foodsystFood systemsПищевые системы2618-97712618-7272Федеральный научный центр пищевых систем им. В.М. Горбатова РАН10.21323/2618-9771-2021-4-4-230-238foodsyst-130Research ArticleСтатьиMethods for determining color characteristics of vegetable raw materials. A reviewМетоды определения цветовых характеристик растительного сырья. Обзорhttps://orcid.org/0000-0002-1665-5445ФедянинаН. И.FedyaninaN. I.

Федянина Наталья Игоревна — старший научный сотрудник, Лаборатория технологии консервирования

42703, Московская обл., г. Видное, ул. Школьная, 78Тел.: +7–495–541–08–92

Natalia I. Fedyanina, Senior researcher, Laboratory of canning technology

Shkolnaia str. 78, 142703, Vidnoe, Moscow regionTel.: +7–495–541–08–92

shatalova@vniitek.ruhttps://orcid.org/0000-0001-7247-7519КарастояноваО. В.KarastoyanovaO. V.

Карастоянова Ольга Вячеславовна — старший научный сотрудник, Лаборатория технологии консервирования

42703, Московская обл., г. Видное, ул. Школьная, 78Тел.: +7–495–541–08–92

Olga V. Karastoyanova, Senior researcher, Laboratory of canning technology

Shkolnaia str. 78, 142703, Vidnoe, Moscow regionTel.: +7–495–541–08–92

okarastoyanova@mail.ruhttps://orcid.org/0000-0002-4108-5835КоровкинаН. В.KorovkinaN. V.

Коровкина Надежда Вячеславовна — младший научный сотрудник, Лаборатория технологии консервирования

42703, Московская обл., г. Видное, ул. Школьная, 78Тел.: +7–495–541–08–92

Nadezhda V. Korovkina, Junior researcher, Laboratory of canning technology

Shkolnaia str. 78, 142703, Vidnoe, Moscow regionTel.: +7–495–541–08–92

corowkinanadya@yandex.ruВсероссийский научно-исследовательский институт технологии консервированияРоссияRussian Research Institute of Canning TechnologyRussian Federation20210501202244230238Copyright © Fedyanina N.I., Karastoyanova O.V., Korovkina N.V., 20222022Федянина Н.И., Карастоянова О.В., Коровкина Н.В.Fedyanina N.I., Karastoyanova O.V., Korovkina N.V.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.fsjour.com/jour/article/view/130

Food product quality defines a complex of food product properties such size, shape, texture, color and others, and determines acceptability of these products for consumers. It is possible to detect defects in plant raw materials by color and classify them by color characteristics, texture, shape, a degree of maturity and so on. Currently, the work on modernization of color control systems has been carried out for rapid and objective measuring information about color of plant raw materials during their harvesting, processing and storage. The aim of the work is to analyze existing methods for determining color characteristics of plant raw materials described in foreign and domestic studies. Also, this paper presents the results of the experimental studies that describe the practical use of methods for measuring food product color. At present, the following methods for determining color characteristics by the sensor analysis principle are used: sensory, spectrophotometric and photometric. These methods have several disadvantages. Therefore, computer vision has found wide application as an automated method for food control. It is distinguished by high confidence and reliability in the process of determining freshness, safety, a degree of maturity and other parameters of plant raw materials that are heterogeneous in terms of the abovementioned indicators. The computer vision method is realized in the following systems: conventional, hyperspectral and multispectral. Each subsequent system is a component of the preceding one. Materials presented in the paper allow making a conclusion about the effectiveness of the computer vision systems with the aim of automatic sorting and determining quality of plant raw materials in the food industry.

Качество пищевых продуктов обусловливает совокупность свойств продукции, таких как размер, форма, текстура, цвет и другие, и определяет приемлемость данной продукции для потребителя. По цвету можно определить дефекты в растительном сырье и классифицировать его по цветовым характеристикам, текстуре, размеру, форме, степени зрелости и т. д. В настоящее время ведутся работы по модернизации систем контроля цвета для быстрого и объективного измерения информации о цвете растительного сырья во время сбора, переработки, а также в процессе его хранения. Целью работы является проведение анализа существующих способов определения цветовых характеристик растительного сырья — они описаны в зарубежных и отечественных работах. Также в данной статье приводятся результаты экспериментальных работ, в которых рассказывается о практическом применении методов определения цвета пищевых продуктов. На сегодняшний день существуют следующие способы определения цветовых характеристик по принципу сенсорного анализа: органолептический, спектрофотометрический, фотометрический. Данные методы отличаются некоторыми недостатками, поэтому в качестве автоматизированного способа контроля пищевых продуктов широкое применение нашло компьютерное зрение. Он отличается высокой достоверностью и надежностью в процессе определения свежести, безопасности, степени зрелости и других параметров растительного сырья, отличающегося неоднородностью по перечисленным выше показателям. Метод компьютерного зрения находит свою реализацию в следующих системах: традиционной, гиперспектральной и многоспектральной. Каждая последующая система является составной частью предыдущей. Представленные в статье материалы позволяют сделать вывод об эффективности систем компьютерного зрения с целью автоматической сортировки и определения качества растительного сырья в пищевой промышленности.

цветспособы определения цветарастительное сырьекачествокомпьютерное зрениеcolormethods for determining colorplant raw materialsqualitycomputer visionСтатья подготовлена в рамках выполнения исследований по государственному заданию № FNEN-2019-00011 Федерального научного центра пищевых систем им. В. М. Горбатова Российской академии наук.The article was published as part of the research topic No. FNEN-2019-00011 of the state assignment of the V. M. Gorbatov Federal Research Center for Food Systems of RAS.ReferencesBarrett, D. M., Beaulieu, J.C., Shewfelt, R. (2010). Color, flavor, texture and nutritional quality of fresh-cut fruits and vegetables: Desirable levels, instrumental and sensory measurement and the effects of processing. Critical Reviews in Food Science and Nutrition, 50(5), 369–389. https://doi.org/10.1080/10408391003626322Barrett, D. M., Beaulieu, J.C., Shewfelt, R. (2010). Color, flavor, texture and nutritional quality of fresh-cut fruits and vegetables: Desirable levels, instrumental and sensory measurement and the effects of processing. Critical Reviews in Food Science and Nutrition, 50(5), 369–389. https://doi.org/10.1080/10408391003626322Lunadei, L., Galleguillos, P., Diezma, B., Lleó, L., Ruiz-Garcia, L. (2011). A multispectral vision system to evaluate enzymatic browning in freshcut apple slices. Postharvest Biology and Technology, 60(3), 225–234. https://doi.org/10.1016/j.postharvbio.2011.02.001Lunadei, L., Galleguillos, P., Diezma, B., Lleó, L., Ruiz-Garcia, L. (2011). A multispectral vision system to evaluate enzymatic browning in freshcut apple slices. Postharvest Biology and Technology, 60(3), 225–234. https://doi.org/10.1016/j.postharvbio.2011.02.001Bhargava, A., Bansal, A. (2020). Quality evaluation of Mono and biColored Apples with computer vision and multispectral imaging. Multimedia Tools and Applications, 79(11–12), 7857–7874. https://doi.org/10.1007/s11042–019–08564–3Bhargava, A., Bansal, A. (2020). Quality evaluation of Mono and biColored Apples with computer vision and multispectral imaging. Multimedia Tools and Applications, 79(11–12), 7857–7874. https://doi.org/10.1007/s11042–019–08564–3Li, J., Rao, X., Wang, F., Wu, W., Ying, Y. (2013). Automatic detection of common surface defects on oranges using combined lighting transform and image ratio methods. Postharvest Biology and Technology, 82, 59–69. https://doi.org/10.1016/j.postharvbio.2013.02.016Li, J., Rao, X., Wang, F., Wu, W., Ying, Y. (2013). Automatic detection of common surface defects on oranges using combined lighting transform and image ratio methods. Postharvest Biology and Technology, 82, 59–69. https://doi.org/10.1016/j.postharvbio.2013.02.016Li, J., Rao, X., Ying, Y. (2011). Detection of common defects on oranges using hyperspectral reflectance imaging. Computers and Electronics in Agriculture, 78(1), 38–48. https://doi.org/10.1016/j.compag.2011.05.010Li, J., Rao, X., Ying, Y. (2011). Detection of common defects on oranges using hyperspectral reflectance imaging. Computers and Electronics in Agriculture, 78(1), 38–48. https://doi.org/10.1016/j.compag.2011.05.010Leemans, V., Magein, H., Destain, M.-F. (2002). On-line fruit grading according to their external quality using machine vision. Biosystems Engineering, 83(4), 397–404. https://doi.org/10.1006/bioe.2002.0131Leemans, V., Magein, H., Destain, M.-F. (2002). On-line fruit grading according to their external quality using machine vision. Biosystems Engineering, 83(4), 397–404. https://doi.org/10.1006/bioe.2002.0131Kurita, M., Kondo, N., Yoshimaru, H., Ninomiya, K. (2006). Extraction methods of color and shape features for tomato grading. Shokubutsu Kankyo Kogaku, 18(2), 145–153. https://doi.org/10.2525/shita.18.145Kurita, M., Kondo, N., Yoshimaru, H., Ninomiya, K. (2006). Extraction methods of color and shape features for tomato grading. Shokubutsu Kankyo Kogaku, 18(2), 145–153. https://doi.org/10.2525/shita.18.145Dhakshina Kumar, S., Esakkirajan, S., Bama, S., Keerthiveena, B. (2020). A microcontroller based machine vision approach for tomato grading and sorting using SVM classifier. Microprocessors and Microsystems, 76, Article 103090. https://doi.org/10.1016/j.micpro.2020.103090Dhakshina Kumar, S., Esakkirajan, S., Bama, S., Keerthiveena, B. (2020). A microcontroller based machine vision approach for tomato grading and sorting using SVM classifier. Microprocessors and Microsystems, 76, Article 103090. https://doi.org/10.1016/j.micpro.2020.103090Luna-Benoso, B., Martínez-Perales, J. C., Cortés-Galicia, J., FloresCarapia, R., Silva-García, V. M. (2021). Detection of diseases in tomato leaves by color analysis. Electronics, 10(9), Article 1055. https://doi.org/10.3390/electronics10091055Luna-Benoso, B., Martínez-Perales, J. C., Cortés-Galicia, J., FloresCarapia, R., Silva-García, V. M. (2021). Detection of diseases in tomato leaves by color analysis. Electronics, 10(9), Article 1055. https://doi.org/10.3390/electronics10091055Maldonado, W., Barbosa, J. C. (2016). Automatic green fruit counting in orange trees using digital images. Computers and Electronics in Agriculture, 127, 572–581. https://doi.org/10.1016/j.compag.2016.07.023Maldonado, W., Barbosa, J. C. (2016). Automatic green fruit counting in orange trees using digital images. Computers and Electronics in Agriculture, 127, 572–581. https://doi.org/10.1016/j.compag.2016.07.023Mendoza, F., Aguilera, J.M. (2006). Application of image analysis for classification of ripening bananas. Journal of Food Science, 69(9), E471-E477. https://doi.org/10.1111/j.1365–2621.2004.tb09932.xMendoza, F., Aguilera, J.M. (2006). Application of image analysis for classification of ripening bananas. Journal of Food Science, 69(9), E471-E477. https://doi.org/10.1111/j.1365–2621.2004.tb09932.xZhang, Y., Wu, L. (2012). Classification of fruits using computer vision and a multiclass support vector machine. Sensors, 12(9), 12489–12505. https://doi.org/10.3390/s120912489Zhang, Y., Wu, L. (2012). Classification of fruits using computer vision and a multiclass support vector machine. Sensors, 12(9), 12489–12505. https://doi.org/10.3390/s120912489Fan, F. H., Ma, Q., Ge, J., Peng, Q. Y., Riley, W. W., Tang, S. Z. (2013). Prediction of texture characteristics from extrusion food surface images using a computer vision system and artificial neural networks. Journal of Food Engineering, 118(4), 426–433. https://doi.org/10.1016/j.jfoodeng.2013.04.015Fan, F. H., Ma, Q., Ge, J., Peng, Q. Y., Riley, W. W., Tang, S. Z. (2013). Prediction of texture characteristics from extrusion food surface images using a computer vision system and artificial neural networks. Journal of Food Engineering, 118(4), 426–433. https://doi.org/10.1016/j.jfoodeng.2013.04.015Al Ohali, Y. (2011). Computer vision based date fruit grading system: Design and implementation. Journal of King Saud University — Computer and Information Sciences, 23(1), 29–36. https://doi.org/10.1016/j.jksuci.2010.03.003Al Ohali, Y. (2011). Computer vision based date fruit grading system: Design and implementation. Journal of King Saud University — Computer and Information Sciences, 23(1), 29–36. https://doi.org/10.1016/j.jksuci.2010.03.003Hasankhani, R., Navid, H. (2012). Potato sorting based on size and color in machine vision system. Journal of Agricultural Science, 4(5), Article 235. https://doi.org/10.5539/jas.v4n5p235Hasankhani, R., Navid, H. (2012). Potato sorting based on size and color in machine vision system. Journal of Agricultural Science, 4(5), Article 235. https://doi.org/10.5539/jas.v4n5p235Costa, C., Menesatti, P., Paglia, G., Pallottino, F., Aguzzi, J., Rimatori, V. et al. (2009). Quantitative evaluation of tarocco sweet orange fruit shape using optoelectronic elliptic fourier based analysis. Postharvest Biology and Technology, 54(1), 38–47. https://doi.org/10.1016/j.postharvbio.2009.05.001Costa, C., Menesatti, P., Paglia, G., Pallottino, F., Aguzzi, J., Rimatori, V. et al. (2009). Quantitative evaluation of tarocco sweet orange fruit shape using optoelectronic elliptic fourier based analysis. Postharvest Biology and Technology, 54(1), 38–47. https://doi.org/10.1016/j.postharvbio.2009.05.001ElMasry, G., Cubero, S., Moltó, E., Blasco, J. (2012). In-line sorting of irregular potatoes by using automated computer-based machine vision system. Journal of Food Engineering, 112 (1–2), 60–68. https://doi.org/10.1016/j.jfoodeng.2012.03.027ElMasry, G., Cubero, S., Moltó, E., Blasco, J. (2012). In-line sorting of irregular potatoes by using automated computer-based machine vision system. Journal of Food Engineering, 112 (1–2), 60–68. https://doi.org/10.1016/j.jfoodeng.2012.03.027Kheiralipour, K., Pormah, A. (2017). Introducing new shape features for classification of cucumber fruit based on image processing technique and artificial neural networks. Journal of Food Process Engineering, 40(6), Article e12558. https://doi.org/10.1111/jfpe.12558Kheiralipour, K., Pormah, A. (2017). Introducing new shape features for classification of cucumber fruit based on image processing technique and artificial neural networks. Journal of Food Process Engineering, 40(6), Article e12558. https://doi.org/10.1111/jfpe.12558Gan, H., Lee, W.S., Alchanatis, V., Ehsani, R., Schueller, J.K. (2018). Immature green citrus fruit detection using color and thermal images. Computers and Electronics in Agriculture, 152, 117–125. https://doi.org/10.1016/j.compag.2018.07.011Gan, H., Lee, W.S., Alchanatis, V., Ehsani, R., Schueller, J.K. (2018). Immature green citrus fruit detection using color and thermal images. Computers and Electronics in Agriculture, 152, 117–125. https://doi.org/10.1016/j.compag.2018.07.011Liu, G., Mao, S., Jin, H., Kim, J.H. (22–24 February 2019). A Robust mature tomato detection in greenhouse scenes using machine learning and color analysis. Proceedings of the 11th International Conference on Machine Learning and Computing — ICMLC, Zhuhai. https://doi.org/10.1145/3318299.3318338Liu, G., Mao, S., Jin, H., Kim, J.H. (22–24 February 2019). A Robust mature tomato detection in greenhouse scenes using machine learning and color analysis. Proceedings of the 11th International Conference on Machine Learning and Computing — ICMLC, Zhuhai. https://doi.org/10.1145/3318299.3318338Lana, M. M., Tijskens, L. M. M., Van Kooten, O. (2005). Effects of storage temperature and fruit ripening on firmness of fresh cut tomatoes. Postharvest Biology and Technology, 35(1), 87–95. https://doi.org/10.1016/j.postharvbio.2004.07.001Lana, M. M., Tijskens, L. M. M., Van Kooten, O. (2005). Effects of storage temperature and fruit ripening on firmness of fresh cut tomatoes. Postharvest Biology and Technology, 35(1), 87–95. https://doi.org/10.1016/j.postharvbio.2004.07.001Septiarini, A., Sunyoto, A., Hamdani, H., Kasim, A.A., Utaminingrum, F., Hatta, H.R. (2021). Machine vision for the maturity classification of oil palm fresh fruit bunches based on color and texture features. Scientia Horticulturae, 286, Article 110245. https://doi.org/10.1016/j.scienta.2021.110245Septiarini, A., Sunyoto, A., Hamdani, H., Kasim, A.A., Utaminingrum, F., Hatta, H.R. (2021). Machine vision for the maturity classification of oil palm fresh fruit bunches based on color and texture features. Scientia Horticulturae, 286, Article 110245. https://doi.org/10.1016/j.scienta.2021.110245Lal, S., Behera, S. K., Sethy, P. K., Rath, A. K. (4–5 May 2017). Identification and counting of mature apple fruit based on BP feed forward neural network. Proceedings of 2017 3rd IEEE International Conference on Sensing, Signal Processing and Security, ICSSS2017, Article 8071621. Chennai, Tamil Nadu. https://doi.org/10.1109/ssps.2017.8071621Lal, S., Behera, S. K., Sethy, P. K., Rath, A. K. (4–5 May 2017). Identification and counting of mature apple fruit based on BP feed forward neural network. Proceedings of 2017 3rd IEEE International Conference on Sensing, Signal Processing and Security, ICSSS2017, Article 8071621. Chennai, Tamil Nadu. https://doi.org/10.1109/ssps.2017.8071621Ayllon, M. A., Cruz, M. J., Mendoza, J. J., Tomas, M. C. (18–20 October 2019). Detection of overall fruit maturity of local fruits using convolutional neural networks through image processing. Proceedings of the 2nd International Conference on Computing and Big Data — ICCBD2019. https://doi.org/10.1145/3366650.3366681Ayllon, M. A., Cruz, M. J., Mendoza, J. J., Tomas, M. C. (18–20 October 2019). Detection of overall fruit maturity of local fruits using convolutional neural networks through image processing. Proceedings of the 2nd International Conference on Computing and Big Data — ICCBD2019. https://doi.org/10.1145/3366650.3366681Mazen, F. M. A., Nashat, A. A. (2019). Ripeness classification of bananas using an artificial neural network. Arabian Journal for Science and Engineering, 44(8), 6901–6910. https://doi.org/10.1007/s13369–018-03695–5Mazen, F. M. A., Nashat, A. A. (2019). Ripeness classification of bananas using an artificial neural network. Arabian Journal for Science and Engineering, 44(8), 6901–6910. https://doi.org/10.1007/s13369–018-03695–5Peter, A., Abdulkadir, S. (2–4 February, 2018). Application of image processing and neural networks in determining the readiness of maize. Proceedings of the 2nd International Conference on Machine Learning and Soft Computing — ICMLSC ’18, Phu Quoc, Island. https://doi.org/10.1145/3184066.3184068Peter, A., Abdulkadir, S. (2–4 February, 2018). Application of image processing and neural networks in determining the readiness of maize. Proceedings of the 2nd International Conference on Machine Learning and Soft Computing — ICMLSC ’18, Phu Quoc, Island. https://doi.org/10.1145/3184066.3184068Harel, B., Parmet, Y., Edan, Y. (2020). Maturity classification of sweet peppers using image datasets acquired in different times. Computers in Industry, 121, Article 103274. https://doi.org/10.1016/j.compind.2020.103274Harel, B., Parmet, Y., Edan, Y. (2020). Maturity classification of sweet peppers using image datasets acquired in different times. Computers in Industry, 121, Article 103274. https://doi.org/10.1016/j.compind.2020.103274Wu, D., Sun, D.-W. (2013). Colour measurements by computer vision for food quality control — A review. Trends in Food Science and Technology, 29(1), 5–20. https://doi.org/10.1016/j.tifs.2012.08.004Wu, D., Sun, D.-W. (2013). Colour measurements by computer vision for food quality control — A review. Trends in Food Science and Technology, 29(1), 5–20. https://doi.org/10.1016/j.tifs.2012.08.004Hunt, R.W.G. (2005). The reproduction of colour. John Wiley & Sons, 2005.Hunt, R.W.G. (2005). The reproduction of colour. John Wiley & Sons, 2005.Петрова, Л.А., Климова, Д.О. (2013). Оценка качества свежих грибов и сроки хранения. Вестник ОрелГИЭТ, 2(24), 166–170.Petrova, L.A., Klimova, D.O. (2013). Quality estimation of fresh mushrooms and storage periods. OrelSIET bulletin, 2(24), 166–170. (In Russian)Мухутдинова, С.М. (2006). Органолептическая оценка качества плодового тела белого гриба. Современные наукоемкие технологии. 2, 67–69.Mukhutdinova, S.M. (2006). Organoleptic assessment of the quality of the fruiting body of the porcini mushroom. Modern high technologies, 2, 67–69. (In Russian)Lee, H. S. (2000). Objective measurement of red grapefruit juice color. Journal of Agricultural and Food Chemistry, 48(5), 1507–1511. https://doi.org/10.1021/jf9907236Lee, H. S. (2000). Objective measurement of red grapefruit juice color. Journal of Agricultural and Food Chemistry, 48(5), 1507–1511. https://doi.org/10.1021/jf9907236Balaban, M.O., Odabasi, A. Z. (2006). Measuring color with machine vision. Food Technology, 60(12), 32–36.Balaban, M.O., Odabasi, A. Z. (2006). Measuring color with machine vision. Food Technology, 60(12), 32–36.Leon, K., Mery, D., Pedreschi, F., Leon, J. (2006). Color measurement in L*a*b* units from RGB digital images. Food Research International, 39(10), 1084–1091. https://doi.org/10.1016/j.foodres.2006.03.006Leon, K., Mery, D., Pedreschi, F., Leon, J. (2006). Color measurement in L*a*b* units from RGB digital images. Food Research International, 39(10), 1084–1091. https://doi.org/10.1016/j.foodres.2006.03.006Семенова, А. В., Морозова, А. А. (2021). Оценка качественных показателей картофеля для промышленной переработки. Пищевые системы, 4(3S), 261–265. https://doi.org/10.21323/2618–9771–2021–4-3S-261–265Semenova, A.V., Morozova, A.A. (2021). Assessment of quality indicators of potatoes for industrial processing. Food systems, 4(3S), 261–265. https://doi.org/10.21323/2618–9771–2021–4–3S-261–265 (In Russian)Melendez-Martinez, A.J., Vicario, I.M., Heredia, F.J. (2005). Instrumental measurement of orange juice colour: a review. Journal of the Science of Food and Agriculture, 85(6), 894–901. https://doi.org/10.1002/jsfa.2115Melendez-Martinez, A.J., Vicario, I.M., Heredia, F.J. (2005). Instrumental measurement of orange juice colour: a review. Journal of the Science of Food and Agriculture, 85(6), 894–901. https://doi.org/10.1002/jsfa.2115Wu, D., Chen, X.J., Shi, P.Y., Wang, S.H., Feng, F.Q., He, Y (2009). Determination of α-linolenic acid and linoleic acid in edible oils using near-infrared spectroscopy improved by wavelet transform and uninformative variable elimination. Analytica Chimica Acta, 634(2), 166–171. https://doi.org/10.1016/j.aca.2008.12.024Wu, D., Chen, X.J., Shi, P.Y., Wang, S.H., Feng, F.Q., He, Y (2009). Determination of α-linolenic acid and linoleic acid in edible oils using near-infrared spectroscopy improved by wavelet transform and uninformative variable elimination. Analytica Chimica Acta, 634(2), 166–171. https://doi.org/10.1016/j.aca.2008.12.024Wu, D., He, Y., Feng, S. (2008). Short-wave near-infrared spectroscopy analysis of major compounds in milk powder and wavelength assignment. Analytica Chimica Acta, 610(2), 232–242. https://doi.org/10.1016/j.aca.2008.01.056Wu, D., He, Y., Feng, S. (2008). Short-wave near-infrared spectroscopy analysis of major compounds in milk powder and wavelength assignment. Analytica Chimica Acta, 610(2), 232–242. https://doi.org/10.1016/j.aca.2008.01.056Wu, D., He, Y., Nie, P. C., Cao, F., Bao, Y.D. (2010). Hybrid variable selection in visible and near-infrared spectral analysis for non-invasive quality determination of grape juice. Analytica Chimica Acta, 659(1–2), 229–237. https://doi.org/10.1016/j.aca.2009.11.045Wu, D., He, Y., Nie, P. C., Cao, F., Bao, Y.D. (2010). Hybrid variable selection in visible and near-infrared spectral analysis for non-invasive quality determination of grape juice. Analytica Chimica Acta, 659(1–2), 229–237. https://doi.org/10.1016/j.aca.2009.11.045Patel, K. K., Kar, A., Jha, S. N., Khan, M. A. (2011). Machine vision system: a tool for quality inspection of food and agricultural products. Journal of Food Science and Technology, 49(2), 123–141. https://doi.org/10.1007/s13197–011–0321–4Patel, K. K., Kar, A., Jha, S. N., Khan, M. A. (2011). Machine vision system: a tool for quality inspection of food and agricultural products. Journal of Food Science and Technology, 49(2), 123–141. https://doi.org/10.1007/s13197–011–0321–4Lin, X. Sun, D.-W. (2019). Research advances in browning of button mushroom (Agaricus Bisporus): Affecting factors and controlling methods. Trends in Food Science and Technology, 90, 63–75. https://doi.org/10.1016/j.tifs.2019.05.007Lin, X. Sun, D.-W. (2019). Research advances in browning of button mushroom (Agaricus Bisporus): Affecting factors and controlling methods. Trends in Food Science and Technology, 90, 63–75. https://doi.org/10.1016/j.tifs.2019.05.007Lu, Y, Zhang, J, Wang, X, Lin, Q., Liu, W., Xie, X. et al. (2016). Effects of UV–C irradiation on the physiological and antioxidant responses of button mushrooms (Agaricus bisporus) during storage. International Journal of Food Science and Technology, 51(6), 1502–1508. https://doi.org/10.1111/ijfs.13100Lu, Y, Zhang, J, Wang, X, Lin, Q., Liu, W., Xie, X. et al. (2016). Effects of UV–C irradiation on the physiological and antioxidant responses of button mushrooms (Agaricus bisporus) during storage. International Journal of Food Science and Technology, 51(6), 1502–1508. https://doi.org/10.1111/ijfs.13100Zhang, K., Pu, Y.-Y., Sun, D.-W. (2018). Recent advances in quality preservation of postharvest mushrooms (Agaricus bisporus): A review. Trends in Food Science and Technology, 78, 72–82. https://doi.org/10.1016/j.tifs.2018.05.012Zhang, K., Pu, Y.-Y., Sun, D.-W. (2018). Recent advances in quality preservation of postharvest mushrooms (Agaricus bisporus): A review. Trends in Food Science and Technology, 78, 72–82. https://doi.org/10.1016/j.tifs.2018.05.012Nasiri, M., Barzegar, M., Sahari, M. A. Niakousari, M. (2019). Efficiency of Tragacanth gum coating enriched with two different essential oils for deceleration of enzymatic browning and senescence of button mushroom (Agaricus bisporus). Food Science and Nutrition, 7(4), 1520–1528. https://doi.org/10.1002/fsn3.1000Nasiri, M., Barzegar, M., Sahari, M. A. Niakousari, M. (2019). Efficiency of Tragacanth gum coating enriched with two different essential oils for deceleration of enzymatic browning and senescence of button mushroom (Agaricus bisporus). Food Science and Nutrition, 7(4), 1520–1528. https://doi.org/10.1002/fsn3.1000Huang, Q., Qian, X., Jiang, T., Zheng, X. (2019). Effect of chitosan and guar gum based composite edible coating on quality of mushroom (Lentinus edodes) during postharvest storage. Scientia Horticulturae, 253, 382–389. https://doi.org/10.1016/j.scienta.2019.04.062Huang, Q., Qian, X., Jiang, T., Zheng, X. (2019). Effect of chitosan and guar gum based composite edible coating on quality of mushroom (Lentinus edodes) during postharvest storage. Scientia Horticulturae, 253, 382–389. https://doi.org/10.1016/j.scienta.2019.04.062Djekic, I., Vunduk, J., Tomašević, I., Kozarski, M., Petrovic, P., Niksic, M. et al. (2016). Total quality index of Agaricus bisporus mushrooms packed in modified atmosphere. Journal of the Science of Food and Agriculture, 97(9), 3013–3021. https://doi.org/10.1002/jsfa.8142Djekic, I., Vunduk, J., Tomašević, I., Kozarski, M., Petrovic, P., Niksic, M. et al. (2016). Total quality index of Agaricus bisporus mushrooms packed in modified atmosphere. Journal of the Science of Food and Agriculture, 97(9), 3013–3021. https://doi.org/10.1002/jsfa.8142Wang, H., Cui, G., Luo, M.R., Xu, H. (2011). Evaluation of colour-difference formulae for different colour-difference magnitudes. Color Research and Application, 37(5), 316–325. https://doi.org/10.1002/col.20693Wang, H., Cui, G., Luo, M.R., Xu, H. (2011). Evaluation of colour-difference formulae for different colour-difference magnitudes. Color Research and Application, 37(5), 316–325. https://doi.org/10.1002/col.20693Tarafdar, A., Shahi, N.C., Singh, A. (2020). Color assessment of freeze-dried mushrooms using Photoshop and optimization with genetic algorithm. Journal of Food Process Engineering, 43(1), Article e12920. https://doi.org/10.1111/jfpe.12920Tarafdar, A., Shahi, N.C., Singh, A. (2020). Color assessment of freeze-dried mushrooms using Photoshop and optimization with genetic algorithm. Journal of Food Process Engineering, 43(1), Article e12920. https://doi.org/10.1111/jfpe.12920Nakilcioğlu-Taş, E., Ötleş, S. (2020). Kinetics of colour and texture changes of button mushrooms (Agaricus bisporus) coated with chitosan during storage at low temperature. Anais da Academia Brasileira de Ciencias, 92(2), Article e20181387, 1–15.https://doi.org/10.1590/0001-3765202020181387Nakilcioğlu-Taş, E., Ötleş, S. (2020). Kinetics of colour and texture changes of button mushrooms (Agaricus bisporus) coated with chitosan during storage at low temperature. Anais da Academia Brasileira de Ciencias, 92(2), Article e20181387, 1–15.https://doi.org/10.1590/0001-3765202020181387Song, Y., Hu, Q., Wu, Y., Pei, F., Kimatu, B.M., Su, A. at al. (2018). Storage time assessment and shelf-life prediction models for postharvest Agaricus bisporus. LWT, 101, 360–365. https://doi.org/10.1016/j.lwt.2018.11.020Song, Y., Hu, Q., Wu, Y., Pei, F., Kimatu, B.M., Su, A. at al. (2018). Storage time assessment and shelf-life prediction models for postharvest Agaricus bisporus. LWT, 101, 360–365. https://doi.org/10.1016/j.lwt.2018.11.020Pathare, P.B., Opara, U.L., Al-Said, F.A.-J. (2012). Colour measurement and analysis in fresh and processed foods: A Review. Food and Bioprocess Technology, 6(1), 36–60. https://doi.org/10.1007/s11947–012–0867–9Pathare, P.B., Opara, U.L., Al-Said, F.A.-J. (2012). Colour measurement and analysis in fresh and processed foods: A Review. Food and Bioprocess Technology, 6(1), 36–60. https://doi.org/10.1007/s11947–012–0867–9Ali, H., Lali, M.I., Nawaz, M.Z., Sharif, M., Saleem, B.A. (2017). Symptom based automated detection of citrus diseases using color histogram and textural descriptors. Computers and Electronics in Agriculture, 138, 92–104. https://doi.org/10.1016/j.compag.2017.04.008Ali, H., Lali, M.I., Nawaz, M.Z., Sharif, M., Saleem, B.A. (2017). Symptom based automated detection of citrus diseases using color histogram and textural descriptors. Computers and Electronics in Agriculture, 138, 92–104. https://doi.org/10.1016/j.compag.2017.04.008Bhange, M., Hingoliwala, H.A. (2015). Smart farming: Pomegranate disease detection using image processing. Procedia Computer Science, 58, 280–288. https://doi.org/10.1016/j.procs.2015.08.022Bhange, M., Hingoliwala, H.A. (2015). Smart farming: Pomegranate disease detection using image processing. Procedia Computer Science, 58, 280–288. https://doi.org/10.1016/j.procs.2015.08.022Лисицын, А.Б., Козырев, И.В. (2016). Исследование цветовых характеристик мышечной и жировой тканей и мраморности говядины. Теория и практика переработки мяса. 1(4), 51–56. https://doi.org/10.21323/2414–438X-2016–1–4–51–56Lisitsyn A. B., Kozyrev, I.V. (2016). Researching of meat and fat colour and marbling in beef. Theory and practice of meat processing, 1(4), 51–56. https://doi.org/10.21323/2414–438X-2016–1–4–51–56 (In Russian)Tomasevic, I.B. (2018). Сomputer vision system for color measurements of meat and meat products: A Review. Theory and practice of meat processing, 3(4), 4–15. https://doi.org/10.21323/2414–438X-2018–3–4–4–15Tomasevic, I.B. (2018). Сomputer vision system for color measurements of meat and meat products: A Review. Theory and practice of meat processing, 3(4), 4–15. https://doi.org/10.21323/2414–438X-2018–3–4–4–15Насонова, В.В., Туниева, Е.К., Мотовилина, А.А., Милеенкова Е. В. (2020). Изменение цветовых характеристик кулинарных изделий из мяса индейки, изготовленных с применением низкотемпературной тепловой обработки. Все о мясе, 5, 22–24. https://doi.org/10.21323/2071–2499–2020–5–22–24Nasonova, V.V., Tunieva, E.K., Motovilina, A.A., Mileenkova, E.V. (2020). Changes in color characteristics of culinary products from turkey meat produced with the use of low-temperature heat treatment. Vsyo o myase, 5, 22–24. https://doi.org/10.21323/2071–2499–2020–5–22–24 (In Russian)Zhang, Y., Wang, S., Ji, G., Phillips, P. (2014). Fruit classification using computer vision and feedforward neural network. Journal of Food Engineering, 143, 167–177. https://doi.org/10.1016/j.jfoodeng.2014.07.001Zhang, Y., Wang, S., Ji, G., Phillips, P. (2014). Fruit classification using computer vision and feedforward neural network. Journal of Food Engineering, 143, 167–177. https://doi.org/10.1016/j.jfoodeng.2014.07.001Zhang, B., Huang, W., Li, J., Zhao, C., Fan, S., Wu, J. et al. (2014). Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: A review. Food Research International, 62, 326–343. https://doi.org/10.1016/j.foodres.2014.03.012Zhang, B., Huang, W., Li, J., Zhao, C., Fan, S., Wu, J. et al. (2014). Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: A review. Food Research International, 62, 326–343. https://doi.org/10.1016/j.foodres.2014.03.012Smys S., Tavares J. M. R. S., Balas V. E., Iliyasu A. M. (September 25–26, 2019). Computational vision and bio-inspired computing. International Conference: Advances in Intelligent Systems and Computing. https://doi.org/10.1007/978–3–030–37218–7Smys S., Tavares J. M. R. S., Balas V. E., Iliyasu A. M. (September 25–26, 2019). Computational vision and bio-inspired computing. International Conference: Advances in Intelligent Systems and Computing. https://doi.org/10.1007/978–3–030–37218–7Sabzi, S., Abbaspour-Gilandeh, Y., García-Mateos, G. (2018). A new approach for visual identification of orange varieties using neural networks and metaheuristic algorithms. Information Processing in Agriculture, 5(1), 162–172. https://doi.org/10.1016/j.inpa.2017.09.002Sabzi, S., Abbaspour-Gilandeh, Y., García-Mateos, G. (2018). A new approach for visual identification of orange varieties using neural networks and metaheuristic algorithms. Information Processing in Agriculture, 5(1), 162–172. https://doi.org/10.1016/j.inpa.2017.09.002Kwoopnaan Tetu, V. I., Olatubosun, A., Ovey, O. C. (10–12 December 2019). The application of digital image processing in extracting the image features in maize samples that aid learning and classification. 15th International Conference on Electronics, Computer and Computation (ICECCO), Abuja. Article 9043259. https://doi.org/10.1109/ICECCO48375.2019.9043259Kwoopnaan Tetu, V. I., Olatubosun, A., Ovey, O. C. (10–12 December 2019). The application of digital image processing in extracting the image features in maize samples that aid learning and classification. 15th International Conference on Electronics, Computer and Computation (ICECCO), Abuja. Article 9043259. https://doi.org/10.1109/ICECCO48375.2019.9043259Lorente, D., Aleixos, N., Gomez-Sanchis, J., Cubero, S., Garcia-Navarrete, O. L., Blasco, J. (2011). Recent advances and applications of hyperspectral imaging for fruit and vegetable quality assessment. Food and Bioprocess Technology, 5(4), 1121–1142. https://doi.org/10.1007/s11947–011–0725–1Lorente, D., Aleixos, N., Gomez-Sanchis, J., Cubero, S., Garcia-Navarrete, O. L., Blasco, J. (2011). Recent advances and applications of hyperspectral imaging for fruit and vegetable quality assessment. Food and Bioprocess Technology, 5(4), 1121–1142. https://doi.org/10.1007/s11947–011–0725–1Khan, S., Narvekar, M. (2020). Novel fusion of color balancing and superpixel based approach for detection of tomato plant diseases in natural complex environment. Journal of King Saud University — Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2020.09.006 (unpublished data)Khan, S., Narvekar, M. (2020). Novel fusion of color balancing and superpixel based approach for detection of tomato plant diseases in natural complex environment. Journal of King Saud University — Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2020.09.006 (unpublished data)Yogesh, Dubey, A. K., Ratan, R., Rocha, A. (2019). Computer vision based analysis and detection of defects in fruits causes due to nutrients deficiency. Cluster Computing, 23(3), 1817–1826. https://doi.org/10.1007/s10586–019–03029–6Yogesh, Dubey, A. K., Ratan, R., Rocha, A. (2019). Computer vision based analysis and detection of defects in fruits causes due to nutrients deficiency. Cluster Computing, 23(3), 1817–1826. https://doi.org/10.1007/s10586–019–03029–6Bhargava, A., Bansal, A. (2020). Quality evaluation of Mono & bi-Colored Apples with computer vision and multispectral imaging. Multimedia Tools and Applications, 79 (11–12), 7857–7874. https://doi.org/10.1007/s11042–019–08564–3Bhargava, A., Bansal, A. (2020). Quality evaluation of Mono & bi-Colored Apples with computer vision and multispectral imaging. Multimedia Tools and Applications, 79 (11–12), 7857–7874. https://doi.org/10.1007/s11042–019–08564–3

The authors declare that there are no conflicts of interest present.