From Data to Decision: A Critical Review of Spectral and Machine Learning Methods for Food Analysis

The integration of spectroscopic techniques with machine learning offers a powerful pathway from spectral data to reliable food safety decisions. This review provides a critical, decision-oriented examination of this approach focusing on food safety analysis. We outline the principles of major spectroscopic methods and key machine learning models. Unlike conventional reviews, we introduce a comparative framework and a novel decision flowchart to guide algorithm selection based on data characteristics and task requirements. We synthesize recent advances across three tasks, including adulterant identification, qualitative classification, and quantitative detection. Finally, we address the persistent barriers to industrial deployment and propose pathways toward practical solutions.