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Lifestyle·P Space·Evidence-backed problem·Published 2026-09-02

Portable Raman spectroscopy coupled with machine learning for rapid identification and source apportionment of plastic particles in aquaculture wastewater

Abstract: Aquaculture expansion has exacerbated microplastics (MPs) contamination in aquaculture water bodies. MPs readily adsorb heavy metals and organic pollutants to form composite contamination and accumulate through food chains, imposing ecological and human health risks. Conventional detection techniques including microscopic observation, FTIR and Py-GC-MS are limited by low identification accuracy, matrix interference, destructiveness and cumbersome procedures, which cannot support rapid large-scale monitoring. Ram…

TRV-2026-0961Peer-reviewedPermanent record — cite & verify
Portable Raman spectroscopy coupled with machine learning for rapid identification and source apportionment of plastic particles in aquaculture wastewater

Ultraviolet resonance Raman enhancements in the detection of explosives by Short, Billy Joe. Public domain

The quick read

Aquaculture expansion has exacerbated microplastics (MPs) contamination in aquaculture water bodies. MPs readily adsorb heavy metals and organic pollutants to form composite contamination and accumulate through food chains, imposing ecological and human health risks.

In this study, Raman spectroscopy was integrated with machine learning to establish a rapid plastic particle classification approach. Seven machine learning models were constructed and optimized by cross-validation, and further validated using spiked aquaculture wastewater samples.

Main points
  • Aquaculture expansion has exacerbated microplastics (MPs) contamination in aquaculture water bodies.
  • MPs readily adsorb heavy metals and organic pollutants to form composite contamination and accumulate through food chains, imposing ecological and human health risks.
  • Conventional detection techniques including microscopic observation, FTIR and Py-GC-MS are limited by low identification accuracy, matrix interference, destructiveness and cumbersome procedures, which cannot support rapid large-scale monitoring.
Problem

Portable Raman spectroscopy coupled with machine learning for rapid identification and source apportionment of plastic particles in aquaculture wastewater: MPs readily adsorb heavy metals and organic pollutants to form composite contamination and accumulate through food chains, imposing ecological and human health risks.

The rundown

Conventional detection techniques including microscopic observation, FTIR and Py-GC-MS are limited by low identification accuracy, matrix interference, destructiveness and cumbersome procedures, which cannot support rapid large-scale monitoring. Raman spectroscopy enables non-destructive detection with unique molecular fingerprinting, yet spectral overlap, background noise and subtle crystallinity differences among similar plastics hinder accurate manual classification.

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