, L. S., Tesfay, S. Z., Mphahlele, R. R. (2022). Destructive and rapid non-invasive methods used to detect adulteration of dried powdered horticultural products: A review. Food Res. Int. 157, 111198. doi: 10.1016/j.foodres.2022.111198

Neupane, C., Koirala, A., Walsh, K. B. (2022). In-orchard sizing of mango fruit: 1. Comparison of machine vision based methods for on-the-go estimation. Horticulturae 8 (12), 1–17. doi: 10.3390/horticulturae8121223

Nicolaï, B. M., Beullens, K., Bobelyn, E., Peirs, A., Saeys, W., Theron, K. I., et al. (2007). Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review. Postharvest Biol. Technol. 46 (2), 99–1185. doi: 10.1016/j.postharvbio.2007.06.024

Nyarko, E. K., Vidović, I., Radočaj, K., Cupec., R. (2018). A nearest neighbor approach for fruit recognition in RGB-D images based on detection of convex surfaces. Expert Syst. Appl. 114, 454–466. doi: 10.1016/j.eswa.2018.07.048

Okere, E. E., Arendse, E., Tsige, A. A., Perold, W. J., Opara, U. L. (2022). Pomegranate quality evaluation using non-destructive approaches: A review. Agric. (Switzerland) 12 (12), 1–255. doi: 10.3390/agriculture12122034

Olarewaju, O. O., Bertling, I., Magwaza, L. S. (2016). Non-Destructive evaluation of avocado fruit maturity using near infrared spectroscopy and PLS regression models. Scientia Hortic. 199, 229–236. doi: 10.1016/j.scienta.2015.12.047

Ozaki, Y. (2021). Infrared spectroscopy—Mid-infrared, near-infrared, and far-infrared/terahertz spectroscopy. Analytical Sci. 37 (9), 1193–1212. doi: 10.2116/analsci.20R008

Ozaki, Y., Christy, A. A., McClure, W.F. (2006). Near-infrared spectroscopy in food science and technology (Hoboken, USA: John Wiley & Sons).

Pan, L., Sun, Ye, Xiao, H., Gu, X., Hu, P., Wei, Y., et al. (2017). Hyperspectral imaging with different illumination patterns for the hollowness classification of white radish. Postharvest Biol. Technol. 126, 40–49. doi: 10.1016/j.postharvbio.2016.12.006

Pandiselvam, R., Kaavya, R., Monteagudo, S. I., Divya, V., Jain, S., Khanashyam, A. C., et al. (2022). Contemporary developments and emerging trends in the application of spectroscopy techniques: A particular reference to coconut (Cocos nucifera L.). Molecules 27 (10), 1–22. doi: 10.3390/molecules27103250

Pathare, P. B., Rahman, M. S. (2022). Nondestructive quality assessment techniques for fresh fruits and vegetables. In Springer Nature . doi: 10.1007/978-981-19-5422-1

Patrizi, B., De Cumis, M. S., Viciani, S., D’Amato, F. (2019). Dioxin and related compound detection: perspectives for optical monitoring. Int. J. Mol. Sci. 20 (11), 2671. doi: 10.3390/ijms20112671

Pham, U. T., Phan, Q. H. T., Nguyen, L. P., Luu, P. D., Doan, T. D., Trinh, Ha T., et al. (2022). Rapid quantitative determination of multiple pesticide residues in mango fruits by surface-enhanced raman spectroscopy. Processes 10 (3), 1–14. doi: 10.3390/pr10030442

Phey, O., Hashim, N., Maringgal, B. (2020). Quality evaluation of mango using non-destructive approaches: A review. J. Agric. Food Eng. 1 (1), 1–85. doi: 10.37865/jafe.2020.0003

Porat, R., Lichter, A., Terry, L. A., Harker, R., Buzby, J. (2018). Postharvest losses of fruit and vegetables during retail and in consumers’ Homes: quantifications, causes, and means of prevention. Postharvest Biol. Technol. 139, 135–149. doi: 10.1016/j.postharvbio.2017.11.019

Pratiwi, E. Z. D., Pahlawan, M. F.R., Rahmi, D. N., Amanah, H. Z., Masithoh, R. E. (2023). Non-destructive evaluation of soluble solid content in fruits with various skin thicknesses using visible–shortwave near-infrared spectroscopy. Open Agric. 8 (1), 1–125. doi: 10.1515/opag-2022-0183

Pu, Y. Y., Feng, Y. Ze, Sun, Da W. (2015). Recent progress of hyperspectral imaging on quality and safety inspection of fruits and vegetables: A review. Compr. Rev. Food Sci. Food Saf. 14 (2), 176–885. doi: 10.1111/1541-4337.12123

Purwanto, Y. A., Budiastra, I.W., Darmawati, E., Arifiya, N. (2015). Measurement of starch and soluble solid content in papaya using near infrared spectroscopy. Available Online Www.Jocpr.Com J. Chem. Pharm. Res. 7 (6), 112–165.

Qin, J. (2010). “Hyperspectral imaging instruments,” in Hyperspectral imaging for food quality analysis and control (England: Elsevier), 129–172.

Qin (2012). “Hyperspectral and multispectral imaging in the food and beverage industries,” in Computer vision technology in the food and beverage industries (Delhi: Elsevier), 27–63.

Qin, M. S.K., Chaoa, K., Dhakala, S., Chob, B.-K., Lohumib, C. M. S., Pengd, Y., et al. (2019). Advances in raman spectroscopy and imaging techniques for quality and safety inspection of horticultural products. Postharvest Biol. Technol. 149, 101–117. doi: 10.1016/j.postharvbio.2018.11.004

Qin, K. C., Kim, M. S., Lu, R., Burks, T. F. (2013). Hyperspectral and multispectral imaging for evaluating food safety and quality. J. Food Eng. 118 (2), 157–715. doi: 10.1016/j.jfoodeng.2013.04.001

Rahman, A., Cho, B. K. (2016). Assessment of seed quality using non-Destructive measurement techniques: A review. Seed Sci. Res. 26 (4), 285–3055. doi: 10.1017/S0960258516000234

Raj, T. S., Suji, H. A. (2019). Post-harvest quality of fresh produce. Adv. Agri. Sci. 129–143. doi: 10.22271/ed.book21

Rajkumar, P., Wang, N., EImasry, G., Raghavan, G. S. V., Gariepy, Y. (2012). Studies on banana fruit quality and maturity stages using hyperspectral imaging. J. Food Eng. 108 (1), 194–200. doi: 10.1016/j.jfoodeng.2011.05.002

Retamales, J. B. (2011). World Temperate Fruit Production: Characteristics and Challenges | Produção Mundial de Frutas de Clima Temperado : Caracteristicas e Desafios. Rev. Bras. Fruticultura 33 (SPEC. ISSU) 33, 121–130. doi: 10.1590/S0100-29452011000500015

Rivera, N. Ve´lez, Gómez-Sanchis, J., Chanona-Pérez, J., Carrasco, J. José, Millán-Giraldo, Mónica, Lorente, D., et al. (2014). Early detection of mechanical damage in mango using NIR hyperspectral images and machine learning. Biosyst. Eng. 122, 91–98. doi: 10.1016/j.biosystemseng.2014.03.009

Rostron, P., Gaber, S., Gaber, D. (2016). Raman Spectroscopy, Review. Int. J. Eng. Tech. Res. (IJETR) 6, 2454–4698.

Ruiz-Altisent, M., Ruiz-Garcia, L., Moreda, G. P., Lu, R., Hernandez-Sanchez, N., Correa, E. C., et al. (2010). Sensors for product characterization and quality of specialty crops-A review. Comput. Electron. Agric. 74 (2), 176–945. doi: 10.1016/j.compag.2010.07.002

Rungpichayapichet, P., Mahayothee, B., Nagle, M., Khuwijitjaru, P., Müller, J. (2016). Robust NIRS models for non-destructive prediction of postharvest fruit ripeness and quality in mango. Postharvest Biol. Technol. 111, 31–40. doi: 10.1016/j.postharvbio.2015.07.006

Ryu, M., Ng, S. H., Anand, V., Lundgaard, S., Hu, J., Katkus, T., et al. (2021). Attenuated total reflection at THz wavelengths: prospective use of total internal reflection and polariscopy. Appl. Sci. 11 (16), 76325. doi: 10.3390/app11167632

Sahu, D., Potdar, R. M. (2017). Defect identification and maturity detection of mango fruits using image analysis. Am. J. Artif. Intell. 1 (1), 5–145. doi: 10.11648/j.ajai.20170101.12

Salehi, F. (2020). Recent advances in the modeling and predicting quality parameters of fruits and vegetables during postharvest storage: A review. Int. J. Fruit Sci. 20 (3), 506–520. doi: 10.1080/15538362.2019.1653810

Sanchez, P. D. C., Hashim, N., Shamsudin, R., Nor, M. Z. M. (2020). Applications of imaging and spectroscopy techniques for non-destructive quality evaluation of potatoes and sweet potatoes: A review. Trends Food Sci. Technol. 96, 208–221. doi: 10.1016/j.tifs.2019.12.027

Sarkar, T., Chandra, B., Viswavidyalaya, K., Mani, A. (2018) Maturity indices of tropical and sub-tropical fruit crops 38 MATURITY INDICES OF TROPICAL AND SUB-TROPICAL FRUIT CROPS . Available at: https://www.researchgate.net/publication/329266894 .

Sebben, J. Antônio, Espindola, J. da S., Ranzan, L., Moura, N. F. de, Trierweiler, L. F., Trierweiler, J. Otávio (2018). Development of a quantitative approach using raman spectroscopy for carotenoids determination in processed sweet potato. Food Chem. 245, 12–31. doi: 10.1016/j.foodchem.2017.11.086

Shewfelt, R. L. (2014). “Measuring quality and maturity,” in Postharvest handling (New York: Elsevier), 387–410.

Si, W., Xiong, J., Huang, Y., Jiang, X., Hu, D. (2022). Quality assessment of fruits and vegetables based on spatially resolved spectroscopy: A review. Foods 11 (9), 1–215. doi: 10.3390/foods11091198

Siesler, H. W., Kawata, S., Michael Heise, H., Ozaki, Y. (2008). Near-infrared spectroscopy: principles, instruments, applications (Weinheim, German: John Wiley & Sons).

Silva, C. E. de F., Abud, A. K. de S. (2017). Tropical fruit pulps: processing, product standardization and main control parameters for quality assurance. Braz. Arch. Biol. Technol. 60, 1–19. doi: 10.1590/1678-4324-2017160209

Sinanoglou, V. J., Tsiaka, T., Aouant, K., Mouka, E., Ladika, G., Kritsi, E., et al. (2023). Quality assessment of banana ripening stages by combining analytical methods and image analysis. Applied Sciences (Switzerland) 13 (6), 3533.

Sirisomboon, P. (2018). NIR spectroscopy for quality evaluation of fruits and vegetables. Materials Today: Proc. 5 (10), 22481–22486. doi: 10.1016/j.matpr.2018.06.619

Sohaib, A. Z., Qureshi, W. S., Arslan, M., Malik, A. U., Alasmary, W., Alanazi., E. (2020). Towards fruit maturity estimation using NIR spectroscopy. Infrared Phys. Technology. 111, 1–17. doi: 10.1016/j.infrared.2020.103479

Srivichien, S., Terdwongworakul, A., Teerachaichayut, S. (2015). Quantitative prediction of nitrate level in intact pineapple using vis-NIRS. J. Food Eng. 150, 29–34. doi: 10.1016/j.jfoodeng.2014.11.004

Su, W. H., Bakalis, S., Sun, Da W. (2019). Chemometrics in tandem with near infrared (NIR) hyperspectral imaging and fourier transform mid infrared (FT-MIR) microspectroscopy for variety identification and cooking loss determination of sweet potato. Biosyst. Eng. 180, 70–86. doi: 10.1016/j.biosystemseng.2019.01.005

Su, W. H., Sun, Da W. (2018). Fourier transform infrared and raman and hyperspectral imaging techniques for quality determinations of powdery foods: A review. Compr. Rev. Food Sci. Food Saf. 17 (1), 104–225. doi: 10.1111/1541-4337.12314

Su, W.-H., Sun, D.-W. (2019). Rapid determination of starch content of potato and sweet potato by using NIR hyperspectral imaging. Hortscience 54, S38.

Subedi, P. P., Walsh, K. B. (2011). Assessment of sugar and starch in intact banana and mango fruit by SWNIR spectroscopy. Postharvest Biol. Technol. 62 (3), 238–245. doi: 10.1016/j.postharvbio.2011.06.014

Subedi, P. P., Walsh, K. B. (2020). Assessment of avocado fruit dry matter content using portable near infrared spectroscopy: method and instrumentation optimisation. Postharvest Biol. Technol. 161, 1–10. doi: 10.1016/j.postharvbio.2019.111078

Tang, T., Zhang, M., Mujumdar, A. S. (2022). Intelligent detection for fresh-cut fruit and vegetable processing: imaging technology. Compr. Rev. Food Sci. Food Saf. 21 (6), 5171–5985. doi: 10.1111/1541-4337.13039

Tata, J. S., Kalidindi, N. K. V., Katherapaka, H., Julakal, S. K., Banothu., M. (2022). Real-time quality assurance of fruits and vegetables with artificial intelligence. J. Physics: Conf. Ser. 2325 (1), 1–13. doi: 10.1088/1742-6596/2325/1/012055

Torres-Sánchez, R., Martínez-Zafra, MaríaT., Castillejo, N., Guillamón-Frutos, A., Artés-Hernández, F. (2020). Real-time monitoring system for shelf life estimation of fruit and vegetables. Sensors (Switzerland) 20 (7), 1–21. doi: 10.3390/s20071860

Tsuchikawa, S., Ma, Te, Inagaki, T. (2022). Application of near-infrared spectroscopy to agriculture and forestry. Analytical Sci. 38 (4), 635–425. doi: 10.1007/s44211-022-00106-6

Uarrota, V. G., Pedreschi, R. (2022). Mathematical Modelling of Hass Avocado Firmness by Using Destructive and Non-Destructive Devices at Different Maturity Stages and under Two Storage Conditions. Folia Horticulturae. 34 (2), 139–150. doi: 10.2478/fhort-2022-0011

Ucat, R. C., Dela Cruz, J. C. (2019). “Postharvest grading classification of cavendish banana using deep learning and tensorflow,” in 2019 international symposium on multimedia and communication technology, ISMAC 2019 1, 6. doi: 10.1109/ISMAC.2019.8836129

Wang, K., Li, Z., Li, J., Lin, H. (2021). Raman spectroscopic techniques for nondestructive analysis of agri-foods: A state-of-the-art review. Trends Food Sci. Technol. 118, 490–504. doi: 10.1016/j.tifs.2021.10.010

Wang, Z., Walsh, K. B., Verma, B. (2017). On-tree mango fruit size estimation using RGB-D images. Sensors (Switzerland) 17 (12), 1–155. doi: 10.3390/s17122738

Wang, A., Hu, D., Xie, L. (2014). Comparison of detection modes in terms of the necessity of visible region (VIS) and influence of the peel on soluble solids content (SSC) determination of navel orange using VIS-SWNIR spectroscopy. J. Food Eng. 126, 126–132. doi: 10.1016/j.jfoodeng.2013.11.011

Wang, M. Hu, Zhai, G. (2018). Application of deep learning architectures for accurate and rapid detection of internal mechanical damage of blueberry using hyperspectral transmittance data. Sensors (Switzerland) 18 (4), 1–145. doi: 10.3390/s18041126

Wu, Di, Sun, Da W. (2013). Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: A review - part I: fundamentals. Innovative Food Sci. Emerging Technol. 19, 1–14. doi: 10.1016/j.ifset.2013.04.014

Xu, H., Ren, J., Lin, J., Mao, S., Xu, Z., Chen, Z., et al. (2023). The impact of high-Quality data on the assessment results of visible/near-Infrared hyperspectral imaging and development direction in the food fields: A review J. Food Measurement Characterization 17 (3), 2988–3004. doi: 10.1007/s11694-023-01822-x

Yahaya, Mardziah, O. K., Omar, A. F. (2017). Spectroscopy of tropical fruits: sala mango and B10 carambola (Penerbit USM) (Penang, Malaysia: Penerbit USM).

Yahaya, O. K. M., Matjafri, M. Z., Aziz, A. A., Omar, A. F. (2011). Non-destructive quality evaluation of fruit by color based on RGB LEDs system. 2014 2nd Int. Conf. Electronic Design ICED 2014 1001, 230–233. doi: 10.1109/ICED.2014.7015804

Yang, Xu, Y. (2021). Applications of deep-learning approaches in horticultural research: A review. Horticulture Res. 8 (1), 1–31. doi: 10.1038/s41438-021-00560-9

Yang, J., Yin, C., Miao, Xu, Meng, X., Liu, Z., Hu, L. (2021). Rapid discrimination of adulteration in radix astragali combining diffuse reflectance mid-infrared fourier transform spectroscopy with chemometrics. Spectrochimica Acta - Part A: Mol. Biomolecular Spectrosc. 248, 119251. doi: 10.1016/j.saa.2020.119251

Ye, D., Sun, L., Tan, W., Che, W., Yang, M. (2018). Detecting and classifying minor bruised potato based on hyperspectral imaging. Chemometrics Intelligent Lab. Syst. 177, 129–139. doi: 10.1016/j.chemolab.2018.04.002

Yeap, K. Ho, Hirasawa, K. (2019). Introductory chapter: electromagnetism. Electromagnetic Fields Waves 356, 3–10. doi: 10.5772/intechopen.85155

Zainalabidin, F. A., Sagrin, M. S., Azmi, W. N. W., Ghazali, A. S. (2019). Optimum postharvest handling-effect of temperature on quality and shelf life of tropical fruits and vegetables. J. Trop. Resour. Sustain. Sci. (JTRSS) 7 (1), 23–305. doi: 10.47253/jtrss.v7i1.505

Zakaria, L. (2023). Fusarium species associated with diseases of major tropical fruit crops. Horticulturae 9 (3), 322. doi: 10.3390/horticulturae9030322

Zhang, B., Huang, W., Li, J., Zhao, C., Fan, S., Wu, J., et al. (2014). Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: A review. Food Res. Int. 62, 326–343. doi: 10.1016/j.foodres.2014.03.012

Zhang, L., Huang, Y., Sun, F., Chen, Da, Netzel, M., Heather, E., et al. (2021). The effect of maturity and tissue on the ability of mid infrared spectroscopy to predict the geographical origin of banana (Musa cavendish). Int. J. Food Sci. Technol. 56 (6), 2621–2275. doi: 10.1111/ijfs.14960

Zhang, J. Y., Lin, T., Ying, Y. (2021). Food and agro-product quality evaluation based on spectroscopy and deep learning: A review. Trends Food Sci. Technol. 112, 431–441. doi: 10.1016/j.tifs.2021.04.008

Zhu, D., Ren, X., Wei, L., Cao, X., Ge, Y., Liu, He, et al. (2020). Collaborative analysis on difference of apple fruits flavour using electronic nose and electronic tongue. Scientia Hortic. 260, 108879. doi: 10.1016/j.scienta.2019.108879

Keywords: non-destructive measurement, spectral measurements, quality parameters, tropical fruits and vegetables, rapid measurement

Citation: Aline U, Bhattacharya T, Faqeerzada MA, Kim MS, Baek I and Cho B-K (2023) Advancement of non-destructive spectral measurements for the quality of major tropical fruits and vegetables: a review. Front. Plant Sci. 14:1240361. doi: 10.3389/fpls.2023.1240361

Received: 14 June 2023; Accepted: 27 July 2023; Published: 16 August 2023.

Reviewed by:

Copyright © 2023 Aline, Bhattacharya, Faqeerzada, Kim, Baek and Cho. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Byoung-Kwan Cho, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

IMAGES

  1. (PDF) Nutritional Quality of Fruits and Vegetables

    quality analysis of fruits and vegetables

  2. Inspection of the Quality of Vegetables and Fruits in the Laboratory of

    quality analysis of fruits and vegetables

  3. Analysis of the Valuation Activity on Vegetables and Fruits Chains for

    quality analysis of fruits and vegetables

  4. Buy Handbook Of Analysis & Quality Control For Fruit & Vegetable

    quality analysis of fruits and vegetables

  5. Fruit Quality Classification with Technological Interventions

    quality analysis of fruits and vegetables

  6. PPT

    quality analysis of fruits and vegetables

VIDEO

  1. Organics fruits and Vegs

COMMENTS

  1. PDF Safety and Quality of Fresh Fruit and Vegetables

    applied as appropriate and feasible to individual fruit and vegetable operations. USE OF THIS MANUAL The information presented includes: Principles - science-based information regarding elements of produce safety and quality. Topics included are: ¾ Introduction to food safety and quality ¾ Food safety of fresh fruits and vegetables

  2. Nondestructive Methods for the Quality Assessment of Fruits and

    Article 08 August 2019 Quality of Vegetable Products: Assessment of Physical, Chemical, and Microbiological Changes in Vegetable Products by Nondestructive Methods Chapter © 2018 Nondestructive Techniques for Fresh Produce Quality Analysis: An Overview Chapter © 2022 Introduction Fruits and vegetables are important food sources in human life.

  3. Safety, Quality, and Processing of Fruits and Vegetables

    This Special Issue "Safety, Quality, and Processing of Fruits and Vegetables" gives an overview of the application of emerging, unconventional technologies to obtain high-quality fruit juice, semi-dried and dried products, waste valorisation, and process control.

  4. Emerging nondestructive technologies for quality assessment of fruits

    Establishing a combination of nondestructive tools throughout the processing unit that are competent in monitoring critical physical and microstructure study of fruits, vegetables, cereals, and other foodstuffs was established as the ideal solution. 7.3. Assessment of food (fruits, vegetables, cereals) quality by nondestructive techniques

  5. Fruits and vegetables quality evaluation using computer vision: A

    Quality inspection of fruits and vegetables using image processing technique involves five steps, as depicted in , namely, image acquisition, pre-processing, image segmentation, feature extraction and classification.

  6. Nondestructive Quality Assessment Techniques for Fresh Fruits and

    As a result, readers gain a better understanding of how to use nondestructive methods and technologies to detect the quality of fresh fruits and vegetables. With a wide range of interesting topics, the book will benefit readers including postharvest & food scientists/technologists, industry personnel and researchers involved in fresh produce ...

  7. Handbook of Analysis and Quality Control for Fruit and Vegetable

    His research areas include strained baby foods, fruit juice concentrates and powders, study of the mechanism of discolouration in canned and dried fruits and vegetable products, lowmethoxyl...

  8. A Review on Quality Determination for Fruits and Vegetables

    Fruits and vegetable quality can be determined using four main attributes such as flavor (aroma, taste), texture, color, appearance and nutritional value [ 1 ]. Many features like soluble solids, smell, acidity and firmness are helpful to determine the quality of fruit and vegetables.

  9. Methods for Determining Fruit Quality in Horticultural Crops

    The quality of fruit is a major aspect from the point of view of consumers. Measures of fruit quality include both the internal and external properties. The internal quality mainly is determined by aroma, flavour, taste, texture, nutritional quality (e.g. soluble sugar content, starch, organic acids, soluble solids content, carotenoids, total ...

  10. Quality measurement of fruits and vegetables

    Fruits and vegetables are notoriously variable, and the quality of individual pieces may differ greatly from the average.

  11. Foods

    Quality Assessment of Fruits and Vegetables Based on Spatially Resolved Spectroscopy: A Review by Wan Si 1, Jie Xiong 1, Yuping Huang 1,*, Xuesong Jiang 1 and Dong Hu 2 1 College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China 2

  12. A Review on Quality Determination for Fruits and Vegetables

    The overall accuracy achieved in quality analysis and defect detection is 87% (apple: 83%; orange: 93%; and tomatoes: 83%) of defective fruits (apple and orange) and vegetables (tomatoes). View ...

  13. Review of quality assessment of fruit and vegetables using NIR

    A survey of the literature on the topic of fruit quality detection, based on international publications, is shown in table 10.2; a survey based on national publications is shown in table 10.3. These surveys demonstrate that NIR spectroscopy accurately measures the quality attributes of fruit and vegetables, compared to traditional methods.

  14. Advancement of non-destructive spectral measurements for the quality of

    The intention of quality inspection is to detect any internal or external characteristics that can aid in identifying both standard quality parameters and defects or non-conformities that can affect the safety of fruits and vegetables or their usability in particular functions such as diets, trade, and industrial chains (Kirezieva et al., 2013).

  15. Quality Control in Fruit and Vegetable Processing

    Quality Control in Fruit and Vegetable Processing: Methods and Strategies illustrates the applications of various nonthermal technologies for improving the quality and safety of fruits and vegetables, such as microwave, ultrasound, gamma irradiation, pulsed light, and hurdle technology.

  16. Textural Quality Assessment for Fresh Fruits and Vegetables

    18 Citations Part of the Advances in Experimental Medicine and Biology book series (AEMB,volume 542) Abstract Texture is critical to the acceptability of fruits and vegetables, both fresh and cooked. This article focuses primarily on texture measurement of fresh (raw, uncooked) fruits and vegetables.

  17. PDF Manual of Methods of Analysis of Foods

    Standards for processed fruits and vegetables are laid down in section 2.3 of Food Safety and Standards (Food Product Standards and Food Additives) Regulations, 2011 and include thermally processed fruits and vegetables, fruit and vegetable juices, soups (canned/bottled/flexibly packed), soup powders, dehydrated vegetables, fr...

  18. (PDF) Non-destructive quality evaluation by sensing maturity and

    Ripening is the process by which fruit attained desirable flavour, quality, colour and other textural properties. The characterization of fruit and vegetables has been an important issue in the ...

  19. Handbook of analysis and quality control for fruit and vegetable

    Access-restricted-item true Addeddate 2023-05-23 16:14:31 Autocrop_version ..14_books-20220331-.2 Boxid IA40944212 Camera USB PTP Class Camera Collection_set

  20. Assessment of Internal and External Quality of Fruits and Vegetables

    First Online: 20 January 2016 1846 Accesses 4 Citations Part of the Food Engineering Series book series (FSES) Abstract In this chapter, advances in the most important imaging techniques that can be applied to fruit and vegetable inspection are addressed.

  21. [PDF] Quality Analysis of Fruits and Vegetables using Machine Learning

    Quality Analysis of Fruits and Vegetables using Machine Learning Techniques. Richa P. Shah, P. Gujarathi, +2 authors. A. Bandal. Published 2018. Computer Science, Agricultural and Food Sciences. TLDR. This work makes an attempt to use image processing techniques to extract colour, size and other attributes of the image forming training dataset ...

  22. Quality analysis of fruits, vegetables and fish available in local

    The quality of fish is more inferior to fruits and vegetables. This research strengthens the idea that overall quality of those foods of different supply chain was destitute and varieties...

  23. Prescriptions for fruits and vegetables can improve the health of ...

    Participants in these programs ate more fruits and vegetables. They were also one-third less likely to experience food insecurity - not having enough food to meet basic needs and lead a healthy ...

  24. Disease Detection and Quality Analysis of Fruits and Vegetables

    The diseases present in the fruits and vegetables decreases the quality and the productivity. There is more involvement of scientists, mall owners and labor to identify the defected part in vegetables and fruits. This whole process consumes a lot of time which in turn damage the rest of production and result cataclysmic for farmers.

  25. Advancement of non-destructive spectral measurements for the quality of

    2 Quality inspection of Tropical fruits and vegetables. Quality inspection is the process of evaluating specific parameters of fruits and vegetables to ensure required quality standards (Phey et al., 2020).The intention of quality inspection is to detect any internal or external characteristics that can aid in identifying both standard quality parameters and defects or non-conformities that ...