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Updated: Aug 23, 2025

Protocol for Microplastics Sampling on the Sea Surface and Sample Analysis

Protocol for Microplastics Sampling on the Sea Surface and Sample Analysis

Published on: December 16, 2016

Identifying plastics with photoluminescence spectroscopy and machine learning.

Benjamin Lotter¹, Srumika Konde¹, Johnny Nguyen¹

¹Department of Physics, Philipps-Universität Marburg, Marburg, Germany.

Scientific Reports

|November 6, 2022

Summary

This study introduces a novel method using photoluminescence spectroscopy and machine learning to quantitatively analyze global plastic distribution and composition. This approach aids in assessing plastic litter

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Area of Science:

Environmental Science
Analytical Chemistry
Data Science

Background:

Accurate quantification of global plastic distribution is essential for environmental impact assessment and developing mitigation strategies.
Existing methods for characterizing plastic amount and composition lack worldwide accessibility and sensitivity to diverse plastic types.

Purpose of the Study:

To develop a globally accessible and sensitive experimental approach for characterizing plastic distribution and composition.
To establish an efficient analysis pipeline for extracting relevant parameters from extensive experimental data.

Main Methods:

Utilized photoluminescence spectroscopy for experimental data collection.
Employed a machine learning-based theoretical analysis pipeline, including classifiers and dimensional reduction algorithms.
Focused on unsupervised learning techniques for enhanced data robustness.

Main Results:

Demonstrated the feasibility of combining photoluminescence spectroscopy and machine learning for plastic analysis.
Showcased the ability of specific machine learning combinations to identify material properties from spectroscopic data.
Validated the robustness of the unsupervised learning approach against input data variations.

Conclusions:

The integrated approach of photoluminescence spectroscopy and machine learning offers a viable solution for global plastic characterization.
This method provides a robust and accessible means to understand worldwide plastic distribution and composition, informing environmental policy and action.