Machine Learning for Real-Time Detection of Microbial and Chemical Contaminants in Food

Abstract

Ensuring food safety requires accurate, rapid, and scalable methods to detect microbial and chemical contaminants in various food products. Traditional laboratory-based testing methods, although accurate, are often slow, resource-intensive, and unsuitable for real-time decision-making in production environments. Recent advancements in machine learning (ML) offer new opportunities to automate and accelerate contaminant detection. This paper proposes a machine learning-driven framework that leverages data from portable spectroscopy devices, biosensors, and smart imaging systems to detect bacterial contamination (e.g., E. coli, Salmonella) and chemical hazards (e.g., pesticides, heavy metals) in real-time. The framework includes supervised learning models such as support vector machines (SVM), convolutional neural networks (CNN), and gradient boosting classifiers trained on high-dimensional spectral and biochemical datasets. Results demonstrate high classification accuracy (>95%) with reduced false positives, making the system suitable for deployment in food processing and inspection workflows. This research underscores the value of ML in enhancing food safety monitoring and provides a foundation for future AI-integrated quality assurance systems.