review article_ non-destructive quality evaluation of vegetables

8/18/2019 Review Article_ Non-Destructive Quality Evaluation of Vegetables

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Review Article: Non-Destructive Quality Evaluation of Vegetables

CONFERENCE PAPER · JULY 2014

DOI: 10.13140/2.1.3449.1204

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Abdelgawad Saad

Agricultural Engineering Research Institute

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National Conference on Pre-/Post-Harvest Losses & Value Addition in Vegetables. July 12-13, 2014.

Indian Institute of Vegetable Research, Varanasi, India.

Review Article: Non-Destructive Quality Evaluation of Vegetables

AbdelGawad Saad1, Pranita Jaiswal2, S.N. Jha2*1 Agricultural Research Center (ARC), Agricultural Engineering Research Institute (AEnRI),

Dokki, Giza, Egypt.2 Agriculture structure and environmental control division, Central Institute of Post-Harvest

Engineering anTechnology (CIPHET), Ludhiana-141004, Punjab, India.* Corresponding author: [email protected] (S.N. Jha)

I. INTRODUCTION

II. SPECTOSCOPY TECHNIQUES

A. Visual Spectroscopy

B. Near infrared Spectroscopy

C. Microwave Dielectric Spectroscopy

D. X-ray and Computerized Tomography (CT)

III. SOUND WAVES TECHNIQUES

A. Acoustics

B. Ultrasound

IV. IMAGING ANALYSIS TECHNIQUES

A. Hyperspectral Imaging

B. Machine Vision

C. Magnetic Resonance (MR) and Magnetic Resonance Imaging (MRI)

VI. CONCLUSIONS

VII. REFERENCES


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National Conference on Pre-/Post-Harvest Losses & Value Addition in Vegetables. July 1-1!" #1$.

%ndian %nstitute of Vegetable esearc'" Varanasi" %ndia .

2

I. INTRODUCTION

Vegetables are consider as the one of the most valuable group of food which play a vital role

in human health by preventing diseases and repair body via maintaining its alkaline reserve

(FSSAI 2012). Different vegetables are used in different forms such as roots, stems, leaves,fruits and seeds and contribute to a healthy diet (USDA 2012). Therefore, the maintenance of

quality of vegetables is the main concern. However, both quantitative (such as decrease in

weight or volume) and qualitative (such as reduced nutrient value and unwanted changes in

taste, color, texture, or cosmetic features of food) losses in vegetables occur between harvest

and consumption (Buzby and Hyman 2012). Majority of them occur during harvest, and

postharvest presses (Kader 2005; Hodges et al. 2011). The qualitative losses, of fresh

produce, are although difficult to assess than quantitative losses (Kader 2005; Kitinoja et al.

2011), yet they significantly affect the overall acceptability of the produce. Quality standards,

consumer preferences and purchasing power vary greatly across countries and cultures and

these differences influence marketability and the magnitude of post-harvest losses. In recent

years, markets of developed countries have emerged as a major hub of agricultural export for

many developing countries. This access to international market has posed many challenges to

meet their stringent food safety standards. So the need of the hour is therefore to develop an

effective quality evaluation system for maintaining an acceptable quality level to the end

users.

Keeping these things in mind, the objective of postharvest research round the globe is

focussed at 1) Understanding the biological and environmental factors responsible for

postharvest losses; 2) Development of suitable postharvest technology to reduce losses and

preserve quality and safety of commodities; 3) Development of rapid, cost effective, user

friendly quality evaluation technique. Quality of vegetables is determined by various

physicochemical parameters such as colour, shape, size, gloss, firmness, total soluble solids

(TSS), pH, dry matter (DM), and acidity which involve laborious laboratory techniques

which are destructive in nature, need trained staff and render the commodity unusable. These

problems can be overcome by applying the approach of non-destructive techniques.

Non-destructive techniques can be used for internal quality assessment and sorting of

vegetables as well as for measurements of critical selection feature in plant breeding

programs. Different Non-destructive techniques frequently used for the assessment of

vegetables quality are fast, user friendly cheaper and accurate (Alander et al. 2013; Saldaña et


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Vegetables

Targets: Chemical components,Physical properties, quality, taste

Energy change

Input energyNear infrared (NIR)X-rayUltrasonicMicrowaveAcousticMagnetic resonanceImaging (MR/MRI)

Output energy

uantification

Develo ment of calibration models between the tar ets and ener

al. 2013). It is possible to screen large numbers of diverse samples by applying these

techniques. The scientific principle of non-destructive technique is to estimate vegetables

quality via measuring change in energy, applied on the target (Figure 1).

Figure 1. Scientific principle of non-destructive quality estimation for vegetables.

Various non-destructive techniques such as optics, near infrared (NIR), ultrasonic, X-ray,microwave, acoustic, and magnetic resonance/magnetic resonance imaging (MR/MRI) have

been applied for quality determination of horticulture produce (Wang et al. 2009; Jha et al.

2010; Lorente et al. 2011).

II. SPECTOSCOPY TECHNIQUES

A. VISUAL SPECTROSCOPY

Chemical components of any food material absorb light energy at specific wavelengths;

therefore some compositional information can be determined from spectra measured by

spectrophotometers. In the visible wavelength range, pigments such as chlorophylls,

carotenoids, anthocyanins and other coloured compounds are the major light absorbing

component of vegetables (Ignat 2012). The reflectance properties of any object in the visible

region (380–750 nm) are perceived by human eyes as colour, which provide information

about the pigment content of the sample (Berns 2000). Colours appear, when light is


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absorbed and some part of it is reflected by the target material. If all light is reflected, the

object will appear white, and if all is absorbed, it will appear black. The wavelengths

influence the perceived color which therefore is dependent on both the light source and the

absorption by the object (Løkke 2012).

Vegetables skin colour has been considered as indicator for maturity in some

horticultural products such as tomato (Edan et al. 1997). Absorption spectrum of several plant

pigments are shown in Figure 2. Basically, colour is regarded as one of the appearing

property attributed to the spectral distribution of light which is directly related to the object to

which the colour is ascribed as well as the eye of the observer. On the other hand in absence

of illumination, colour does not appear. Hence, a number of factors influenced the radiation

and subsequently affect the exact colour that one perceives (Jha 2010).

Figure 2. Absorption spectra of chlorophyll and carotenoids. From www.cfb.unh.eduJha and Matsuoka (2002) used spectral radiometer to determine the freshness of eggplant

on the basis of surface gloss and weight. They established a relationship between gloss index

and weight during storage and developed quick and reliable instrumental method for non-

destructive estimation of freshness of eggplant. Jha et al. (2002) further developed a freshness

index of eggplant using this technique.

B. NEAR INFRARED SPECTOSCOPY

Near‐infrared spectroscopy is one of the most widely studied quality assessment tools for the

last twenty years. It is a rapid, powerful, reliable and non-destructive technique for the

measuring qualitative and quantitative properties of biological materials (Jha and Matsuoka

2004; Teye et al. 2013). This technique is now increasingly used for non-destructive

measurement of the quality of fruits and vegetables such as soluble solids (Brix), acidity,

titratable acidity, water content, dry matter, firmness, and so on and for rapid assessment of

fiber, protein, fat, ash content and so on (Jha and Matsuoka 2004; Bureau et al. 2013).

https://www.researchgate.net/publication/229884499_Color_and_Firmness_Classification_of_Fresh_Market_Tomatoes?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/229889817_Non-destructive_determination_of_acid-brix_ratio_of_tomato_juice_using_near_infrared_spectroscopy?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/229884499_Color_and_Firmness_Classification_of_Fresh_Market_Tomatoes?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/229889817_Non-destructive_determination_of_acid-brix_ratio_of_tomato_juice_using_near_infrared_spectroscopy?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==


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Near-Infrared spectroscopy involves use of light in the wavelengths range of 780-2500

nm, and the light penetration depth, depends on the wavelength and the sample

characteristics. It is up to 4 mm in the 700-900 nm range (Lammertyn et al. 2000; Nicolai et

al. 2007). In the NIR region, the absorption is due to overtones and the combination tones of

the fundamental infrared (IR) vibration bands of bonds where the electric dipole moment

chanes; anhar!onic bonds ("h#esen et al. 2003). "he liht e!itted fro! the ob$ect

(sample), reveals information about chemical properties and surface structure. The light

scattered from fresh produce reveals the microstructure of the tissue and the light absorption

is associated with the presence of chemical components in the sample under study. Therefore

both phenomenons are useful in quality assessment (Nicolai et al. 2007). The fresh

agriculture product are generally found to be high in moisture content, hence the NIR

spectrum of fresh agriculture product is vastly controlled by water content since water has ahigh absorption in NIR region of light (Cen and He 2007). The spectral pattern information is

used to predict the chemical compositions of the sample by extracting the relevant

information from many overlapping peaks. So, the pivotal step is to extract the useful

information from original spectral data. Additionally, automation of NIR measurements can

be done after proper calibration. Multivariate calibration is required to develop the prediction

models, for quantitative analysis of sample constituents. Partial least squares (PLS), principal

components regression (PCR), and artificial neural networks (ANN) are the most usedmultivariate calibration techniques for the NIR spectroscopy (He et al. 2005). Vis-NIR

spectroscopy has been successfully used to develop model for prediction of sensory quality of

chicory, soluble solids content and firmness of bell pepper (Francois et al. 2008; Penchaiya et

al. 2009). It has also been used in prediction of chlorophyll content in leafy green vegetables

(Xue and Yang 2009). Slaughter et al. 1996) used NIR spectroscopy to study the soluble solid

content of more than thirty varieties of fresh tomatoes. Shao et al. (2007) measured the

quality characteristics (soluble solids content, pH and firmness) of tomato “Heatwave”, byNIR spectroscopy and found satisfying results.

NIR spectroscopy has been used to measure the nitrate concentrations in vegetables

(%hao and &e 200'; anda et al. 2010; Itoh et al. 2011). Shao and He (2008) estimated the

strawberry acidity by NIR reflectance spectroscopy, the absorbance data compressed by using

wavelength transformation and two models were established to predict strawberry acidity.

Matsumoto et al. (2009) developed models to measuring nitrate concentration in the whole

https://www.researchgate.net/publication/222787701_Theory_and_application_of_near_infrared_reflectance_spectroscopy_in_determination_of_food_quality?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/220887615_Study_of_Application_Model_on_BP_Neural_Network_Optimized_by_Fuzzy_Clustering?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/243334184_Deriving_leaf_chlorophyll_content_of_green-leafy_vegetables_from_hyperspectral_reflectance?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/227647961_Nondestructive_Determination_of_Soluble_Solids_in_Tomatoes_using_Near_Infrared_Spectroscopy?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/222666786_Visiblenear_infrared_spectrometric_technique_for_nondestructive_assessment_of_tomato_'Heatwave'_Lycopersicum_esculentum_quality_characteristics?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/233338204_Nondestructive_Measurement_of_Acidity_of_Strawberry_Using_VisNIR_Spectroscopy?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/233338204_Nondestructive_Measurement_of_Acidity_of_Strawberry_Using_VisNIR_Spectroscopy?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/233338204_Nondestructive_Measurement_of_Acidity_of_Strawberry_Using_VisNIR_Spectroscopy?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/233338204_Nondestructive_Measurement_of_Acidity_of_Strawberry_Using_VisNIR_Spectroscopy?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/233338204_Nondestructive_Measurement_of_Acidity_of_Strawberry_Using_VisNIR_Spectroscopy?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/227647961_Nondestructive_Determination_of_Soluble_Solids_in_Tomatoes_using_Near_Infrared_Spectroscopy?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/243334184_Deriving_leaf_chlorophyll_content_of_green-leafy_vegetables_from_hyperspectral_reflectance?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/222666786_Visiblenear_infrared_spectrometric_technique_for_nondestructive_assessment_of_tomato_'Heatwave'_Lycopersicum_esculentum_quality_characteristics?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/220887615_Study_of_Application_Model_on_BP_Neural_Network_Optimized_by_Fuzzy_Clustering?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/222787701_Theory_and_application_of_near_infrared_reflectance_spectroscopy_in_determination_of_food_quality?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==


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body of a lettuce by using absorption spectra in the range of 700–960 nm. Itoh et al. (2011)

developed a method for non-destructive measurement of nitrate concentration in Spinach and

komatsuna leaves by near-infrared (NIR) spectroscopy. They measured absorption spectra of

small portion of the leaves thereafter the nitrate concentrations of the same portion were

measured by a liquid chromatography analyzer. Finally PCR or PLS methods with a

wavelength selection algorithm were developed to estimate the nitrate concentration.

C. MICROWAVE DIELECTRIC SPECTROSCOPY

Microwave electromagnetic radiation spectrum stretch from a frequency range 108 Hz to

1011Hz (Chieh 2012). Microwave dielectric spectroscopy is an emerging technique used in

assessment of the internal quality based on dielectric properties of food products (Bohigas et

al. 2008). Dielectric properties of all materials are dependent on their molecular structure.

Specifically, it depends on the distribution of electric charges, which are either constantly

embedded within the molecules or temporarily covers its surfaces. It is also known that the

molecular structure of objects determine their physical and chemical properties. Therefore,

the dielectric properties of various molecules constituting a given material will uniquely

identify it. It can successfully diversify physical and chemical properties of a tested material

(Figure 3). The crucial point of the application of the dielectric spectroscopy measurement

techniques in agrophysics is the utilization of their advantages for rapid and non-destructive

assessment of the quality of the agricultural objects. It may be done by searching for

dependencies between the dielectric properties and other physical and chemical properties of

tested materials of agricultural origin (Skierucha et al. 2012).

Figure 3. Quality parameters of heterogeneous materials of agricultural origin described by

physical and chemical parameters as well as the dielectric ones (Skierucha et al. 2012).

Molecular structure of heterogeneous materials

Dielectric properties (phaseshift, signal attenuation,

relaxation time, temperatureinfluence, ect.) in the functionof frequency

Physical and chemical properties(moisture, firmness, colour, pH,

acidity, salinity, content of starch,sugar, trace elements, aromaticcompounds, etc.)

Correlationsought

Quality parameters of heterogeneous materials

https://www.researchgate.net/publication/258658319_Dielectric_spectroscopy_in_agrophysics?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/258658319_Dielectric_spectroscopy_in_agrophysics?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/258658319_Dielectric_spectroscopy_in_agrophysics?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/258658319_Dielectric_spectroscopy_in_agrophysics?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==


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The dielectric properties of food materials in the microwave region can be determined by

different microwave measuring sensors (Kraszewski 1996). The specific method used

depends on the frequency range and the type of target material. The water content and soluble

solid content of watermelon were evaluated by measuring permittivity of watermelon (Nelson

et al. 2007). The open ended probe was used to measure the complex permittivity of

watermelon. Soluble solid content and moisture content of watermelon were used as a quality

factor for the correlation with the dielectric properties. A high correlation was obtained

between the dielectric constant and solid soluble content (Nelson et al. 2007).

Nelson and Trabelsi (2008) used the permittivity measurements of honeydew melons and

watermelons, with an open-ended coaxial-line probe and impedance analyzer at frequencies

of 10 MHz to 1.8 GHz to provide information about their maturity. Total soluble solid of

melons was used as measure of maturity and was correlated with permittivity. Dielectric

constant and loss factor correlations with total soluble solid were low, but a high correlation

was recorded between the total soluble solid and permittivity from a complex-plane plot of

dielectric constant and loss factor, each divided by total soluble solid.

Dielectric spectroscopy can be considered an important non-destructive tool for

controlling the freezing process of potato at frequency range of 500 MHz to 20 GHz (Cuibusa

et al. 2013). The dielectric properties of tomatoes were measured over a frequency range of

300–3000 MHz at temperatures between 22 and 120 °C (Penga et al. 2013). The dielectric

properties has been used to measure the moisture content and tissue density of agricultural

materials by predicting heating rates which in turn describe the behaviour of products when

exposed to high-frequency or microwave electric fields (Venkatesh and Raghavan 2004).

Nelson (2003); (Nelson et al. 2006) carried out permittivity measurements of cut

vegetables (cantaloupe, carrot, cucumber, and potato) over a frequency range of 10 MHz to

1.8 GHz and at various temperatures ranging from 5ºC to 95ºC. The dielectric loss factor was

considerably decreased with frequency whereas slight decrease in dielectric constant wasobserved with frequency. Similarly, the dielectric loss factor was generally found to be

increased with temperature. In fruits and vegetables, the moisture content is high then the

dielectric constant is generally high at temperature ranges from 5ºC to 95 ºC. McKeown et al.

(2012) observed the highest magnitude of permittivity values (dielectric constant and

dielectric loss) at low frequencies in carrot. The dielectric constant value of carrot,

cantaloupe, potato, and finally cucumber was found to be in the decreasing order. Although

https://www.researchgate.net/publication/43274841_Dielectric_Spectroscopy_Measurements_on_Fruit_Meat_and_Grain?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/228772325_An_Overview_of_Microwave_Processing_and_Dielectric_Properties_of_Agri-Food_Materials?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/270616827_Frequency-and_temperature-dependent_permittivities_of_fresh_fruits_and_vegetables_from_001_to_18_GHz_Trans_ASAE_46567-574?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/43255040_Dielectric_Spectroscopy_of_Honeydew_Melons_from_10_MHz_to_18_GHz_for_Quality_Sensing?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/43255040_Dielectric_Spectroscopy_of_Honeydew_Melons_from_10_MHz_to_18_GHz_for_Quality_Sensing?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/270616827_Frequency-and_temperature-dependent_permittivities_of_fresh_fruits_and_vegetables_from_001_to_18_GHz_Trans_ASAE_46567-574?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/43274841_Dielectric_Spectroscopy_Measurements_on_Fruit_Meat_and_Grain?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/228772325_An_Overview_of_Microwave_Processing_and_Dielectric_Properties_of_Agri-Food_Materials?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==


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moisture content did not correlate with the dielectric properties, other factors such as density,

tissue structure, nature of water binding to constituents of vegetables might have affected the

dielectric properties

D. X-RAY AND COMPUTERIZED TOMOGRAPHY (CT)

X-ray imaging is an established technique for quickly detecting the strongly attenuating

materials. It has been applied to a number of inspection applications within the agricultural

and food industries (Jha and Matsuoka 2000; Donis-González 2013).

Recently, techniques based on two-dimensional (2D) X-ray, and computed tomographic

(CT) imaging have been explored, and used for internal quality determination of agricultural

and food products non-destructiel# (*bbott 1+++; &aff 2008). Despite extensive research

effort, real-time inspection systems for detection of internal quality of fresh produce are not

commercially available, because of limitations in useful information when using high-speed

systems (Butz et al. 2005). However, with the improvement in high-performance computers,

new detector technologies, high-performance x-ray tubes, accessibility, real-time imaging,

cost diminution, and significant reducing in image acquisition time and in-line CT sorting

systems are gaining tremendous attraction (Pratx and Xing 2011).

X-ray is short wave radiation (0.01 – 10 nm) with high energy (1.92 × 10 -17 – 1.92 ×

10-14J) that can easily penetrate matter. X-rays are generated by bombarding electrons on a

metallic anode (X-ray tube) (Bushberg et al. 2002). Traditional CT is an imaging modality

where an x-ray tube is rotated around an object or objects and the attenuation is recorded on a

detector. Other equipment may contain a rotating stage in front of a fixed x-ray tube and

detector (Donis-González et al. 2012). Quenon and De Baerdemaeker (2000) developed X-

ray method to measure the length of the floral stalk in Belgian endive Cichorium intybus L

non-destructively. Detection algorithm was developed based on the minimal transmitted

intensities along the length. The method is very accurate with an absolute precision of 4.9mm

and allows the study of the influence of storage conditions and time on the internal quality of

Belgian endive. They concluded the X-ray transmission is suitable for a non destructive

measurement of the length of the floral stalk in Belgian endive.

III. SOUND WAVES TECHNIQUES

Acoustic sound waves (in the range of human hearing i.e 20 Hz to 20 KHz) and ultrasonic

waves (which are above the range of human hearing i.e. 20 KHz to 1 MHz) are used to

https://www.researchgate.net/publication/43266035_Real-time_correction_of_distortion_in_x-ray_images_of_cylindrical_or_spherical_objects_and_its_application_to_agricultural_commodities?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/43266035_Real-time_correction_of_distortion_in_x-ray_images_of_cylindrical_or_spherical_objects_and_its_application_to_agricultural_commodities?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/227680809_Recent_Developments_in_Noninvasive_Techniques_for_Fresh_Fruit_and_Vegetable_Internal_Quality_Analysis?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/245026412_The_essential_physics_of_medical_imaging_third_edition?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/26552166_Non-destructive_method_for_internal_quality_determination_of_belgian_endive_Cichorium_Intybus_L?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/43266035_Real-time_correction_of_distortion_in_x-ray_images_of_cylindrical_or_spherical_objects_and_its_application_to_agricultural_commodities?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/26552166_Non-destructive_method_for_internal_quality_determination_of_belgian_endive_Cichorium_Intybus_L?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/227680809_Recent_Developments_in_Noninvasive_Techniques_for_Fresh_Fruit_and_Vegetable_Internal_Quality_Analysis?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/245026412_The_essential_physics_of_medical_imaging_third_edition?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/222489854_Quality_measurement_of_fruits_and_vegetables_Postharvest_Biol_Technol?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==


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evaluate the quality of fresh vegetables non-destructively (Sagartzazu et al. 2008). In acoustic

sound, a device is often used to lightly tap or thump the commodity to create a sound wave

that pass through the product tissue. The characteristics of the sound waves as they pass

though the product can be used to indicate the quality attributes of fruit and vegetables during

postharvest processes (Butz et al. 2005).

A. ACOUSTICS

Acoustic resonance technique is an emerging trend for non-destructive quality evaluation of

fruits and vegetables. This technique is based on the response to sound and vibration when

the source is gently tapped. It can be used to predict the maturity, internal quality, ripening

stage and other similar parameters using the audible frequency range of 20 Hz to 20 kHz. The

availability of high-speed data acquisition and processing technology has renewed research

interest in the development of impact and sonic response techniques (Vahora et al. 2013).

When an acoustic wave reaches to the agricultural products, the reflected or transmitted

acoustic wave depends on the acoustic characteristics of the agricultural products. The

reflected or transmitted acoustic wave can provide information on the interaction between

acoustic wave and agricultural products, and acoustic characteristics such as attenuation

coefficient, transmitting velocity, acoustic impedance, and natural frequency. Different

agricultural products have various acoustic characteristics based on the internal tissue

structures (Sugiyama et al. 19+4; "rnka et al. 2013).

From last three decades, there has been tremendous development in acoustics technology.

It has become a primary method for watermelon sorting and grading (Mizrach et al. 1996).

He et al. (1994) developed pendulum hitting device to judge maturity and other internal

qualities of watermelons without damage, by studying the feature curves of the sound

waveform of watermelons. Sugiyama et al. (1994) studied the relationship between the

transmission velocity and firmness of muskmelons. They found that the transmission velocity

became lower in ripened muskmelons. Based on the study, they developed an instrument to

measure the transmission velocity of sound wave in muskmelons and found that the

transmission velocity of sound wave in edible muskmelons ranged from 37 -50 m/s.

An instrument for measuring the hollow heart and maturity of watermelons (Figure 4) was

developed by Applied Vibro-Acoustics (AVA) Company. It was based on the theory that

everything in the world holds its own special frequency. Lü (2003); (Rao et al. 2004)

developed a quality inspecting system using acoustic technology. The sound waves were

https://www.researchgate.net/publication/227097210_Review_in_Sound_Absorbing_Materials?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/227680809_Recent_Developments_in_Noninvasive_Techniques_for_Fresh_Fruit_and_Vegetable_Internal_Quality_Analysis?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/227097210_Review_in_Sound_Absorbing_Materials?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/227680809_Recent_Developments_in_Noninvasive_Techniques_for_Fresh_Fruit_and_Vegetable_Internal_Quality_Analysis?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==


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collected via microphones and transformed into electric signal. Thereafter this electric signal

was amplified, followed by filtered via processing circuit, and sampled by a data acquisition

board (Figure 5). A correlation was developed between the transmission velocity and soluble

solids content of watermelons and the best correlation coefficient for different striking

positions and growth status of watermelons was found to be 0.81–0.95.

Baltazar et al. (2007) used acoustic impact test to study the ripening process of intact

tomato. They concluded that the non-destructive acoustic impact technique could detect small

physiological changes. The relation between water loss and firmness measurements in

tomatoes during post harvest period was studied by Hertog et al. (2004). The acoustic

stiffness measurement (Figure 6) was found to be suitable for determining the softening

phenomena of individual pepper samples during post harvest period (Zsom-Muha 2008). The

tests applied on two excitation positions of paprika: top (1) and shoulder part (2) the product

showed characteristic frequency peak of the measured acoustic response in these two

positions. One dominant frequency peak can be obtained by the excitation on the top of the

pepper berry. In contrast to this, by the excitation on the shoulder part, other frequency peak

can be seen (probably because of the excitation of other vibration modes) but no significantdifference can be observed as a acoustic stiffness coefficients on both (top and shoulder part)

excitation positions of the pepper berry, may be due to the suitability to excite the pepper

berry on the

top part (Zsom-

Muha 2008).

Figure 4. A portable frequencyresponse measurementinstrumenthtt ://www.ava.co. . Figure 5. Diagram of setup the transmitting velocity

measurement of watermelon Lü 2003 .


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An imaging system technique is a common to obtain spatial information of the samples in

monochromatic forms or colour images. Imaging system is therefore used for colour, shape,

size, surface texture evaluation of food products and to detect surface defect in food samples,

however it cannot identify or detects chemical properties of a food product (Sun 2009; Sun

2010).

A. HYPERSPECTRAL IMAGING

Now days, hyperspectral imaging system has become a powerful tool and popular for food

research. It can capture the spatial data of the whole target at selected wavelengths instead of

measuring spectral values at single point (Huang et al. 2014). It’s an inherently effective tool

because of ability to collect data with both spatial and spectral characteristics of the scanned

target (Wang et al. 2013).

Itoh et al. (2010) used near-infrared hyperspectral imaging system to measure the nitrate

concentration distribution in a vegetable leaf. The nitrate concentration estimated at each

pixel in a leaf image with high accuracy, and the results indicated variation in nitrate

concentration inside a leaf. Wang et al. (2009) developed a NIR reflectance hyperspectral

imaging system for sour skin detection in Vidalia onions. The system consisted of an InGaAs

video camera, normal lens, liquid crystal tunable filter (LCTF), and frame grabber for

acquiring image data, and two tungsten halogen lamps as light sources. The schematic

diagram of the system for transmission experiments to take transmittance images of food

material (sweet onions, onion bulbs), onion sample placed between the light source and the

hyperspectral imaging system as shown in Figure 7 (Wang et al. 2009).

Figure 7. Schematic of hyperspectral imaging system for onion transmittance experiments

One of the very interesting applications of hyperspectral imaging technique is to predict

the sugar content distribution in melons (Sun 2009). Polder et al. (2004) measured the surface

distribution of carotenes and chlorophyll in ripening tomatoes at spectral range of 400 - 700

https://www.researchgate.net/publication/261837569_Recent_Developments_in_Hyperspectral_Imaging_for_Assessment_of_Food_Quality_and_Safety?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/261837569_Recent_Developments_in_Hyperspectral_Imaging_for_Assessment_of_Food_Quality_and_Safety?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==


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nm with 1 nm resolution. Zhang et al. (2013) predicted soluble solid content of tomatoes at

spectral range of 720-990 nm using a prototype of hyperspectral transmittance imaging

system. It has also been used to predict the amount of dry matter, soluble solids content, and

firmness of onions by using a line-scan hyperspectral imaging system with three sensing

modes (reflectance, interactance, and transmittance) at spectral range of 400-1000 nm (Wang

et al. 2013), total soluble solids, total chlorophyll, carotenoid and ascorbic acid content

during bell pepper maturity at spectral range of 550-850 nm (Itoh et al. 2010).

B. MACHINE VISION

During recent years, the machine vision system has been increasingly used for examination of

fruits and vegetables, especially for applications in quality inspection and defect sorting

applications (Eissa and Abdel Khalik 2012). Computer vision system is recognized as the

integration of devices for non-contact optical sensing, computing and decision processes,

which receive and interpret automatically an image of a real scene (Parmar et al. 2011). It

includes capturing, processing and analysis of two-dimensional images, with other noting that

aims to duplicate the effect of human vision by electronically perceiving and understanding

an image. The basic principle of computer vision is described in Figure 8. Image processing

and image analysis are the core of computer vision with numerous algorithms and methods

available to achieve the required classification and measurements (Eissa and Abdel Khalik

2012).

Figure 8. Principle of computer vision system.

An automatic strawberry grading system with photoelectric sensors was designed to

detect shape and grading of strawberry (Liming and Yanchao 2010). A machine vision

system together with linear discriminate analysis based on color information of the pixel in

dry and wet condition of the object was used for discriminating potato tubers from solid clods

https://www.researchgate.net/publication/222361435_Automated_strawberry_grading_system_based_on_image_processing?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/222361435_Automated_strawberry_grading_system_based_on_image_processing?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==


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(Al-Mallahi et al. 2010). A discrimination rate of 92% for wet condition and 73% for dry

condition was successfully achieved (Al-Mallahi et al. 2010). Machine vision system was

developed for guidance of a robot arm to pick the ripe tomato during harvest, via acquiring

images from tomato plant (Arefi et al. 2011).

C. MAGNETIC RESONANCE (MR) AND MAGNETIC RESONANCE IMAGING (MRI)

Magnetic resonance imaging (MRI) has become a well-established technique for non-

destructive analysis of the internal structure of food. The MRI technique provides a non-

destructive method to evaluate both the qualitative and the quantitative properties of

biological materials (Cheng et al. 2011). This technique is based on the interaction of certain

nuclei, such as carbon and hydrogen, with electromagnetic radiation in the radio frequency

range (Slaughter 2009). It is used to evaluate the property of interest for food processing (as

drying), physical tissue damage assessment (as bruising) and others for online sorting process

or detection of internal defects (as internal browning) (Defraeye et al. 2013). Magnetic

resonance imaging technique has been used as a non-invasive research tool for internal

quality assessment of some fruit and vegetables (Mazhar et al. 2013). The physiological

change of tomato at different maturity stages has been visualized in MR (%alteit 1++1;

Zhang and McCarthy 2012). The change in macroscopic structure and water proton

relaxation times during ripening of tomato fruit have been investigated by (Musse et al.

2009). Chemical shift imaging (CSI) technique (nuclear magnetic resonance spectroscopic

method) was employed to investigate spatial–temporal changes in sugar and lycopene

contents of tomatoes during ripening, to provide a better conception of the postharvest

ripening process of tomatoes (Cheng et al. 2011). Dedicated MRI has been used to trace the

thawing process for boiled and frozen edible vegetables such as green soybeans, broad beans,

okra, asparagus and taro. It was measured by the spin-echo method (echo time= 7 ms) with

0.1 or 0.2 s and 1 s repetition times (Koizumi et al. 2006). The pericarp tissue injury in

tomatoes was detected by using in-line MRI equipment (Milczarek et al. 2009).

VI. CONCLUSIONS

The current review focused on some valuable applications of non-destructive methods for

vegetables quality evaluation. Non-destructive techniques are centre of attraction for its

feasibility to predict external and internal quality of vegetables without any loss in structure.

https://www.researchgate.net/publication/268266176_Recognition_and_localization_of_ripen_tomato_based_on_machine_vision?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/236281653_Application_of_MRI_for_tissue_characterisation_of_'Braeburn'_apple?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/223944912_Assessment_of_tomato_pericarp_mechanical_damage_using_multivariate_analysis_of_magnetic_resonance_images?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/223944912_Assessment_of_tomato_pericarp_mechanical_damage_using_multivariate_analysis_of_magnetic_resonance_images?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/236281653_Application_of_MRI_for_tissue_characterisation_of_'Braeburn'_apple?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==https://www.researchgate.net/publication/268266176_Recognition_and_localization_of_ripen_tomato_based_on_machine_vision?el=1_x_8&enrichId=rgreq-b3a4a329-0ad9-4443-a75e-8f8834c61f3e&enrichSource=Y292ZXJQYWdlOzI3MDk1Njk4MTtBUzoxODY1ODMwMDUyNzgyMDhAMTQyMTQ5NjI2MzYyMA==


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Moreover, these techniques provide constitutional variation of the vegetables/vegetable

products and their accurate quantification, leading to better characterization and improved

quality and safety evaluation results. Considering these advantages, it is expected that non-

destructive technology will play more significant role in the field of vegetables/fruits

industries in near future.

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