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Related Experiment Video

Updated: May 3, 2026

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

Open Specy 1.0: Automated (Hyper)spectroscopy for Microplastics.

Win Cowger^1,2, Aleksandra Karapetrova², Clarissa Lincoln³

¹Moore Institute for Plastic Pollution Research, Long Beach, California 90815, United States.

Analytical Chemistry

|August 11, 2025

Summary

This summary is machine-generated.

Open Specy 1.0 automates microplastic spectral analysis, significantly reducing processing time. New algorithms and machine learning classifiers enhance efficiency and accuracy in identifying microplastic pollution.

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Multimodal Analysis of Microplastics in Drinking Water using a Silicon Nanomembrane Analysis Pipeline

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Area of Science:

Environmental Science
Analytical Chemistry
Spectroscopy

Background:

Microplastic spectral analysis is a critical but time-consuming step in pollution studies.
Traditional methods require extensive manual processing, limiting throughput.
Automated and hyperspectral imaging techniques offer potential for increased efficiency.

Purpose of the Study:

To introduce Open Specy 1.0, an updated software for automated microplastic spectral analysis.
To present new algorithms for automated processing, including smoothing and particle compression.
To evaluate the performance of machine learning classifiers and a comprehensive spectral library.

Main Methods:

Integration of automated processing algorithms (smoothing, particle compression) into Open Specy.
Development of two machine learning classifiers (logistic regression, k medoids) using a large spectral library (>40,000 spectra).
Evaluation of hyperspectral smoothing, particle identification, compression, and splitting configurations.

Main Results:

Open Specy 1.0 achieved combined recovery rates of 50-150% for particle counts, identities, and sizes with CV < 40%.
Hyperspectral smoothing improved recovery to 96% (CV = 38%) compared to non-smoothed controls (86% recovery, CV = 83%).
Spectral compression for particles was >3x faster with similar accuracy and reduced variability compared to pixel-by-pixel processing.

Conclusions:

Open Specy 1.0 significantly enhances the efficiency and accuracy of microplastic spectral analysis.
Automated techniques, particularly hyperspectral smoothing and spectral compression, are effective.
Further refinement of particle splitting algorithms and identification routines is needed to address challenges like false positives/negatives.