Yasin Ozdemir¹*, Omer Nuri Cam² and Seda Kayahan¹

¹Ataturk Horticultural Central Research Institute, Food Technologies Department - Yalova, Turkey
²Uludag University Technical Sciences, Vocational School, Bursa, Turkey

*Corresponding Author: Yasin Ozdemir, Ataturk Horticultural Central Research Institute, Food Technologies Department – Yalova, Turkey.

Received: April 14, 2021; Published: June 09, 2021

Abstract

There is a strong relationship between food poisoning and inadequate personal hygiene of staff in food production. Innovative food hygiene control methods will be beneficial and effective tools for ensuring high food safety and quality. (Artificial intelligence is one of the potential tool to control them.) This review summarizes and explain some uses and future potential use cases of artificial intelligence in some food safety control measurements. Low cost, continuous , objective and real time control may be possible with artificial intelligence uses in the food industry. Future studies should focus on new usage areas of artificial intelligence control tools on food industry. However, in this respect we recommend to perform preventive control measurements rather than final product quality control.

Keywords: Hygiene Control; Food Quality; Artificial Intelligence; Computer Vision; HACCP

Bibliography

1. “Food safety”. Key facts (2020).
2. Green L R., et al. “Factors related to food worker hand hygiene practices”. Journal of Food Protection 70 (2007): 661-666.
3. Olsen S L., et al. “Surveillance for foodborne disease outbreaks—United States, 1993- 1997”. Morbidity and Mortality Weekly Report 49 (2000): 1-51.
4. Guzewich J and Ross M. “Evaluation of risks related to microbiological contamination of ready-to-eat food by food preparation workers and the effectiveness of interventions to minimize those risks” (1999).
5. Adane M., et al. “Food hygiene and safety measures among food handlers in street food shops and food establishments of Dessie town, Ethiopia: A community-based cross-sectional study”. PloS one 13 (2018): e0196919.
6. Ali A I and Immanuel G. “Assessment of hygienic practices and microbiological quality of food in an institutional food service establishment”. Journal of Food Processing and Technology 8 (2017): 2-8.
7. Trafialek J., et al. “Street food vendors’ hygienic practices in some Asian and EU countries-A survey”. Food Control 85 (2018): 212-222.
8. Patel K K., et al. “Machine vision system: a tool for quality inspection of food and agricultural products”. Journal of Food Science and Technology 49 (2012): 123-141.
9. McAllister P., et al. “Combining deep residual neural network features with supervised machine learning algorithms to classify diverse food image datasets”. Computers in Biology and Medicine 95 (2018): 217-233.
10. Zuech N. “Understanding and Applying Machine Vision”. 2ndeds, Vision Systems International Yardley, Pennsylvania, MarcelDekker, Inc.,(2000): 1-
11. Sun D-W. “Inspecting pizza topping percentage and distribution by a computer vision method”. Journal of Food Engineering 44 (2000): 245-249.
12. Vithu P and Moses J A. “Machine vision system for food grain quality evaluation: A review”. Trends in Food Science and Technology 56 (2016): 13-20.
13. Lukinac J., et al. “Computer vision method in beer quality evaluation—a review”. Beverages5 (2019): 38-47.
14. Du C J and Sun D W. “Learning techniques used in computer vision for food quality evaluation: a review”. Journal of Food Engineering 72 (2006): 39-55.
15. Brosnan T and Sun DW. “Improving quality inspection offood products by computer vision—a review”. Journal of Food Engineering 61 (2004): 3-16.
16. Sun DW and Brosnan T. “Pizza quality evaluation usingcomputer vision—Part 1 Pizza base and sauce spread”. Journal of Food Engineering 57 (2003): 81-89.
17. Wu J., et al. “Automatic detection of hardhats worn by construction personnel: A deep learning approach and benchmark dataset”. Automation in Construction 106 (2019): 102894.
18. Filatov N., et al. “Development of hard hat wearing monitoring system using deep neural networks with high ınference speed”. In 2020 International Russian Automation Conference (2020): 459-463.
19. C Jagadeeswari., et al. “Hard Hat Detection Using Deep Learning Techniques”. International Journal of Advanced Science and Technology29 (2020): 1292-1298.
20. Singh S., et al. “Face mask detection using YOLOv3 and faster R-CNN models: COVID-19 environment”. Multimedia Tools and Applications (2021): 1-16.
21. Loey M., et al. “A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic”. Measurement 167 (2021): 108288.
22. Militante SV and Dionisio N V. “Real-time facemask recognition with alarm system using deep learning”. In 2020 11th IEEE Control and System Graduate Research Colloquium (ICSGRC) (2020): 106-110.
23. Fang Q., et al. “A deep learning-based method for detecting non-certified work on construction sites”. Advanced Engineering Informatics35 (2018): 56-68.
24. Son H., et al. “Detection of construction workers under varying poses and changing background in image sequences via very deep residual networks”. Automation in Construction99 (2019): 27-38.
25. Pradana R D W., et al. “MIdentification system of personal protective equipment using Convolutional Neural Network (CNN) Method”. In 2019 International Symposium on Electronics and Smart Devices (2019): 1-6.
26. Nath N D., et al. “Deep learning for site safety: Real-time detection of personal protective equipments”. Automation in Construction 112 (2020): 103085.
27. Zhafran F., et al. “Computer vision system based for personal protective equipment detection, by using convolutional neural network”. International Electronics Symposium (2019): 516-521.
28. Chen H., et al. “Change detection in multisource VHR images via deep Siamese convolutional multiple-layers recurrent neural network”. Transactions on Geoscience and Remote Sensing 58 (2019): 2848-2864.
29. McMeekin T A., et al. “Information systems in food safety management”. International Journal of Food Microbiology 112 (2006): 181-194.
30. Michaels B., et al. “Prevention of food worker transmission of foodborne pathogens: risk assessment and evaluation of effective hygiene intervention strategies”. Food Service Technology 4 (2004): 31-49.
31. Topoyan M. “Analysis of hazard analysis and critical control points (HACCP) and ISO:9001:2000 quality management system relations in food industry”. Dokuz Eylül University Institute of Social Sciences, Master's thesis (2003): 153.
32. Kurt, E. “Investigation of work accidents in dried fruits factory”. OHS Academy 2 (2019): 88-118.
33. Utlu Z and Yılmaz G. “Occupational health and safety impact of technological developments in the food manufacturing sector”. Anadolu Bil Vocational School Journal 44 (2019): 1-6.

Citation

Citation: Ozdemir, Y., Cam O.N., Kayahan S. “Artificial Intelligence in Food Safety Control". Acta Scientific Computer Sciences 3.7 (2021): 23-28.

Copyright

Copyright: © 2021 Yasin Ozdemir., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.