Machine learning-driven microplastics identification using ensemble stacking with Extra Tree meta-models from FTIR data

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12520%2F25%3A43910036" target="_blank" >RIV/60076658:12520/25:43910036 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1016/j.jece.2025.118315" target="_blank" >https://doi.org/10.1016/j.jece.2025.118315</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jece.2025.118315" target="_blank" >10.1016/j.jece.2025.118315</a>

Result language

angličtina

Original language name

Machine learning-driven microplastics identification using ensemble stacking with Extra Tree meta-models from FTIR data

Original language description

Microplastics (MPs) have become a major global environmental issue in recent decades due to their widespread presence in oceans, bioavailability, and ability to carry toxic chemicals. Attenuated Total Reflectance Fourier-Transform Infrared (ATR-FTIR) spectroscopy is widely used for MPs identification and analysis, but it faces challenges such as class imbalance, spectral similarities, and fouling, which hinder data accuracy and classification. This study proposes MLStackXT, a stacking-based machine learning (ML) model designed to enhance MPs classification by addressing these challenges using FTIR spectral datasets. The model was trained and validated using the Kedzierski and Jung dataset. It demonstrated superior performance compared to previous ML and deep learning (DL) approaches, including support vector machines (SVMs), ensemble learning, and deep neural networks (DNNs). On the Kedzierski dataset, MLStackXT achieved 95.85% accuracy, with a kappa score of 94.96%, F1-score of 95.73%, recall of 95.85%, and precision of 96.10%. Similarly, on the Jung dataset, the model attained 95.00% accuracy, a kappa score of 91.28%, an F1-score of 94.78%, a recall of 95.00%, and a precision of 94.81%. Confusion matrix analysis confirmed a significant reduction in misclassification, achieving 100% accuracy in 8 out of 12 MPs categories (Kedzierski) and over 97% accuracy in 4 out of 5 MPs categories (Jung). Model optimization via Optuna and interpretability analysis using SHAP highlighted the influence of principal component analysis (PCA) and key spectral features on classification performance. In addition, MLStackXT outperformed various stacking configurations, demonstrating its robustness and effectiveness for MPs detection.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20701 - Environmental and geological engineering, geotechnics

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of Environmental Chemical Engineering

ISSN

2213-2929

e-ISSN

2213-3437

Volume of the periodical

13

Issue of the periodical within the volume

5

Country of publishing house

GB - UNITED KINGDOM

Number of pages

13

Pages from-to

nestránkováno

UT code for WoS article

001565151200021

EID of the result in the Scopus database

2-s2.0-105015139475