Recently, there has been an increasing interest in non-destructive methods of quality evaluation, and a considerable amount of effort has been made in that direction. Many kinds of nondestructive techniques have been developed to measure quality components of biological products including fruits and vegetables, and can be classified into mechanical, optical, electromagnetic, and dynamic methods according to their measurement principals. It is noted that most of the quality components listed could be measured at off-line state but not at on-line state on that spot.

Classification of non-destructive quality evaluation is as follows:


  • (1) Gas sensors arrays: electronic noses
  • (2) Impact tests
  • (3) Color


  • (1) Visible/near infrared spectroscopy
  • (2) Time-resolved reflectance spectroscopy
  • (3) Laser spectroscopy


  • (1) Vibrated excitation
  • (2) Ultrasonic
  • (3) X-ray image and CT


(1) Magnetic resonance/MRI


Mechanical non-destructive methods aim at measuring texture characteristics, mainly firmness. It includes low mass impact tests: impact parameters are detected by accelerometers, or resonance frequency is detected by a microphone and technique using electronic noses. The resonance frequency generally changes with ripening and measures a property of the whole fruit. CAS SENSORS ARRAYS: ELECTRONIC NOSES

The electronic noses try to simulate the functioning of the olfactory system (Fig. 13.3). They are made of an array of chemical and electronic sensors with partial specificity and of a system of pattern recognition, able to recognize simple and complex odors (Gardner and Bartlett, 1993). There are many types of sensors, which, based on different principles, react with a change in their properties: metal oxide semiconductors of different types and conducting organic polymers change their electrical properties when absorbing volatile compounds; sensors based on quartz crystal microbalance change their mass, so changing their resonance frequency, which is measured. Different types of sensors differ according to repeatability, to reaction and recover time, to selectivity and to sensitivity to humidity. Electronic noses can recognize classes of compounds. Each sensor reacts to a different set of volatile compounds; the pattern of the combined responses of all the sensors gives a “fingerprint” of a compound or a mixture. The electronic nose cannot analyze and determine the different volatile compounds, like a gas chromatograph, because its response is not unique. It is useful to detect deviations from a standard, whose fingerprint is well known, or to follow changes in times (Riva et ah, 2005).

Electronic noses

FIGURE 13.3 Electronic noses.

Source: Reprinted from Deshmukh et al., 2015. © 2015 with permission from Elsevier. IMPACT TESTS

Fruit firmness can be estimated by different techniques including the measurement of variables extracted from the analysis of impact forces and the rebound technique (Yesim, 2012). There are many ways of using impact sensors, such as: (1) hitting the fruit with some element that includes the sensor; (2) putting the fruit over a load cell and letting a weight fall on it;

(3) placing the fruit on a flat plate with a load cell located beneath it. A technique was developed for measuring firmness that involved impacting fruit with a pendulum. This system, with several modifications, is still used to measure damage in tomatoes (Khalifa et al., 2011). There is a vertical impact sensor to measure the response of fruit to impacts. The sensor consisted of a small, semi-spherical mass with an accelerometer, which was dropped from different heights onto the fruit. Manual impact sensors, lateral impact sensors are some other sensors used as mechanical measures (Jamal, 2012). COLOR ANALYSIS

Visual appearance is one of the main factors used by consumers when purchasing produce. Color is an important part of the visual appearance and is used in many grade standards as a criterion for quality. Color is the human visual perception of light reflected, transmitted, or emitted from an object in the visible portion of the electromagnetic spectrum from 380 to 780 nm. The main factor in the distribution of light energy reflected from the fruit is the presence and concentration of pigments including carotenoids, anthocyanins and other flavonoids, betalains, and chlorophylls in the skin (Butz et al., 2005). The changes in these pigments as fruit develops affect the perception of fruit color and thus the color of fruit is frequently used as an index of maturity or ripeness. The color aspect of visual appearance of the skin can be measured nondestructive^ using three types of sensors: colorimeters, spectrophotometers, and color machine vision systems.

Colorimeters are instruments designed to quantify color in terms of human perception. Colorimeters are broadband instruments that generally divide the information in the visible spectrum into three components similar to the red, green, and blue cone cells in the human eye. Spectrophotometers are designed to provide more detailed information about the optical properties of the sample, typically dividing the infonnation in the visible spectrum into fifteen or more components. Colorimeters and spectrophotometers are designed to give a single average reading over a spot on the sample typically ranging in size from 5 to 25 mm in diameter. For online use or when detailed color information is needed for spatial analysis across a two-dimensional surface, a color machine vision system is typically used. One of the first colorimeters developed was the color difference meter developed by Richard Hunter in 1948 (Jha et al., 2007). Hunter developed a tristimulus color space called L, a, b to mimic human color perception. The L, a, b color space is based upon the opponent color theory of human color perception developed by Hering in 1872 where perception is a function of signals from the rods and cones in the eye that are processed in an antagonistic manner with three opponent channels: black versus white (Hunter’s L value), red versus green (Hunter’s a value), and blue versus yellow (Hunter’s b value) (Fig. 13.4). While many other color systems have been developed since 1948, the Hunter L, a, b system was one of the first used in foods and is commonly used in research studies as a means of measuring ripeness or defects in many commodities including mango.

Color difference meter (Miniscan XE, Hunterlab)

FIGURE 13.4 Color difference meter (Miniscan XE, Hunterlab).


Optical properties are concerned with the response of a matter to UV (180-130 nm) light, which are characterized by reflectance, transmittance, absorbance, or scattering. The main optical techniques being used are image analysis to measure size, shape, color, external defects, and so on, near infrared spectroscopy to measure soluble solid content, dryness, firmness, acidity, and so on reflectance, transmittance, and absorbance spectroscopy to measure color, chemical constituents, internal defects, and so on and laser spectroscopy to measure firmness, visco-elasticity, defects, shape, and so on.


Researchers all over the world have investigated the potential of various technologies for the assessment of fruit qualities. Among all technologies, spectroscopic techniques have drawn great attention for their prominent advantages as these provide accurate information on the structure and properties of organic biosubstances. Compared to chemical methods of identification, spectral methods have the advantage that it provides data faster, are accurate, require small amounts of material, and enable continuous analysis at different stages of processing of the compound extracted without changing the composition of the biosubstance investigated, which enables its recovery.

Spectroscopy provides access to information about the chemical components and physical properties of fruits by obtaining optical information. Visible and near infrared (Vis/NIR) radiation covers the range from 380 to 2500 mn in the electromagnetic spectrum. As the signals of almost all major structures and functional groups of organic compounds can be detected in the Vis/NIR spectrum with a considerably stable spectrogram, spectra in the Vis/NIR range are frequently used for analysis (McClure, 1994) (Fig. 13.5). Wavebands which are commonly used in multispectral and hyper- spectral imaging technologies to assess fruit quality are also in the Vis/NIR region (Mendoza et al., 2011; Shan et al., 2011; Liu et al., 2007). When incident radiation hits a sample, it may be reflected, transmitted, or absorbed. Correspondingly, a spectrum is obtained in the reflectance, transmittance or absorbance mode, each of which can reflect some physical attribute and chemical constitution of the sample. After the spectrum is obtained, chemo- metric methods are applied to extract information concerning the quality attributes and to eliminate the interference of factors irrelevant to sample concentration.

UV-Vis-NIR spectroscopy

FIGURE 13.5 UV-Vis-NIR spectroscopy.

Fruits are sorted manually or automatically on the basis of size, color, and surface defects such as bruises. However, dry matter content, total soluble solids content, sugar content, juice acidity, and firmness are important internal quality attributes of fruit products. Most instrumental techniques to measure these properties are destructive and involve a considerable amount of manual work. Thus, researches have presently been focused on developing nondestructive techniques, for example, Visible,/NIR spectroscopy, for measuring fruit quality attributes. Visible and near infrared (Vis/NIR) radiation covers the range from 380 to 2500 mn in the electromagnetic spectrum. It is regarded different from other spectroscopic techniques. Because once the instrument is calibrated, it could be used for days or months without being recalibrated, with limited sample preparation and high speed of analyses. These advantages have been used to affect the analyses of large batches (Batten, 1998).

Visible/NIR spectrometry has been evaluated for nondestructive estimation of internal starch, soluble solids content, oil contents, water content, dry matter content, acidity, firmness, stiffness factor, and other physiological properties of batches of fruits, such as citruses (Steuer et al., 2001), fresh tomatoes (Slaughter et al., 1996), mangos (Saran- wong et al., 2004), peaches (Slaughter, 1995), pineapples (Guthrie and Walsh, 1987), apples (McGlone et al., 2002). Differences between sound and damaged tissues in visible and near-infrared diffuse reflectance are useful for detecting bruises, chilling injuries, scalds, decay lesions and numerous other defects. Bruises on apples and peaches can be detected at specific NIR wavelengths; however, the wavelengths chosen for apples differ between fresh and aged bruises because of the diying of the injured tissues. When incident radiation hits a sample, it may be reflected, transmitted or absorbed. Correspondingly, a spectrum is obtained in the reflectance, transmittance or absorbance mode, each of which can reflect some physical attribute and chemical constitution of the sample. After the spectrum is obtained, chemometric methods are applied to extract information concerning the quality attributes and to eliminate the interference of factors irrelevant to sample concentration. Spectra in the Vis/NIR range contain abundant information concerning O-H, C-H, and N-H vibration absorptions (Pissard et al., 2013), making the measurement of various quality attributes of fruits possible. Some wavebands contain typical absorption bands for some chemical groups. A brief overview is presented in Table

13.2 to give some guidance for waveband selection.

TABLE 13.2 Overview of Wavebands Containing Typical Absorption Bands for Certain Chemical Groups.

Quality attribute

Chemical group




1190, 1400

Soluble solid content (SSC)




960, 1450

C-H and O-H





960, 1180, 1450, 2000



Combination bands of C-H and O-H


O-H and C-H



C-O from COOH


O-H from carboxyl acids


C=0 from saturated and unsaturated carboxyl acid







Source: Reprinted from Wang et al., 2015. Open access.


Time-resolved reflectance spectroscopy (TRS) is a non-destructive method for optical characterization of highly diffusive media. It has recently gained increasing use in biomedicine for the non-invasive investigation of biological tissues (Yodli and Chance, 1995). Similarly, it has been used for optical characterization of fruit (Cubeddu et al., 200 lab). In TRS, a short laser light pulse is injected into the medium to be analyzed. Due to photon absorption and scattering events, the diffusely reflected pulse is attenuated, broadened and delayed. The absorption coefficient pa and the transport scattering coefficient p’s are simultaneously and independently estimated by fitting the time distribution of the diffusely reflected light pulse, detected by time- correlated single photon counting techniques, with a theoretical model of light propagation. In TRS, light penetration into a diffusive medium depends on the optical properties of the medium and on the source-detector distance.

In most biological tissues such as fruit and vegetables the depth of the probed volume is of the same order as the source-detector distance, which is 1-2 cm (Cubeddu et al., 1999). Consequently, the measurements probe the bulk properties, not the superficial ones, and may provide useful information on internal quality. The novelty with TRS is the use of a pulsed laser source and the detection of the temporal distribution of re-emitted photons. This allows one to measure separately both pa and ps’ in the pulp of the fruit averaged over the probed medium, while continuous wave techniques are intrinsically dependent on the coupled effect of both of them. These optical parameters cany quite distinct information about the tissue since absorption is determined by pigments (chlorophyll, anthocyanins) or key constituents (water, sugars), while scattering is caused by the dielectric constant mismatch in the tissue, and is more related to the cellular structure. Thus, direct measurement of both pa and ps’, as provided by TRS, can provide more valuable information on the probed medium. The time required for one TRS measurement is now one second with a manual portable prototype, but the technique could be adapted for on-line measurement, reducing acquisition time to 10 ms without loss of accuracy.


Laser absorption spectroscopy (LAS) in the mid-infrared region offers a promising new effective teclmique for the quantitative analysis of trace gases in human breath. LAS enables sensitive, elective detection, quantification, and monitoring in real time, of gases present in breath. It summarizes some of the recent advances in LAS based on semiconductor lasers and optical detection techniques for clinically relevant exhaled gas analysis in breath, specifically such molecular biomarkers as nitric oxide, ammonia, carbon monoxide, ethane, carbonyl sulfide, formaldehyde, and acetone. The mid-infrared spectral range is ideal for tunable LAS since most molecular gases possess strong, characteristic fundamental rotational vibrational lines (Bems, 2000). High-resolution LAS can resolve absorption features of targeted molecules and selectively access optimal spectral lines at low (100 Torr) pressure without interference from CO, and H,0 to achieve high levels of trace gas detection sensitivity and specificity. Avoiding CO, and H,0 interferences is particularly important in the development of biomedical gas sensors for breath analysis. The time domain reflectance spectroscopy procedure provides a complete optical characterization of a diffusive sample as it estimates (at the same time and independently) the light absorption inside the tissues and the scattering across them. The light source is a laser, monochromatic, but tunable at several wavelengths, and with a very short pulse rate. The light is directed on to the surface of the fruit through the intact skin using fiber optics positioned perpendicularly to the central part of the fruit. The light penetrates the tissues and part of it is reflected out of the sample at a particular region adjacent to the transmission point. This portion of reflected light was recovered with the collecting fiber optics placed at about 20 mm in parallel to the transmission ones. The three-dimensional region formed by the light which is capable of entering the collecting fiber is constructed by the optical paths of the photons with greater probability of being recovered after suffering internal body reflection.


It includes vibrated excitation to measure firmness, ripeness, sonic technique to measure firmness, visco-elasticity and internal cavity, and density, ultrasonic technique to measure internal cavity and structure, firmness, and tenderness and x-ray image and CT to measure internal cavity and structure and ripeness.


The internal quality of fruit can be non-invasively tested using systems based on vibrational characteristics. Acoustic impulses were used to detect internal hollows; the change in the signal revealing the problem. Frequency spectrum variables were analyzed for their potential as nondestructive predictors of this defect. The band magnitude variables, obtained from the integral of the spectrum magnitudes between two frequencies, best predicted internal disorders. Experimental modal analysis was used to investigate the vibrational performance of fruits and vegetables and to determine the best positions for the impact point and response measurement microphone. A first type spherical mode and its resonant frequency was the best indicator of internal quality problems (Shyam and Matsuoka, 2000). Finite element modal analysis was performed to establish a watermelon shape/character- istics model and to compare theoretical and experimental results. When an object is excited in the audible or no audible range of frequencies it responds by vibrating. The amplitude peaks obtained in the frequency spectrum are its resonant frequencies, the appearance of which is related to the elasticity, density, size, and shape of the object (Jamal, 2012).


The non-destructive ultrasonic measurement system was depended for the assessment of same transmission parameters which might have quantitative relation with the maturity, firmness, and other quality-related properties of fruits and vegetables. Fruit important features can be evaluated by ultrasonic non-destructive method (Fig. 13.6). This method is based on energy transmission into product and evaluation of response energy (Jamal, 2012). When the system implementing an ultrasonic method for non-destructive measurements of internal quality of fruits and vegetables was tested by pair of ultrasonic transducers, one acts as transmitted and the other as a receiver for some transmission of sound wave through peel and flesh of the fruits and the reception of the transient signal. Ultrasonic waves can be transmitted, reflected, refracted, or diffracted as they interact with the material. Wave propagation velocity, attenuation, and reflection are the important ultrasonic parameters used to evaluate the tissue properties of horticultural commodities. However, because of the structure and air spaces in fruits and vegetables, it is difficult to transmit sufficient ultrasonic energy through them to obtain useful measurements ultrasonic measurements could be used for firmness determination in some fruits but that a more powerful ultrasonic source is required to penetrate others. Despite numerous studies, few applications

Assembly of ultrasonication unit

FIGURE 13.6 Assembly of ultrasonication unit.

have developed. This method is difficult to use in fruit quality determination since it is strongly attenuated when traveling through fruit tissues and as a result, the ultrasound waves cannot penetrate deeply into the fruit (Zerbini, 2006).


X-ray imaging is an established technique to detect strongly attenuating materials and has been applied to a number of inspection applications within the agricultural and food industries. In particular, there are many applications within the biological sciences where we wish to detect weakly attenuating materials against similar background material. X-ray computed tomography (CT) has been used to image interior regions of fruits with varying moisture and, to a limited extent, density states. The images were actually maps of X-ray absoiption of fruit cross sections. X-ray absorption properties were evaluated using normal fruits alternatively canned and sequentially freeze-dried, fruit affected by water core disorder, and normal fruits freeze- dried to varying levels. The results suggested that internal differences in X-ray absorption within scans of fruit cross-sections are largely associated with differences in volumetric water content. Similarly, the physiological constituents have been monitored in fruits by CT methods in which X-ray absorbed by the fruits is expressed in CT number and used as an index for measuring the changes in internal quality of the fruit. Relationships between the CT number, and the physiological contents were determined and it was concluded that X-ray CT imaging could be an effective tool in the evaluation of fruit internal quality (Shyam and Matsuoka, 2000). X-ray has been explored for inspecting the interior of agricultural commodities. The intensity of energy exiting the product is dependent upon the incident energy, absoiption coefficient, density of the product, and sample thickness. Due to the high moisture content in fruits and vegetables, water dominates X-ray absorption.


It includes impedance technology to measure moisture contents, density, sugar content, density, and internal cavity and NMR and MRI to measure sugar content, oil, moisture content, internal defect, and structure. NMR TECHNOLOGY

NMR or commonly known as MRI technology lias the potential for detecting size and shape of agricultural products. MRI involves high magnetic field to objects in its exact or isocenter. A strong magnetic field is applied with the presence of water in fruits and vegetables for monitoring the information of spatial distribution of proton density, relaxation and self-diffusional parameters inside the sample. MRI is used to differentiate the component in biological materials such as water, fat, oil, or salt. So, MRI technology makes it attractive for scanning intact fruit and vegetable. The physical properties such as size, shape and volume and have been correlated well with firmness, soluble solid can be measured by MRI technique (Abbott, 1999). The relationship between density and internal quality of watermelon also could be estimated by multiple regression analysis with mass was investigated by Kato (1997). MRI technology has been used successfully to detect size and shape of agricultural products, but the use of MRI technique is limited. This is because of the expensive equipment and low speed of capturing image in the past years (Clark et al., 1997).


Computer vision, also known as computer image processing or machine vision, is the science that develops the theoretical and algorithm basis by which useful information about an object which extracted from an observed image. This technique is widely used in food industry especially on examination of fruits and vegetables. The example of application of machine vision including grading, quality evaluation from external parameters or internal parameters, monitoring of fruit processes during storage since it is more reliable and objective than human inspection. Commonly, the features of the product such as color, size, shape, texture, and presence of the damage are the parameters of interest in controlling quality agricultural products. A lot of researches have been done on detecting the quality of the fruit based on machine vision. Sudhakara et al. (2004) have developed a model for on-line sorting for apple based on color, size, and shape. Sadrnia et al. (2007) have determined the shape of watermelon and Ohali (2011) has developed a prototype computer vision based on size, shape intensity and defects. The computer vision is an automated inspection of agricultural simplifies tedious in monitoring process. However, it takes a long time and requires complex apparatus to perform the task. Furthermore, the analysis of digital image requires specialized and expensive software to successfully process the image. Sometimes color is not the most suitable parameters to be assessed. As for example, it becomes complicated to differentiate the fruits and leaves of citrus fruit on the tree because both of them have the same color.

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