Evaluating the effectiveness of AI-driven anomaly detection in food safety compliance monitoring
1 FSQ (Food Safety Quality) Analyst, SFC Global Supply Chain Inc (Schwan’s) – Florence, Kentucky, USA.
2 Department of Computer Science and Engineering, Texas A & M University, Texas, USA.
3 Department of Computer Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
Review
International Journal of Frontline Research in Life Science, 2025, 03(02), 001-009.
Article DOI: 10.56355/ijfrls.2025.3.2.0022
Publication history:
Received on 07 April 2025; revised on 28 June 2025; accepted on 30 June 2025
Abstract:
Food safety compliance is a critical matter of public health and regulatory oversight, which requires strict monitoring to protect against contamination, ensure quality, and maintain standards across the industry. Conventional food safety monitoring methods typically depend on manual inspections and rule-based systems, which can be labor-intensive, time-consuming, susceptible to human error, and ineffective in identifying complex or evolving anomalies. This research investigates the effectiveness of AI-driven anomaly detection systems in food safety compliance monitoring, focusing on their capacity to improve real-time monitoring, enhance detection accuracy, and risks of non-compliance. It evaluates the performance of AI-based anomaly detection vs. traditional monitoring approaches using key metrics including detection accuracy, response time, and false-positive rates. Additionally, it also discusses the challenges of adopting AI in food safety applications, such as data quality limitations, model interpretability and regulatory restrictions. Through an analysis of case study comparisons and industry observations, the findings highlight that AI-driven technologies such as machine learning, deep learning, and computer vision significantly enhance food safety compliance through automation of contamination detection, optimizing processing parameters, and supply chain traceability. Different applications of AI mentioned in this research study have demonstrated success in reducing food spoilage, improving shelf-life prediction and quality control, as evidenced by improved accuracy of contamination detection and compliance with regulations. Nevertheless, challenges remain in terms of data availability, model interpretability, regulatory restrictions, and the high cost of AI adoption. Through a thorough analysis, this research addresses the indicators and practical uses of AI automation in food safety compliance monitoring and its potential impact on existing food safety standards.
Keywords:
AI-Driven Anomaly Detection; Food Safety Compliance; Machine Learning in Food Safety; Regulatory Monitoring Systems
Full text article in PDF:
Copyright information:
Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0