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Related Experiment Video

Updated: Mar 20, 2026

Protocol for Microplastics Sampling on the Sea Surface and Sample Analysis
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Unveiling uncertainty in microplastic quantification: Artificial intelligence integrated Raman analysis.

Min Young Oh1, In-Chun Jeong2, Kihyun Kim1

  • 1Department of Chemistry, College of Natural Sciences, Chung-Ang University, Seoul 06974, Republic of Korea.

Journal of Hazardous Materials
|March 18, 2026
PubMed
Summary
This summary is machine-generated.

Accurate microplastic analysis is crucial. An AI-enhanced Raman spectroscopy method improves identification and quantifies sub-sampling biases, essential for reliable environmental monitoring and pollution mitigation.

Keywords:
Deep neural network modelEnvironmental microplastic monitoringMicroplasticsSub-sampling strategiesμ-Raman spectroscopy

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Area of Science:

  • Environmental Science
  • Analytical Chemistry
  • Spectroscopy

Background:

  • Microplastic (MP) pollution requires accurate analysis for effective mitigation.
  • Micro-Raman spectroscopy offers high resolution and specificity but faces limitations in speed and efficiency.
  • Sub-sampling strategies, while reducing analysis time, introduce quantification bias, potentially distorting environmental exposure assessments.

Purpose of the Study:

  • To optimize Raman acquisition conditions and develop an AI model for enhanced microplastic identification and property analysis.
  • To quantitatively assess the uncertainties associated with sub-sampling strategies in microplastic analysis.
  • To establish a framework for accurate and efficient microplastic detection in environmental samples.

Main Methods:

  • Optimization of micro-Raman spectroscopy acquisition parameters.
  • Development and application of an Artificial Intelligence (AI) model for microplastic identification, spectral quality assessment, and size/morphology analysis.
  • Quantitative simulation using AI-integrated Raman mapping to evaluate sub-sampling uncertainties under varying distribution scenarios.

Main Results:

  • The AI model successfully identified microplastics overlooked by human inspection and distinguished low-quality spectra.
  • Quantitative simulations revealed that higher filter coverage (40-70%) is needed for heterogeneous microplastic distributions compared to homogeneous ones (3-35%) to achieve low error.
  • Frequent, small-block sampling was identified as an efficient strategy to reduce required filter coverage.

Conclusions:

  • AI-integrated micro-Raman spectroscopy significantly enhances the accuracy and efficiency of microplastic analysis.
  • Sub-sampling strategies introduce inherent uncertainties, particularly for heterogeneous environmental samples, necessitating careful consideration of filter coverage.
  • This study provides a validated framework for reliable microplastic quantification, crucial for informed environmental monitoring and pollution control efforts.