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Related Experiment Video

Updated: Sep 11, 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

Enhancing microplastic classification through filter-interfered FTIR spectra using dimensionality reduction and deep

Aeint Shune Thar¹, Seksan Laitrakun¹, Pattara Somnuake¹

¹Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12120, Thailand.

Marine Pollution Bulletin

|August 17, 2025

Summary

This study introduces a new method combining dimensionality reduction and deep learning to accurately classify microplastics from filter-interfered FTIR spectra, improving environmental monitoring.

Keywords:

Convolutional neural networks (CNNs)Deep learning (DL)Dimensionality reduction (DR)Fourier-transform infrared (FTIR) spectroscopy Machine learning (ML)Microplastics Spectral classification

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Last Updated: Sep 11, 2025

Protocol for Microplastics Sampling on the Sea Surface and Sample Analysis

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Published on: December 16, 2016

Separation and Identification of Conventional Microplastics from Farmland Soils

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Sampling, Sorting, and Characterizing Microplastics in Aquatic Environments with High Suspended Sediment Loads and Large Floating Debris

Sampling, Sorting, and Characterizing Microplastics in Aquatic Environments with High Suspended Sediment Loads and Large Floating Debris

Published on: July 28, 2018

Area of Science:

Environmental Science
Analytical Chemistry
Data Science

Background:

Microplastic pollution in aquatic environments poses a significant threat.
Fourier-transform infrared (FTIR) spectroscopy is a key method for microplastic identification.
Filter-interfered FTIR spectra reduce classification accuracy due to sample size and filter interference.

Purpose of the Study:

To develop an enhanced framework for microplastic classification using filter-interfered FTIR spectra.
To improve accuracy and efficiency in identifying microplastic types in aquatic samples.

Main Methods:

A framework combining dimensionality reduction (DR) techniques with deep learning (DL) classification was proposed.
High-dimensional FTIR spectra were converted to low-dimensional representations using DR.
A one-dimensional convolutional neural network (CNN) based on LeNet5 architecture was used for classification.

Main Results:

Classification accuracies ranged from 96.64% to 98.83%, outperforming the baseline approach (94.95%).
The number of trainable parameters in the CNN model was reduced by over 98%.
Five different DR techniques were evaluated for their impact on classification performance.

Conclusions:

The proposed DR-DL framework effectively enhances microplastic classification from filter-interfered FTIR spectra.
This approach offers an efficient and accurate method for analyzing microplastics in aquatic environmental monitoring.
The study highlights the potential of combining DR and DL for complex spectral data analysis.