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Field readiness of Raman-AI pipelines for microplastic analysis across environmental matrices
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Scientists are increasingly using laser-based tools paired with AI to detect microplastics in water, food, and other samples, but this review of 61 studies finds these methods are mostly tested in controlled lab settings, not the messy real world. Key checks that would prove the technology works reliably outside the lab (like testing on new equipment or unfamiliar samples) are still rare, meaning the tech isn't quite ready to be trusted for widespread, real-world microplastic monitoring yet. This matters because before we can confidently track how much plastic contamination is in our water and food, the detection tools themselves need to prove they're consistently accur
Microplastic monitoring increasingly depends on workflows that can inform decisions beyond controlled laboratory datasets. Raman spectroscopy coupled with AI/ML or chemometrics can assist polymer identification, but high benchmark accuracy does not show whether a workflow remains reliable across matrices, instruments, preprocessing choices, and field use. This protocol-guided critical review evaluates the evidence needed before Raman-AI microplastic analysis can be considered field-ready. A Scopus search retrieved 151 records and retained 61 studies, coded by sample matrix, Raman modality, acquisition metadata, preprocessing, model family, analytical task, validation design, reporting assets, and deployment claim. The corpus is recent and classification-led. In all, 43 studies (70.5%) were published in 2023-2025; classification/identification accounted for 42/61 studies (68.9%); water-related or controlled/laboratory matrices dominated. Micro-Raman was the main backbone (36/61, 59.0%), followed by SERS-Raman (10/61, 16.4%), whereas quantification/regression (6/61, 9.8%) and imaging/mapping (3/61, 4.9%) remained limited. Reporting gaps were substantial. Spectral resolution appeared in 20/61 studies, normalization/scaling in 33/61, cosmic-ray removal in 16/61, leakage-control discussion in 9/61, uncertainty analysis in 6/61, interpretability in 4/61, and data/code availability in 8/61. We present a readiness-oriented framework that evaluates the full Raman-AI workflow, with classifier performance as one component. The MCDA-informed synthesis points to open benchmarks with code, grouped and external validation, uncertainty-aware outputs, peak-level interpretability, traceable preprocessing, and cross-matrix/cross-instrument testing. Until these elements are routine, field-readiness claims should remain conditional.
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From laboratory to field: A review of portable Raman spectroscopy for real-time microplastic monitoring in aquatic environments
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Tiny plastic particles called microplastics are showing up in water everywhere, but testing for them is slow and usually requires bringing samples back to a lab, which can even accidentally introduce more plastic contamination during handling. This review looked at portable devices that use light (laser) technology to detect microplastics on the spot, finding that while promising, most "portable" tools still need lab-like prep steps and aren't yet truly ready for quick, contamination-free field testing. Faster, more reliable on-site detection would help scientists track plastic pollution in the water we drink and the fish we eat, giving us better information about our exposure to these poll
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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.
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Tiny plastic particles are showing up in our food, but scientists still don't have great tools to reliably detect and measure them—making it hard to know exactly how much we're consuming or how worried we should be. This review rounds up promising new detection technologies, like advanced laser-based imaging and AI-powered analysis, that could make spotting these particles faster and more accurate. The bigger takeaway: before we can properly assess the health risks of microplastics in food, researchers need to agree on standardized testing methods—work that's still very much in progress.
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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.
A Critical Review on Artificial Intelligence—Based Microplastics Imaging Technology: Recent Advances, Hot-Spots and Challenges
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Researchers reviewed the use of artificial intelligence and machine learning techniques for detecting and identifying microplastics in environmental samples. The study found that AI-based imaging tools can significantly speed up analysis and improve accuracy compared to traditional manual methods. However, challenges remain around standardizing datasets and making these tools accessible for routine environmental monitoring.
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