Domain-Adaptive Knowledge-Enhanced Learning for Generalized Food Hazard Identification

Abstract

The complexity of modern food safety monitoring systems demands sophisticated approaches that can effectively identify hazards across diverse food domains while adapting to evolving contamination patterns and analytical conditions. Traditional machine learning approaches often fail when confronted with domain shifts between different food categories, processing environments, or detection methodologies. This research presents a comprehensive domain-adaptive knowledge-enhanced learning framework specifically designed to address the fundamental challenges in generalized food hazard identification. Our approach builds upon extensive analysis of 114 machine learning studies in food safety applications, revealing critical patterns in algorithm selection and application domains that inform our architectural design decisions. The framework integrates domain-adversarial neural networks with gradient reversal mechanisms to learn domain-invariant feature representations while preserving hazard-discriminative information across multiple food domains. The core architecture employs a sophisticated three-component design consisting of a feature extractor that learns transferable representations, a label predictor optimized for hazard classification, and a domain classifier that enables adversarial training through gradient reversal techniques. Knowledge enhancement is achieved through integration of structured food safety expertise and ensemble learning approaches that combine multiple weak learners to achieve superior generalization performance. Comprehensive evaluation across biological hazards, chemical contaminants, and physical hazards demonstrates significant improvements over conventional approaches, with cross-domain accuracy gains of 14.8% and ensemble-enhanced performance achieving 91.3% accuracy across diverse food matrices. The framework successfully addresses the critical challenge of limited training data in emerging hazard detection scenarios, achieving 87.6% accuracy with minimal labeled examples through effective domain adaptation and knowledge transfer. Our approach provides interpretable predictions supported by domain expertise while maintaining computational efficiency suitable for real-time food safety monitoring applications. This work establishes a new paradigm for intelligent food safety systems that can adapt to evolving food environments and emerging contamination patterns without requiring extensive retraining.