Bridging the Gap: Spectroscopy, Machine Learning, and the Path to Reliable Food Safety Analysis

The integration of spectroscopic techniques with machine learning has emerged as a powerful analytical paradigm for food safety analysis. However, the rapid expansion of this field has created a new challenge. Researchers and practitioners lack systematic guidance on selecting the most appropriate combination for a given problem. This review provides a critical, decision-oriented examination of this interdisciplinary field. We first establish applicability profiles for major spectroscopic techniques and functional groupings for common machine learning algorithms. We then propose a four-dimensional decision framework that guides method selection based on analytical goals, data characteristics, sample matrix, and application constraints. The utility of this framework is validated through three comparative case studies drawn from the literature. Beyond method selection, we identify three core bottlenecks that impede industrial deployment. These are interpretability, generalization, and deployment efficiency. We propose concrete solutions including explainable machine learning techniques, domain adaptation with calibration transfer, and model lightweighting strategies. We emphasize that these bottlenecks are interconnected, and finding the right balance requires careful consideration of application-specific priorities. This review aims to equip researchers and practitioners with a practical, theoretically grounded guide for navigating the complex landscape of spectroscopy and machine learning in food safety analysis, ultimately contributing to safer food supply chains and better public health protection.