Lviv Polytechnic National University
Lviv Polytechnic National University

The food industry is going through constant improvements and is subject to analyzing consumer needs, product quality research is essential to striking this balance. In this regard, meat quality, the most essential food category, should be studied with unbiased methods that give precise and correct results. Classification algorithms are considered one of the main components of developing an objective and reliable method of meat quality assessment. Such algorithms imply meat analysis and classification automation with many parameters in mind, which eventually gives a chance to make quick and correct decisions concerning its quality.

  1. "Digital 2024: Global overview report – dataReportal - global digital insights". DataReportal - Global Digital Insights. Date of access:  April       22.           2024.       [Online]. Available: report.
  2. C. Ruedt, M. Gibis, and J. Weiss, “Meat color and iridescence: Origin, analysis, and approaches to modulation”, Comprehen- sive Rev. Food Sci. Food Saf., Jun. 2023. Accessed: May 2, 2024. [Online]. Available: 4337.13191
  3. O. R. Kutyansky and M. M. Mykyichuk, "Application of classi- fication algorithms in quality control of meat products," in Abstr. XI Int. Scientific Practical Conf., Florence, Italy, Mar. 18-20, 2024. 2024. pp. 341-342. [Online]. Available: URL: education-and-industry-experience-problems-and-prospects/
  4. Z. Keita. "Classification in machine learning: A guide for be- ginners". Learn Data Science and AI Online | DataCamp. Date of access: April 22. 2024. [Online]. Available: https://www.
  5. "Albumentations Documentation - What is image augmenta- tion". Albumentations: fast and flexible image augmentations. Date of access: April 22. 2024. [Online]. Available: https://
  6. I. M. Kobasa, L. M. Cheban, M. M. Vorobets, V. H. Yukalo, and M. Kukhtyn, Chemical and microbiological analysis of food products. Chernivtsi: Cherniv. nats. un-t, 2014. [Online]. Available: bitstream/handle/123456789/3704/%D0%A5%D1% 96%D0%BC%D1%96%D1%87%D0%BD%
  7. I. F. Ovchynnikova, S. O. Dubinina, T. M. Letuta, M. O. Naumenko, and A. A. Dubinina, Methods for determin- ing the falsification of goods. Kyiv: Pub.dim "Profes- sional", 2010.                 [Online].      Avalilable:
  8. N. Huynh. "Understanding loss functions for classification". Medium. Date of access: April 22. 2024. [Online]. Available: functions-for-classification-81c19ee72c2a
  9. M. Dawood. "Introduction to classification algorithms". Me- dium. Date of access: April 22. 2024. [Online]. Available: classification-algorithms-8e42b37adebf.
  10. L. Chen, S. Li, Q. Bai, J. Yang, S. Jiang, and Y. Miao, “Review of image classification algorithms based on convolutional neu- ral networks”, Remote Sens., vol. 13, no. 22, p. 4712, Nov. 2021. Accessed: May 2, 2024. [Online]. Available:
  11. M. Aladhadh, “A review of modern methods for the detection of foodborne pathogens”, Microorganisms, vol. 11, no. 5, p. 1111, Apr. 2023. Accessed: May 2, 2024. [Online]. Available:
  12. C. Xu, L. Kong, H. Gao, X. Cheng, and X. Wang, “A Review of Current Bacterial Resistance to Antibiotics in Food Ani- mals”, Frontiers Microbiol., vol. 13, May 2022. Accessed: May 2, 2024. [Online]. Available: fmicb.2022.822689.
  13. M. Hayes, “Measuring protein content in food: An overview of methods”, Foods, vol. 9, no. 10, p. 1340, Sep. 2020. Accessed:May            2,            2024.           [Online].           Available: