Work

Explainable Artificial Intelligence for the Rapid Identification and Characterization of Ocean Microplastics

Year: 2025

Type: article

Abstract: Accurate identification of microplastic polymers in marine environments is essential for tracing pollution sources, understanding ecological impacts, and guiding mitigation strategies. This study presents a comprehensive, explainable-AI framework that uses Raman spectroscopy to classify pristine and weathered microplastics versus biological materials. Using a curated spectral library of 78 polymer specimens—including pristine, weathered, and biological materials—we benchmark seven supervised machine learning models (Decision Trees, Random Forest, k-Nearest Neighbours, Neural Networks, LightGBM, XGBoost and Support Vector Machines) without and with Principal Component Analysis for binary classification. Although k-Nearest Neighbours and Support Vector Machines achieved the highest single metric accuracy (82.5%), k NN also recorded the highest recall both with and without PCA, thereby offering the most balanced overall performance. To enhance interpretability, we employed SHapley Additive exPlanations, which revealed chemically meaningful spectral regions (notably near 700 cm−1 and 1080 cm−1) as critical to model predictions. Notably, models trained without Principal Component Analysis provided clearer feature attributions, suggesting improved interpretability in raw spectral space. This pipeline surpasses traditional spectral matching techniques and also delivers transparent insights into classification logic. Our findings can support scalable, real-time deployment of AI-based tools for oceanic microplastic monitoring and environmental policy development.

Source: Microplastics

DOI: 10.3390/microplastics4030051

Authors: Dimitris Kalatzis, Angeliki I. Katsafadou, Eleni I. Katsarou, Dimitrios C. Chatzopoulos, Yiannis Kiouvrekis

Institutions: University of Thessaly, University of Nicosia, University of Limassol

Language: English

Topic: Microplastics and Plastic Pollution

Subfield: Pollution

Field: Environmental Science

Domain: Physical Sciences

SDG: Life below water

Open Access status: gold

Estimated APC paid: $1,207