Методы определения цветовых характеристик растительного сырья. Обзор

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.

1. Barrett, 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/10.1080/10408391003626322

2. Lunadei, 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.2011.02.001

3. Bhargava, 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/s11042–019–08564–3

4. Li, 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.2013.02.016

5. Li, 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.2011.05.010

6. Leemans, 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.2002.0131

7. Kurita, 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.18.145

8. Dhakshina 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.2020.103090

9. Luna-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/electronics10091055

10. Maldonado, 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.2016.07.023

11. Mendoza, 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.x

12. Zhang, 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/s120912489

13. Fan, 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.2013.04.015

14. Al 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.2010.03.003

15. Hasankhani, 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.v4n5p235

16. Costa, 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.2009.05.001

17. ElMasry, 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.2012.03.027

18. Kheiralipour, 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.12558

19. Gan, 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.2018.07.011

20. Liu, 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/10.1145/3318299.3318338

21. Lana, 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.2004.07.001

22. Septiarini, 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.2021.110245

23. Lal, 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.2017.8071621

24. Ayllon, 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/10.1145/3366650.3366681

25. Mazen, 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/s13369–018-03695–5

26. Peter, 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/10.1145/3184066.3184068

27. Harel, 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.2020.103274

28. Wu, 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.2012.08.004

29. Hunt, R.W.G. (2005). The reproduction of colour. John Wiley & Sons, 2005.

30. Петрова, Л.А., Климова, Д.О. (2013). Оценка качества свежих грибов и сроки хранения. Вестник ОрелГИЭТ, 2(24), 166–170.

31. Мухутдинова, С.М. (2006). Органолептическая оценка качества плодового тела белого гриба. Современные наукоемкие технологии. 2, 67–69.

32. 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/jf9907236

33. Balaban, M.O., Odabasi, A. Z. (2006). Measuring color with machine vision. Food Technology, 60(12), 32–36.

34. 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.2006.03.006

35. Семенова, 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-261–265

36. 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.2115

37. Wu, 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.2008.12.024

38. Wu, 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.2008.01.056

39. Wu, 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.2009.11.045

40. Patel, 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/s13197–011–0321–4

41. Lin, 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.2019.05.007

42. Lu, 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.13100

43. Zhang, 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.2018.05.012

44. Nasiri, 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/fsn3.1000

45. Huang, 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.2019.04.062

46. Djekic, 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.8142

47. Wang, 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.20693

48. Tarafdar, 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.12920

49. Nakilcioğlu-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/10.1590/0001-3765202020181387

50. Song, 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.2018.11.020

51. Pathare, 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/s11947–012–0867–9

52. Ali, 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.2017.04.008

53. Bhange, 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.2015.08.022

54. Лисицын, 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-2016–1–4–51–56

55. 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-2018–3–4–4–15

56. Насонова, 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/10.21323/2071–2499–2020–5–22–24

57. 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.2014.07.001

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

59. Smys 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/10.1007/978–3–030–37218–7

60. Sabzi, 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.2017.09.002

61. Kwoopnaan 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/ICECCO48375.2019.9043259

62. Lorente, 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/s11947–011–0725–1

63. 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)

64. 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/s10586–019–03029–6

65. Bhargava, 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