Now customize the name of a clipboard to store your clips. The simplest global thresholding method is by manual selection, in which the threshold is manually selected by researchers using graphic–user interface image-processing software such as Photoshop (Adobe Systems Incorporated, USA), Aphelion (AAI, Inc., USA), Optimas (Media Cybernetics, Inc., USA), etc. The most frequently used color model is the RGB model, in which each sensor captures the intensity of the light in the red (R), green (G) and blue (B) spectra respectively. Although this method is the simplest and easiest in implementation, it is not ideal for online automatic food-quality evaluation using computer vision without any human participation. Therefore, it was necessary to compare the effects of fixed and zoom lens biospeckles on the final experimental results. The classification technique is one of the essential features for food quality evaluation using computer vision, as the aim of computer vision is ultimately to replace the human visual decision-making process with automatic procedures. Using sample data, a classification system can generate an updated basis for improved classification of subsequent data from the same source, and express the new basis in intelligible symbolic form (Michie, 1991). Although two-dimensional thresholding performs better than one-dimensional thresholding, a far greater computation load is required for the two-dimensional technique; for this reason, it is less popular in the food industry. Based on the knowledge, intelligent decisions are made as outputs and fed back to the knowledge base at the same time, which generalizes the method that inspectors use to accomplish their tasks. Evaluation Technologies for Food Quality summarizes food quality evaluation technologies, which include sensory evaluation techniques and chemical and physical analysis. The former, also termed as backscattering imaging, has been extensively used for food quality evaluation as the simple imaging processing techniques were required in its data analysis (De Belie et al., 1999; Hashim et al., 2013). Although the techniques described above are all bi-level thresholding, apart from the isodata algorithm, most of them can be easily expanded to tri-level thresholding simply by increasing the number of classes segmented by the threshold to three – object, background1, and background2 (or object1, object2, and background). It has also to be kept in mind that since the respondents could choose only 2 quality properties, those who choose environmental aspects had to do that at the expense of other properties, such as taste, … Furthermore, for the purpose of eliminating the effects of noise in segmentation, two-dimensional histogram thresholding can be proposed. Consequently, these findings will elucidate if specific food characteristics or declarations might be used as markers of the overall quality of food products. Pedreschi et al. The former work mostly for objects whose shape is more or less regular, while the latter are especially suitable for describing shapes with great irregularities. Nevertheless, some advantages and disadvantages of the two methods have already been found. Here, the histogram is preliminarily normalized and regarded as probability distributions using equation (2.3): The distribution is classified into two groups (i.e. Journal of Food Quality Evaluation of Effect of Extraction Solvent on Selected Properties of Olive Leaf Extract Won-Young Cho , 1 Da-Hee Kim , 1 Ha-Jung Lee , 1 Su-Jung Yeon , 1 and Chi-Ho Lee 1 1 Department of Food Science and Biotechnology of Animal Resources, Konkuk University, Seoul 05029, Republic of Korea Figure 2.5. Area and perimeter are preferable to length and width, because they are more reliable and more easily extracted. (2011) estimated the blueberry firmness using a laser air-puff instrument with the correlation coefficient of 0.80 related to the traditional destructive method. Looks like you’ve clipped this slide to already. Although the visual appraisal method has been serving the meat industry for many years, the subjective evaluation leads to some major intrinsic drawbacks, namely inconsistencies and variations of the results in spite of the fact that the graders are professionally trained (Cross et al., 1983). These data indicate that environmental concerns in evaluation of food quality are more prominent with regard to fruits, vegetables and potatoes than meat. Analysis and evaluation are important tools that managers can use to improve their processes and increase efficiency. Clipping is a handy way to collect important slides you want to go back to later. For evaluation each criterion has to be described by indicators. In the entropy-based objective function, the optimal threshold is selected as the intensity value at which the sum entropies of the two classes are maximized. Although the existing human grading system has many intrinsic drawbacks, any new systems designed as a replacement must still be compared with the human system before they can be accepted. Quality evaluation of food products 1. (1986). Descriptive analysis for sensory evaluation, Quality control techniques for food safety, Sensory evaluation of food august 15 2012, No public clipboards found for this slide, Cook, Eat, Repeat: Ingredients, Recipes, and Stories, The Book on Pie: Everything You Need to Know to Bake Perfect Pies, The Book of Difficult Fruit: Arguments for the Tart, Tender, and Unruly (with recipes), Chasing Flavor: Techniques and Recipes to Cook Fearlessly, Unvarnished: A Gimlet-eyed Look at Life Behind the Bar, Procrastibaking: 100 Recipes for Getting Nothing Done in the Most Delicious Way Possible, Wine Girl: The Obstacles, Humiliations, and Triumphs of America's Youngest Sommelier, The Primal Gourmet Cookbook: Whole30 Endorsed: It's Not a Diet If It's Delicious, Drive-Thru Dreams: A Journey Through the Heart of America's Fast-Food Kingdom. of eggs) and internal (chemical, physical, microbial).. Food quality in the United States is enforced by the Food … Food quality can be evaluated by 1. Although transform-based methods are suitable for both micro- and macro-texture patterns, a great deal of computation and computer storage is required. Among the applications where classification techniques have been employed for building a knowledge base, artificial neural network (ANN) and statistical approaches are the two main methods. The evaluation of the safety, freshness and quality of foods in the marketplace, as opposed to at the point of manufacture, is a matter of increasing international interest. Measurement from the colorimeter was used as the standard measurement. Petruzzelli A(1), Foglini M, Paolini F, Framboas M, Serena Altissimi M, Naceur Haouet M, Mangili P, Osimani A, Clementi F, Cenci T, Tonucci F. Vaclavik V.A., Christian E.W. Food quality can be evaluated by sensory and objective methods. The first automatic threshold selecting method was probably by isodata algorithm, which was originally proposed by Ridler and Calvard (1978). Chaoxin Zheng, Da-Wen Sun, in Computer Vision Technology for Food Quality Evaluation, 2008. To date, there is still no relevant study for the use of Raman spectroscopy on small berry fruits. Food Science Texts Series. The use of CV for the color assessment of potato chips requires an absolute color calibration technique based on a common interchange format of color data and the knowledge of which features can be best correlated to the product quality. Therefore, the threshold selected will be much less reliable. In contrast, the entropy-based objective functions retain a more stable performance across images with different ratios of population, yet there is a major problem with entropy-based methods. Raman spectroscopy could be considered as a laser-induced technique for the reason that the Raman signals were excited by the laser. By the proper integration of different types of object measurements, the accuracy of computer vision for food quality inspection may be increased. (2006) recently designed and implemented a CV system to measure representatively and precisely the color of highly heterogeneous food materials, such as potato chips, in L*a*b* units from RGB images (Figure 22.5). (2006) developed a computational color conversion procedure that allows the obtaining of digital images in L*a*b* color units from the RGB images by testing five models: linear, quadratic, gamma, direct, and neural network. As shown in Fig. Liyun Zheng, ... Jinglu Tan, in Computer Vision Technology for Food Quality Evaluation, 2008. The critics often use a food evaluation form to indicate if a particular food, menu, or recipe have met their standards. Experts from the industry may be invited to deliver lectures on the latest technology. Shape measurements can be categorized into two groups – size-dependent measurements (SDM) and size-independent measurements (SIM). Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. Thresholding-based segmentation is a particularly effective technique in the scenario where solid objects are laid upon a contrasting background, as in pizzas. Regions C and D are referred to as noises and edges, and thus are ignored in threshold selection. The general configuration of the classification system. There have been two trends recently in the application of image color for, ). This could support consumers in their purchasing process, without neglecting the importance of nutrition education programmes that are mandatory to improve consumers’ food and nutrition literacy and consequently food choices. Furthermore, it can learn meaningful or non-trivial relationships automatically in a set of training data, and produce a generalization of these relationships that can be used to interpret new, unseen test data (Mitchell et al., 1996). The clustering method that is mainly used in threshold selection is k-means clustering. However, in some cases use of the global thresholding technique alone is not enough to segment the pizza base from the background because the contrast varies within the image. Some researchers used Raman spectroscopy as the non-destructive (Dhakal et al., 2014; He et al., 2014; Qin et al., 2012) and micro-destructive (Fang et al., 2015) analytical tools for controlling food safety and quality. Finally, in order to show the capability of the proposed method, León et al. Since RGB digital cameras obtain information in pixels, León et al. Object Classification Methods. The results are summarized in Figure 22.7, and the error of the CV system was only 1.8 percent. Sensory... 3. Afterwards, partition error of each candidate threshold is computed using equation (2.5), and the one with the smallest partition error is chosen as the optimal threshold. Moreover, to improve the usefulness of the grading system, new instrumentally-measurable characteristics are needed to enhance the power of the grades in predicting eating quality, such as tenderness. Aspects such as theory/basics of the techniques, practical applications (sampling, experimentation, data analysis) for evaluation of quality attributes of food and some recent works reported in literature are presented and discussed. Challenge is to Alternatively, the objective function method might be used. Hardware-orientated spaces are developed for the purpose of facilitating computer hardware processes; human-orientated spaces are aimed to help the human understanding of color; and instrumental spaces are employed for the comparison of computer measurements with those obtained from instruments. Estimation of L*a*b* values of a potato chip: (a) RGB image; (b) segmented image by the method proposed by Mery and Pedreschi (2005); (c) color measured in L*a*b* space using a commercial colorimeter and the approach of León et al. (2008) Evaluation of Food Quality. 2015;31(Supl. The most frequently used color model is the RGB model, in which each sensor captures the intensity of the light in the red (R), green (G) and blue (B) spectra respectively. If the threshold satisfies the criterion that every intensity value of class A (B) is closer to mA (mB) than to mB (mA), the threshold is selected as a candidate threshold. 1.3 Sensory Evaluation and Quality of Food For today’s consumers, the primary consideration for selecting and eating a food commodity is the product’s palatability or eating quality, and other quality parameters, such as nutrition and wholesomeness are secondary (Meiselman & MacFie, 1996; Lawless & Heymann, 1998). Springer, New York, NY. Chapter 2 Food Presentation Chapter 3 Evaluation of Food • Sensory evaluation is the evaluation of the sensory properties of food (appearance, flavour, texture) • It can be carried out by the following steps: 1. look at the food and describe the overall appearance 2. smell the food and describe its aroma 3. cut the food and feel its texture 4. chew the food and describe … Li et al. (2006) compared the color of a potato chip measured by this approach with that obtained by a Hunter Lab colorimeter. See our User Agreement and Privacy Policy. This is a preview of subscription content, access via your institution. The classification technique is one of the essential features … Classifiers can give simple yes or no answers, and they can also give an estimate of the probability that an object belongs to each of the candidate classes. (2008) tested the potential usage of the laser-induced fluorescence spectroscopy for quantifying the blueberry p-coumaroyl-glucose and cinnamoyl-glucose contents. The former, also termed as backscattering imaging, has been extensively used for. Evaluation of the quality of foods for special diets produced in a school catering facility within a HACCP-based approach: a case study. It is suggested that the variance-based objective function generally performs better than the entropy-based one, except for images in which the population (the number of pixels of one class) of one class is relatively larger than that of the other (Read, 1982). For strawberries, Mulone et al. You can change your ad preferences anytime. In thinking and writing about food quality, it can be stated first of all that the term quality covers something different for people involved in the production and distribution of foods and for consumers. Techniques that are available for the extraction of texture measurements include statistical, structural, transform-based, and model-based methods. The worst situation, that the variance-based objective function will produce erroneous results, occurs in images in which the ratio of the population of one class over the other is lower than 0.01 (Kittler and Illingworth, 1985). Knowledge from pollution control and devices for … Sensory analysis (or) sensory evaluation. These features are objective data used to represent the food products, which can be used to form the training set. The computationally hard part of classification is inducing a classifier – i.e., determining the optimal values of whatever parameters the classifier will use. For example, Lenhert and Gilliland (1985) designed a black-and-white (B/W) imaging system for lean-yield estimation, and the application results were reported by Cross et al. The advantage of this method is that it can obtain the areas of the smallest and largest rounds of the pizza for further shape analysis, as shown in the work of Sun and Brosnan (2003a). The threshold for a two-dimensional histogram is illustrated in Figure 2.5. Beef quality assessment by image processing started with the work by Chen et al. However, the different calculation of the sum entropy leads to different entropy thresholding methods, as in those proposed by Pun (1980), Kapur et al. 3):128-144 ISSN 0212-1611 • CODEN NUHOEQ S.V.R. ScienceDirect ® is a registered trademark of Elsevier B.V. ScienceDirect ® is a registered trademark of Elsevier B.V. Computer Vision Technology for Food Quality Evaluation, The measurements of an object most commonly used in, The classification technique is one of the essential features for, Quality Evaluation and Control of Potato Chips and French Fries, ). With a digital camera it is possible to register the color of any pixel of the image of the object using three color sensors per pixel, which depend on the color model being used (Forsyth and Ponce, 2003). The automatic thresholding technique developed by Panigrahi et al. See our Privacy Policy and User Agreement for details. When the probability distribution of an intensity value is too small, the entropy of the value is exponentially larger than those of other values, which will introduce potentially large computation errors (Sahoo et al., 1997). Furthermore, the existing human grading system is qualitative, whereas the quantitative characteristics that contribute to the human grading are not always obvious. An intensity value from l to L is picked as the threshold to segment the histogram into two classes, object and background, with mean intensity values of mA and mB. For cooked meat, the shear strength correlates well with sensory tenderness scores (Shackelford et al., 1995); however, such a method is not practical for commercial fresh-meat grading. Figure 22.5. To distinguish the pizza base area from the background, a thresholding-based image segmentation technique can be employed. The most basic convenient measurement for the size of pizza bases is the area. fitness for purpose. DOI https://doi.org/10.1007/978-0-387-69940-0_1; Publisher Name Springer, New York, NY; Print ISBN 978-0-387-69939-4; Online ISBN 978-0-387-69940-0 If you continue browsing the site, you agree to the use of cookies on this website. Besides the above classical classification approaches, the support vector machine (SVM) is a currently emerging classification technique and has been demonstrated to be feasible for performing such a task. In the case of food, quality primarily involves safety, nutritive value, and acceptance. In most cases, Raman spectroscopy was operated in destructive ways (Fan et al., 2014). Based on the segmented image, the area of the pizza base can be straightforwardly determined by counting the number of pixels assigned to the pizza base. (1983) and Wassenberg et al. Recently, applications of computer vision for, Optical non-destructive techniques for small berry fruits: A review, According to the previous literature, the later-induced biospeckle technology could be categorized into the static and dynamic biospeckle. About Us; Our Team; Services; News & Updates; Get In Touch (1995) is an alternative method that can be applied to segment the background from the images of the pizza base. The new threshold is updated iteratively by the new average intensity values until convergence is achieved. (1985), Sahoo et al. The area of the pizza can then be calculated using the average Feret diameter. Figure 22.6. Region A is regarded as being object (background), and B as being background (object). Access Free Nondestructive Evaluation Of Food Quality By Shyam N Jha and scanning electron microscopies and other nanomaterial-based methods All these approached have a common objective: to simulate a human decision-maker's behavior, while having the advantage of consistency and, to a variable extent, explicitness. As these techniques are less popular and much more complex than the isodata algorithm, objective function, and histogram clustering methods, they are only mentioned here for completeness. Size and shape are two geometrical measurements, while color and texture are measurements of the object surface. Schematic representation of a computer vision system used to convert color images from RGB to L*a*b* space. +233 244 646 016; info@impactfoodhub.com; Home; About. going on in the field of organic food quality”. Figure 22.7. Food quality has both subjective and non-subjective aspects. A look into sensory evaluation methods for the quality control of food and beverages. The average intensity values, mA and mB, for both classes are calculated, and the new threshold is then determined as the average of mA and mB. Copyright © 2021 Elsevier B.V. or its licensors or contributors. Color measurements are dependent on the color spaces used, which include hardware-orientated, human-orientated, and instrumental spaces. Organic food quality is defined through specific aspects and criteria. The discussion of micro-destructive techniques would be presented in Section 3.6. Quality It is the ultimate criterion for desirability of any product. (1997), etc. To develop a computer vision system (CVS) for objective grading of meat quality, several steps are essential. Architecture of the neural network used to estimate L*a*b* values from RGB images. In this review, we collect the most important studies conducted using the hyperspectral imaging technology in assessing the quality and safety of food products, such as fruits and vegetables, legumes, cereals, meats, dairy and egg products. For quality evaluation of other fresh meat, such as pork and lamb, early studies were performed by Kuchida et al. 128 Nutr Hosp. In the variance-based objective function (Otsu, 1979), the optimal threshold t is selected to maximize the between-class variance, which can be calculated by. Backed by powerful classification systems, computer vision provides a mechanism in which the human thinking process is simulated artificially, and can help humans in making complicated judgments accurately, quickly, and very consistently over a long period (Abdullah et al., 2004). The optimal threshold is determined as the one that maximizes the objective function, and is based on the interaction of the two classes with regard to evaluating the success of the thresholds. The two-dimensional histogram is constructed by considering the co-occurrence of the intensity values of pixels, and the average intensity values between pixels and their neighboring pixels (Abutaleb, 1989). Currently meat quality is evaluated through visual appraisal of certain carcass characteristics, such as marbling (intramuscular fat), muscle color, and skeletal maturity. This has seriously limited the ability of the meat industry to provide consumers with products of consistent quality, and subsequently its competitiveness. There have been two trends recently in the application of image color for food quality evaluation: one is to carry out a point analysis, encompassing a small group of pixels for the purpose of detecting small characteristics of the object; the other is to carry out a global analysis of the object under the study of the color histogram in order to analyze its homogeneity (Brosnan and Sun, 2004; Du and Sun, 2004).