Citation:

Naga Ramesh Palakurti, 2022. "AI Applications in Food Safety and Quality Control" ESP Journal of Engineering & Technology Advancements  2(3): 48-61.

Abstract:

Today’s food industry across the world is facing never-ending difficulties in meeting customers’ expectations of safe and quality food. Such of them include issues to do with contamination, adulteration, and ensuring quality standards of the products when manufactured in large quantities and distributed to different areas. This has called for new solutions, which have come in the form of Artificial Intelligence (AI), which offers solutions to challenges in food safety and quality control in detection, monitoring and management. The present deployment of AI in this field will also be discussed in this paper by virtue of machine learning algorithms, computer vision, industrial robots, and the IoT. With the help of AI, the safety and quality of food products can be improved as well as the food industry can obtain more accurate and real-time data for analysis, maintenance, and risk assessment. The application of AI systems in food safety and quality assurance provides not only reliability and decreased chances of human mistakes but efficiency as well. This paper aims to systematically summarize the current research achievements, methods, and application practices of AI in food safety and quality control. In effect, this paper presents a qualitative case analysis and research evidence that demonstrate the opportunities and risks arising from the implementation of AI technologies in the food sector. The analysis goes further considering the future trends and possible advancements oriented to creating a clear and prospective route for the researchers and specialists in the spheres of AI application for food safety and quality control.

References:

[1] Food Safety, World Health Organization, 2020. https://www.who.int/health-topics/food-safety#tab=tab_1

[2] Embracing the future of AI in the food industry, CAS Science Team, 2024. https://www.cas.org/resources/cas-insights/digital-rd/embracing-future-ai-food-industry#:~:text=The%20application%20of%20AI%20in,and%20optimize%20supply%20chain%20management.

[3] Sonwani, E., Bansal, U., Alroobaea, R., Baqasah, A. M., & Hedabou, M. (2022). An artificial intelligence approach toward food spoilage detection and analysis. Frontiers in Public Health, 9, 2022. https://doi.org/10.3389/fpubh.2021.816226

[4] The Impact of AI in Food Safety Management Systems, aiola, 2023. https://aiola.com/blog/ai-in-food-safety-management/

[5] Artificial intelligence (AI) for food safety, 2024. https://blog.ipleaders.in/artificial-intelligence-ai-for-food-safety/

[6] Sandberg, Marianne, et al. “Applications of computer vision systems for meat safety assurance in abattoirs: A systematic review.” Food Control (2023): 109768.

[7] Advancements of Machine Learning and Artificial Intelligence in Food Safety, Science Direct, https://www.sciencedirect.com/special-issue/10NJ1WFCJWL

[8] C. Qian et al., How Can AI Help Improve Food Safety? Annual Review Of Food Science And Technology, https://doi.org/10.1146/annurev-food-060721-013815

[9] How Machine Vision is Revolutionizing Food Industry? Visionify, 2022. https://visionify.ai/how-machine-vision-is-revolutionizing-food-industry/

[10] Mavani NR, Ali JM, Othman S, Hussain MA, Hashim H, Rahman NA. Application of Artificial Intelligence in Food Industry—a Guideline. Food Eng Rev. 2022;14(1):134–75. doi: 10.1007/s12393-021-09290-z. Epub 2021 Aug 9. PMCID: PMC8350558.

[11] Ayyalasomayajula, Madan Mohan Tito, Srikrishna Ayyalasomayajula, and Sailaja Ayyalasomayajula. "Efficient Dental X-Ray Bone Loss Classification: Ensemble Learning With Fine-Tuned VIT-G/14 And Coatnet-7 For Detecting Localized Vs. Generalized Depleted Alveolar Bone." Educational Administration: Theory and Practice 28.02 (2022).

[12] Doctor, Akbar. "Manufacturing of medical devices using artificial intelligence-based troubleshooters." Biomedical Signal and Image Processing with Artificial Intelligence. Cham: Springer International Publishing, 2023. 195-206.

[13] Chanthati, Sasibhushan Rao. (2022). A CENTRALIZED APPROACH TO REDUCING BURNOUTS IN THE IT INDUSTRY USING WORK PATTERN MONITORING USING ARTIFICIAL INTELLIGENC. International Journal on Soft Computing Artificial Intelligence and Applications. Sasibhushan Rao Chanthati. Volume-10, Issue-1, PP 64-69.[LINK]

[14] Ayyalasomayajula, M. M. T., Chintala, S., & Sailaja, A. (2019). A Cost-Effective Analysis of Machine Learning Workloads in Public Clouds: Is AutoML Always Worth Using? International Journal of Computer Science Trends and Technology (IJCST), 7(5), 107–115.

[15] Chanthati, Sasibhushan Rao. (2021). Second Version on A Centralized Approach to Reducing Burnouts in the IT industry Using Work Pattern Monitoring Using Artificial Intelligence using MongoDB Atlas and Python. 10.13140/RG.2.2.12232.74249.sasibhushan Rao Chanthati. https://rgdoi.net/10.13140/RG.2.2.12232.74249

[16] Empowering Rules Engines: AI and ML Enhancements in BRMS for Agile Business Strategies. (2022). International Journal of Sustainable Development through AI, ML and IoT, 1(2), 1-20. https://ijsdai.com/index.php/IJSDAI/article/view/36

[17] Aparna Bhat, “Comparison of Clustering Algorithms and Clustering Protocols in Heterogeneous Wireless Sensor Networks: A Survey,” 2014 INTERNATIONAL JOURNAL OF SCIENTIFIC PROGRESS AND RESEARCH (IJSPR)-ISSN : 2349-4689 Volume 04- NO.1, 2014.

Keywords:

Artificial Intelligence, Food Safety, Quality Control, Machine Learning, Computer Vision, IoT, Predictive Maintenance, Contamination Detection.