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A Systematic Review of Artificial Intelligence and Machine Learning Techniques for Microplastic Detection and Analysis
Summary
Scientists are increasingly using AI to detect microplastics faster than traditional lab methods like microscopes and chemical testing, according to a review of 113 studies. While AI shows promise, sometimes spotting these tiny plastic particles with over 90% accuracy, the field still lacks standardized testing methods, making it hard to compare results across studies or trust the technology is ready for widespread use. This matters because better, faster microplastic detection could eventually help us understand how much of this pollutant we're exposed to in our food, water, and air.
Microplastics are a pervasive contaminant of aquatic, terrestrial, and atmospheric systems, with accumulating evidence of ecological harm and human exposure. Conventional workflows, manual microscopy, FTIR, and Raman spectroscopy, are labor-intensive and operator-dependent, motivating artificial intelligence (AI) and machine learning (ML) as scalable alternatives. Following PRISMA guidelines, five databases were searched; 936 records were screened and 113 primary studies met the eligibility criteria, analyzed through dual-reviewer extraction across seven dimensions. Contrary to the assumption that deep learning dominates, classical and chemometric estimators (46.0% of studies) were at least as prevalent as deep learning (36.3%), with support-vector machines the single most used family (35.4%). Method choice tracked input modality rather than recency: chemometric classifiers such as SVM and PLS dominate spectroscopic data (FTIR, Raman; 53.1% of studies), whereas deep learning concentrates in the image-based minority, where representation learning outperforms manual feature engineering. Detection/identification remained the principal task (65.5%), although 19.5% addressed predictive modeling of sorption, toxicity, distribution, and remediation. Characterization coverage was uneven: origin and polymer type were reported in 88.5% and 72.6% of studies, but size and shape in only 50.4% and 28.3%. Reported accuracies, often exceeding 90%, derive from heterogeneous, non-comparable datasets. Future progress depends on open benchmarks, standardized reporting, and explainable methods.