Academic Journal
A modular convolutional neural network framework for Raman spectra-based identification of environmental microplastics.
| Τίτλος: | A modular convolutional neural network framework for Raman spectra-based identification of environmental microplastics. |
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| Συγγραφείς: | Feng X; Department of Civil and Mineral Engineering, University of Toronto, 35 St. George St., Toronto ON M5S 1A4 Canada., Andrews RC; Department of Civil and Mineral Engineering, University of Toronto, 35 St. George St., Toronto ON M5S 1A4 Canada., Almuhtaram H; Department of Civil and Mineral Engineering, University of Toronto, 35 St. George St., Toronto ON M5S 1A4 Canada. Electronic address: husein.almuhtaram@utoronto.ca. |
| Πηγή: | Water research [Water Res] 2026 Oct 01; Vol. 304, pp. 126308. Date of Electronic Publication: 2026 Jun 15. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Pergamon Press Country of Publication: England NLM ID: 0105072 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2448 (Electronic) Linking ISSN: 00431354 NLM ISO Abbreviation: Water Res Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Oxford, Pergamon Press. |
| Ιατρικοί όροι (MeSH): | Spectrum Analysis, Raman* , Microplastics*, Convolutional Neural Networks ; Multilayer Perceptrons ; Neural Networks, Computer ; Environmental Monitoring |
| Περίληψη: | Raman spectroscopy is widely employed in microplastic (MP) research for polymer identification. However, when applied to spectra obtained from complex environmental samples, the conventional spectral interpretation approach based on Hit Quality Index (HQI) is limited with respect to sensitivity and precision. While manual interpretation by experts provides improved accuracy, it is time-consuming thus restricting its application. To address this challenge, a series of modular convolutional neural network (CNN) models which target seven different polymer types (originating from lakes, rivers, and drinking water) were optimized by incorporating approxiamtely 16,000 expert-verified Raman spectra. Optimized CNN models achieved high sensitivity (97%-100%) across all target polymers with precision exceeding 50% for most. It was determined that approximately 500-1500 unique spectra per polymer type were required for effective training of CNN models; oversampling via use of replication did not improve performance. Targeted inclusion of specific types of spectra was identified as a viable approach to reduce model bias and improve sensitivity and precision. When compared to multilayer perceptron (MLP) models, CNNs achieved higher sensitivity and precision when abundant training data was available, whereas MLPs were beneficial when training data was limited or imbalanced (between positive and negative). These results highlight the importance of evaluating models using spectra which include noise and non-target signals, in order to avoid overestimation of performance. A hybrid workflow which combined preliminary machine learning (ML) screening with expert verification was found to substantially reduce manual workload while maintaining data quality. The modular ML-based framework developed in this study provides a flexible, scalable, and robust strategy for identification of MPs using Raman spectra, offering a pathway toward high-throughput analysis of microplastics in complex environmental matrices. (Copyright © 2026 The Author(s). Published by Elsevier Ltd.. All rights reserved.) |
| Competing Interests: | Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Contributed Indexing: | Keywords: CNN; Human labelled spectra; Machine learning; Polymer identification; Raman spectroscopy |
| Substance Nomenclature: | 0 (Microplastics) |
| Entry Date(s): | Date Created: 20260625 Date Completed: 20260815 Latest Revision: 20260815 |
| Update Code: | 20260815 |
| DOI: | 10.1016/j.watres.2026.126308 |
| PMID: | 42349162 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1879-2448 |
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| DOI: | 10.1016/j.watres.2026.126308 |