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Microplastic Contamination and Detection in Food Systems: A Review of Machine Learning, Traditional Methods, and Other Relevant Factors
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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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Microplastic Contamination and Detection in Food Systems: A Review of Machine Learning, Traditional Methods, and Other Relevant Factors
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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.
Artificial intelligence-driven detection of microplastics in food: A comprehensive review of sources, health risks, detection techniques, and emerging artificial intelligence solutions
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This review compares traditional and advanced methods for detecting microplastics in food, finding that while older techniques provide basic information, newer technologies like infrared spectroscopy and mass spectrometry can identify much smaller particles with greater accuracy. The study highlights how artificial intelligence can significantly improve the speed and precision of microplastic detection in food products. As global plastic production continues to rise, better detection methods are essential for monitoring food safety and protecting human health.
A Critical Review on Analytical Techniques for Microplastics in Food Commodities
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Researchers critically surveyed the full suite of analytical techniques for detecting microplastics in food—from conventional microscopy, FTIR, and Raman spectroscopy to emerging AI-assisted imaging, microfluidic lab-on-chip, and biosensor platforms—concluding that while detection capabilities have advanced substantially, the absence of standardized protocols remains the central barrier to reliable food safety monitoring.
“Microplastic contamination and detection in food systems: a review of machine learning, traditional methods, and other relevant factors”
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This review paper summarizes existing research on detecting tiny plastic particles called microplastics in food and water. It compares older lab methods with newer machine learning and AI tools, noting challenges like limited data and inconsistent testing standards, while describing opportunities for faster, automated detection in the future.
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This review covers how machine learning combined with Raman spectroscopy can improve the detection and identification of microplastics in environmental samples. Traditional detection methods are slow and have limitations in resolution and particle size analysis, but AI algorithms can process spectral data more quickly and accurately. Better detection tools are essential for understanding the true scale of microplastic contamination in our water, food, and environment.
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