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Microplastic Contamination and Detection in Food Systems: A Review of Machine Learning, Traditional Methods, and Other Relevant Factors
Summary
This review examines traditional and machine learning approaches to detecting and classifying microplastics in food systems, highlighting the limitations of FTIR, Raman spectroscopy, and SEM in complex food matrices. It identifies AI-assisted methods as promising tools for improving detection accuracy and throughput.
Microplastics (MPs) are widespread contaminants in food, beverages, and drinking water, raising concerns over potential health risks. Accurate and standardized detection a significant analytical chemistry challenge due to complex food matrices and lim
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