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Integrating Artificial Intelligence with Analytical Techniques for Enhanced Microplastics Analysis
Summary
This review demonstrates how integrating machine learning techniques—including support vector machines, random forests, and deep neural networks—with established spectroscopic and electrochemical methods substantially improves microplastic identification accuracy under challenging real-world conditions like noisy spectra and polymer degradation. AI-driven platforms combined with open-access spectral libraries represent a transformative advance toward scalable, real-time environmental monitoring of microplastics.
The widespread presence of microplastics (MPs) in environmental matrices poses significant ecological and human health risks due to their persistence and bioaccumulation. Conventional detection methods, including Raman, FTIR, and near-infrared spectroscopy, remain limited by spectral overlap, low resolution, and time-consuming analysis. This review highlights recent advances in microscopic, spectroscopic, and electrochemical approaches for MPs detection, with a particular focus on the integration of artificial intelligence (AI) and machine learning (ML). Techniques such as support vector machines, decision trees, random forests, principal component analysis, and deep neural networks have demonstrated improved classification accuracy under challenging conditions, including noisy spectra and polymer degradation. The role of open-access spectral libraries (e.g., SLoPP and SLoPP-E), data augmentation, and AI assisted imaging in enhancing reproducibility and resolution is emphasized. Additionally, ML driven electrochemical platforms enable sensor optimization, real-time data interpretation, and predictive modeling, underscoring AI's transformative potential for scalable MPs monitoring and risk assessment.