The problem of technical solutions for the implementation of a software and hardware complex for food quality management is considered. The review and analysis of existing modern control systems is presented, which made it possible to conclude that today food enterprises need new effective solutions using highly efficient intelligent technologies. The analysis of the possibility of intellectualization of the food production quality management system is carried out. The main tasks of this system are presented. It is shown that a practical basis for the implementation of this problem can be the creation of a software and hardware complex for an automated food quality control system using artificial intelligence technologies, which includes neural network technologies, computer vision systems, simulation modeling and an effective combination of hybrid methods and technologies in its arsenal. Methods, algorithms and technologies for the development of the investigated software and hardware complex of an intelligent automated food quality control system are analyzed. The developed generalized functional structure of such an intelligent system and the main stages of its implementation are presented. The main types of support for this system have been developed: information, mathematical and software. The main stages of decision-making on the quality of finished food products have been developed. The necessary technical means are recommended for the implementation of the system. For the practical implementation of the developed intelligent system, the CP1EE14DRA controller from Omron was chosen - a modular programmable controller. As an operator's workstation, a choice should be made in favor of Siemens products - SIMATIC Panel PC. For the tasks of data storage and implementation of calculations, a conventional personal server equipped with a powerful processor, for example, IntelCorei7, has been proposed. It is shown that the implementation of the developed intelligent automated food quality management system makes food industry enterprises more efficient and safe.

1. Balykhin M.G., Borzov A.B., Blagoveshchensky I.G. Methodological foundations for the creation of expert systems for monitoring and predicting the quality of food products using intelligent technologies. Moscow, Frantera, 2017. 338 p. (in Russian).

2. Blagoveshchensky V.G. and others. Development of an expert quality control system in the process of making halva. Living systems and biological safety of the population: collection of materials of the XV international scientific conference of students and young scientists. 2017. pp. 132-137. (in Russian).

3. Blagoveshchensky V.G. et al. Development of a hardware and software complex for monitoring halva production. Development of the food and processing industry in Russia: personnel and science. 2017. pp. 196-199. (in Russian).

4. Balykhin M.G., Borzov A.B., Blagoveshchensky I.G. Architecture and the basic concept of creating an intelligent expert system for quality control of food products. Food Industry. 2017. no. 11. pp. 60 - 63. (in Russian).

5. Kharitonova P.N., Karelina E.B., Blagoveshchensky V.G., Kleho D.Yu. et al. Implementation of the digital twin of management in technological production. In the collection: Intelligent systems and technologies in the food industry: collection of conference materials. 2019. pp. 171–180. (in Russian).

6. Jedermann R., Nicometo M., Uysal I., Lang W. Reducing food losses by intelligent food logistics. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 2014. vol. 372. no. 2017. pp. 20130302. doi: 10.1098/rsta.2013.0302

7. Karelina E.B., Chuvakhin S.V., Klekho D.Yu. Algorithmic support of an automated storage and maturation system for bulk food products. Intelligent systems and technologies in the food industry: collection of conference materials. 2019. pp. 73–80. (in Russian).

8. Kumar I., Rawat J., Mohd N., Husain S. Opportunities of artificial intelligence and machine learning in the food industry. Journal of Food Quality. 2021. vol. 2021. doi: 10.1155/2021/4535567

9. Yussupova N., Kov?cs G., Boyko M., Bogdanova D. Models and methods for quality management based on artificial intelligence applications. Acta Polytechnica Hungarica. 2016. vol. 13. no. 3. pp. 45-60.

10. Utepbergenov I., Kalimoldaev M., Skliarova I., W?jcik W. et al. Intelligent management system of production and quality products for the small and medium business enterprises. Przeglad Elektrotechniczny. 2018. vol. 94. no. 1. pp. 152-156. doi:10.15199/48.2018.01.38

11. Yu B., Zhan P., Lei M., Zhou F. et al. Food quality monitoring system based on smart contracts and evaluation models. IEEE Access. 2020. vol. 8. pp. 12479-12490. doi: 10.1109/ACCESS.2020.2966020

12. Khan Z.H., Khalid A., Iqbal J. Towards realizing robotic potential in future intelligent food manufacturing systems. Innovative food science & emerging technologies. 2018. vol. 48. pp. 11-24. doi:10.1016/j.ifset.2018.05.011

13. Kahraman C., Yan?k S. Intelligent decision making techniques in quality management: a literature review. Intelligent Decision Making in Quality Management. 2016. pp. 1-22. doi: 10.1007/978-3-319-24499-0_1

14. Talib F., Rahman Z. Identification and prioritization of barriers to total quality management implementation in service industry: an analytic hierarchy process approach. The TQM Journal. 2015. doi: 10.1108/TQM-11-2013-0122

15. Karelina E.B., Kleho D.Yu. et al. Integration of adaptive control into technological processes of the food industry. Intelligent systems and technologies in the food industry: collection of conference materials. Moscow, IK MGUPP, 2019. pp. 81–89. (in Russian).

16. Bakoti? D., Rogo?i? A. Employee involvement as a key determinant of core quality management practices. Total Quality Management & Business Excellence. 2017. vol. 28. no. 11-12. pp. 1209-1226. doi: 10.1080/14783363.2015.1094369

17. Akhmetova S.O., Fuschi D.L., Vasili?nait? R. Towards food safety: quality management peculiarities. Journal of Security & Sustainability Issues. 2017. vol. 6. no. 3.

18. Loutfi A., Coradeschi S., Mani G.K., Shankar P. et al. Electronic noses for food quality: A review. Journal of Food Engineering. 2015. vol. 144. pp. 103-111. doi: 10.1016/j.jfoodeng.2014.07.019

19. Sun D.W. Computer vision technology for food quality evaluation. Academic Press, 2016.

20. Wu D., Sun D.W. Colour measurements by computer vision for food quality control–A review. Trends in Food Science & Technology. 2013. vol. 29. no. 1. pp. 5-20. doi: 10.1016/j.tifs.2012.08.004