Pulse Brain · Growing Health Evidence Index
Peer-reviewed

Artificial intelligence-driven detection of microplastics in food: A comprehensive review of sources, health risks, detection techniques, and emerging artificial intelligence solutions

Himani Rawat, Ashish Gaur, Narpinder Singh, Manickam Selvaraj, Arun Karnwal, Gaurav Pant, Tabarak Malik

Food Chemistry X · 2025

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Summary

Microplastic contamination in food is an escalating concern due to associated environmental and health risks, with a rising global plastic production projected to exceed 2.1 billion tons annually by 2060. This makes it essential to have effective detection and identification of microplastics for determining environmental risk and secure food safety. This study is an effort to compare conventional methods (optical detection, thermo-analytical, hyperspectral imaging) with advanced techniques (Fourier transform infrared spectroscopy, pyrolysis-gas chromatography-mass spectrometry, Raman spectroscopy) in the detection of microplastics in food. While conventional methods are effective enough in providing qualitative insights, advanced techniques provide superior sensitivity and specificity for

Source type
Peer-reviewed study
DOI
10.1016/j.fochx.2025.102687
Catalogue ID
SNmoakvjlz-gg3t1y
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