Development of an algorithm for assessing the antioxidant properties of cherry extracts using artificial intelligence methods
https://doi.org/10.32634/0869-8155-2025-397-08-136-143
Abstract
Relevance. Artificial intelligence tools are playing an increasingly important role in food technology and biotechnology, significantly accelerating and improving various processes. Operational control of colorimetric parameters of cherry extracts using computer vision allows for a quick assessment of the content of bioactive substances without the cost of experimental studies.
Methods. The objects of the study are samples of cherry extracts. Dilutions from 2 to 0.25% were prepared to obtain images of the test samples. The color characteristics of the extracts were determined using an NR60CP colorimeter. Quantitative determination of the total content of polyphenols in solutions of extracts of different concentrations was carried out using the Folin — Ciocalteu method. RGB images from a digital camera (50 MP) were obtained as input data for classifying objects and compiling a database. The Python programming language, OpenCV library and TensorFlow were used to develop image processing software. TensorFlow extracts features from photographs and adds them to the database.
Results. The research results showed that with an increase in the content of bioactive substances — polyphenols — the color of the extracts changes and becomes darker, which is confirmed by the results of the evaluation of color characteristics with a colorimeter: with an increase in concentration, the redness increases and the lightness of the samples decreases. A database was created by extracting features using the TensorFlow library from ready-made sample images. A program for assessing the content of polyphenols in extracts was developed in the Python programming language. The described model of the system for monitoring the quality of extracts using computer vision can facilitate the determination of the content of bioactive substances on an industrial scale, which requires significant time and resources.
About the Authors
O. V. Zinina
South Ural State University
Russian Federation
Russian Federation
Oksana Vladimirovna Zinina, Doctor of Technical Sciences, Associate Professor of the Department of Food and Biotechnology
76 Lenin Аve., Chelyabinsk, 454080
E. A. Vishnyakova
South Ural State University
Russian Federation
Russian Federation
Elena Aleksandrovna Vishnyakova, Postgraduate
76 Lenin Аve., Chelyabinsk, 454080
M. B. Rebezov
All-Russian Research Institute of Confectionery Industry – branch of the V.M. Gorbatov Federal Scientific Center for Food Systems Russian Academy of Sciences
Russian Federation
Russian Federation
Maksim Borisovich Rebezov, Doctor of Agricultural Sciences, Professor, Chief Researcher
26 Talalikhin Str., Moscow, 109316
References
1. Sarkar T. et al. Application of bio-inspired optimization algorithms in food processing. Current Research in Food Science. 2022; 5: 432–450. https://doi.org/10.1016/j.crfs.2022.02.006
2. Sarkar T. et al. The Fuzzy Cognitive Map-Based Shelf-life Modelling for Food Storage. Food Analytical Methods. 2022; 14 (11). https://doi.org/10.1007/s12161-021-02147-5.
3. Dmitriev N.D., Rogozina E.A. Application of innovative technologies at food enterprises. Vestnik Universiteta. 2020; (7): 36–44 (in Russian). https://doi.org/10.26425/1816-4277-2020-7-36-44
4. Meinert C. et al. Food safety and food security through predictive microbiology tools: a short review. Potravinarstvo Slovak Journal of Food Sciences. 2023; 17: 324–342. https://doi.org/10.5219/1854
5. Sarkar T. et al. Underutilized green leafy vegetables: frontier in fortified food development and nutrition. Critical Reviews in Food Science and Nutrition. 2023; 63(33): 11679–11733. https://doi.org/10.1080/10408398.2022.2095555
6. Shafiq M. et al. Development and quality cum nutritional assessment based on physical properties for corn extruded snacks enriched with protein and carbohydrates: а remedy to malnutrition for society. Potravinarstvo Slovak Journal of Food Sciences. 2024; 18: 633–653. https://doi.org/10.5219/1942
7. Abdel Salam S.G. et al. Bioactive components and antibacterial activity of raw and boiled Egyptian pepper. Journal of Microbiology, Biotechnology and Food Sciences. 2024; 14(3): e11878. https://doi.org/10.55251/jmbfs.11878
8. Smaoui S. et al. Beyond Conventional Meat Preservation: Saddling the Control of Bacteriocin and Lactic Acid Bacteria for Clean Label and Functional Meat Products. Applied Biochemistry and Biotechnology. 2024; 196(6): 3604–3635. https://doi.org/10.1007/s12010-023-04680-x
9. Kalandarov P.I., Abdullaev Kh.Kh., Khaitov A.N., Sharifov Kh.S., Murodova G.F. Modeling of biotechnological objects. BIO Web of Conferences. 2024; 108: 25005. https://doi.org/10.1051/bioconf/202410825005
10. Kobrinsky B.A. Trust in artificial intelligence technologies. Artificial intelligence and decision making. 2024; (3): 3–17 (in Russian). https://doi.org/10.14357/20718594240301
11. Skuliabina O., Petrova K., Nass O., Bapiyev I., Vakhitova A., Baigubenova S. et al. An approach to creating a thinking process in systems empowered with intelligence using 3D environments. E3S Web of Conferences. 2023; 389: 07001. https://doi.org/10.1051/e3sconf/202338907001
12. Mukherjee A. et al. Development of Artificial Vision System for Quality Assessment of Oyster Mushrooms. Food Analytical Methods. 2022; 15(6): 1663–1676. https://doi.org/10.1007/s12161-022-02241-2
13. Sarkar T. et al. Expert Knowledge-Based System for Shelf-Life Analysis of Dairy Cheese Ball (Rasgulla). Food Analytical Methods. 2022; 15(7): 1945–1960. https://doi.org/10.1007/s12161-022-02261-y
14. Mishra M. et al. Allergen30: Detecting Food Items with Possible Allergens Using Deep Learning-Based Computer Vision. Food Analytical Methods. 2022; 15(11): 3045–3078. https://doi.org/10.1007/s12161-022-02353-9
15. Zribi M., Pagliuca P., Pitolli F. A Computer Vision-Based Quality Assessment Technique for the automatic control of consumables for analytical laboratories. Expert Systems with Applications. 2024; 256: 124892. https://doi.org/10.1016/j.eswa.2024.124892
16. Samoilova E.M. et al. Monitoring as an element of quality management system of technological systems. AIP Conference Proceedings. 2023; 2700(1): 020059. https://doi.org/10.1063/5.0125186
17. Gusynina Y.S., Shornikova T.A. Image processing and pattern recognition issues. Proceedings of SPIE. 2022; 12251: 122510B. https://doi.org/10.1117/12.2631054
18. Tikhonyuk N., Paytaeva K., Ivanova S. Digital Production and Biotechnology as a New Techno-Economic Paradigm. BIO Web of Conferences. 2023; 57: 01003. https://doi.org/10.1051/bioconf/20235701003
19. Kovshov E., Kuvshinnikov V., Dolgov N. Digital twins creation based on discrete modelling of non-destructive evaluation objects. E3S Web of Conferences. 2023; 431: 05003. https://doi.org/10.1051/e3sconf/202343105003
20. Degtyareva K.V., Nikolaev S.V., Nelyub V.A., Tynchenko V.S., Borodulin A.S. Automatic monitoring system designed to detect defects in PET preforms. E3S Web of Conferences. 2023; 458: 02002. https://doi.org/10.1051/e3sconf/202345802002
21. Rogozhina I., Guseva M., Andreeva E. Garment Production Quality Evaluation Using Machine Vision. Solovyov D.B., Savalei V.V., Bekker A.T., Petukhov V.I. (eds.). FarEastСon 2021. Proceeding of the International Science and Technology Conference. Singapore: Springer. 2022; 309–318. https://doi.org/10.1007/978-981-16-8829-4_27
22. Blagoveshchensky I.G. The use of computer vision systems to control online the quality of raw materials and finished food industry products. Food processing industry. 2015; (6): 9–13 (in Russian). https://www.elibrary.ru/ulrkxx
23. Lysova N., Solari F., Montanari R. Investigating Research Trends in Computer Vision for Food Quality Control: A Bibliometric Approach. Procedia Computer Science. 2025; 253: 1923–1932. https://doi.org/10.1016/j.procs.2025.01.254
24. Kostyshina E.Ya. Digital economy and computer vision: integrationand application practice in the food industry. Vestnik IMSIT. 2024; (4): 27–33 (in Russian). https://www.elibrary.ru/cxqamm
25. Pandey D.K., Mishra R. Towards sustainable agriculture: Harnessing AI for global food security. Artificial Intelligence in Agriculture. 2024; 12: 72–84. https://doi.org/10.1016/j.aiia.2024.04.003
26. Bartenev D.S., Illarionova E.E. The prospect of using machine vision methods for quality control of milk powder. Food processing industry. 2023; (12): 85–89 (in Russian). https://doi.org/10.52653/PPI.2023.12.12.017
27. Savostin S.D., Besfamilnaya E.M., Vartanov G.V., Vasilkin D.P. Methodology for developing software for automated search for trash in a stream using computer vision equipment. Food processing industry. 2024; (2): 68–71 (in Russian). https://doi.org/10.52653/PPI.2024.2.2.013
28. Mironchenko V.I. Method for determining the number of defective products in a sample based on one measurement result. Izmeritel’naya tekhnika. 2024; (2): 30–34 (in Russian). https://doi.org/10.32446/0368-1025it.2024-2-30-34
29. Tolstel O.V., Shirkin A.E., Kalabin A.L. Constructing a computer vision system for aligning the contents of packages by a delta manipulator in food production. Software & Systems. 2023; 36(2): 334–341 (in Russian). https://doi.org/10.15827/0236-235X.142.334-341
30. Klimachev S.A., Solovyov N.A. The Decision-Making Technique Based on Computer Vision and Selection of Pareto-Optimal Alternative of Technological Production Parameters. Information Technologies. 2023; 29(7): 382–388 (in Russian). https://doi.org/10.17587/it.29.382-388
31. Bobyr M.V., Emelyanov S.G., Milostnaya N.A. Optimization of the number of passes in the problem of logical image filtering. Artificial intelligence and decision making. 2023; (2): 98–107 (in Russian). https://doi.org/10.14357/20718594230208
32. Obukhov A.D., Nazarova A.O. A Control Method Based on Computer Vision and Machine Learning Technologies for Adaptive Systems. Mekhatronika, Avtomatizatsiya, Upravlenie. 2023; 24(1): 14–23 (in Russian). https://doi.org/10.17587/mau.24.14-23
33. Diyazitdinova A.A. Criteria for image superposition in a twocamera technical vision system. Radioelectronics. Nanosystems. Information technologies. 2024; 16(2): 307–314 (in Russian). https://doi.org/10.17725/rensit.2024.16.307
34. Yoon W.B., An S., Oyinloye T.M., Kim J. Developing a Quality Control System in a Continuous Hot Air Heating Process in Surimi Seafood Processing Using Image Analysis and Artificial Intelligence. Processes. 2023; 11(11): 3187. https://doi.org/10.3390/pr11113187
35. Menezes G.L. et al. Empowering informed choices: How computer vision can assists consumers in making decisions about meat quality. Meat Science. 2025; 219: 109675. https://doi.org/10.1016/j.meatsci.2024.109675
36. Shahedi Y., Zandi M., Bimakr M. A computer vision system and machine learning algorithms for prediction of physicochemical changes and classification of coated sweet cherry. Heliyon. 2024; 10(20): e39484. https://doi.org/10.1016/j.heliyon.2024.e39484
37. Liu X.-Y., Yu J.-R., Deng H.-N. Non-Destructive Prediction of Anthocyanin Content of Rosa chinensis Petals Using Digital Images and Machine Learning Algorithms. Horticulturae. 2024; 10(5): 503. https://doi.org/10.3390/horticulturae10050503
38. Al-Saif A.M., Abdel-Sattar M., Aboukarima A.M., Eshra D.H., Górnik K. Physico-Chemical Properties Prediction of Flame Seedless Grape Berries Using an Artificial Neural Network Model. Foods. 2022; 11(18): 2766. https://doi.org/10.3390/foods11182766
39. Lund D.H., Alban L., Hansen C., Dalsgaard A., Denwood M., Olsen A. Using latent class modelling to evaluate the performance of a computer vision system for pig carcass contamination. Preventive Veterinary Medicine. 2025; 241: 106556. https://doi.org/10.1016/j.prevetmed.2025.106556
40. Kang Z., Zhou B., Fei S., Wang N. Predicting the greenhouse crop morphological parameters based on RGB-D Computer Vision. Smart Agricultural Technology. 2025; 11: 100968. https://doi.org/10.1016/j.atech.2025.100968
41. Sunil G.C., Khan A., Horvath D., Sun X. Evaluation of multispectral imaging for freeze damage assessment in strawberries using AI-based computer vision technology. Smart Agricultural Technology. 2025; 10: 100851. https://doi.org/10.1016/j.atech.2025.100851
42. Liao Q. et al. Improving traceability and quality control in the redmeat industry through computer vision-driven physical meat feature tracking. Food Chemistry. 2025; 480: 143830. https://doi.org/10.1016/j.foodchem.2025.143830
43. Anjali et al. State-of-the-art non-destructive approaches for maturity index determination in fruits and vegetables: principles, applications, and future directions. Food Production, Processing and Nutrition. 2024; 6: 56. https://doi.org/10.1186/s43014-023-00205-5
Review
For citations:
Zinina O.V., Vishnyakova E.A., Rebezov M.B. Development of an algorithm for assessing the antioxidant properties of cherry extracts using artificial intelligence methods. Agrarian science. 2025;(8):136-143. (In Russ.) https://doi.org/10.32634/0869-8155-2025-397-08-136-143
Views:
9
Email this article 