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Improved Microplastic Identification From Simultaneously Collected Photothermal Infrared And Raman Spectra Using Multiview Conformal Prediction.

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Improved Microplastic Identification From Simultaneously Collected Photothermal Infrared And Raman Spectra Using Multiview Conformal Prediction.

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Related Experiment Video

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

Improved Microplastic Identification from Simultaneously Collected Photothermal Infrared and Raman Spectra Using

Rebecca L Parham¹, Eduardo Ochoa Rivera², Abbygail M Ayala¹

¹Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States.

ACS Measurement Science Au

|June 22, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces multiview conformal prediction (MVCP) to accurately identify microplastics (MPs) using combined photothermal infrared and Raman spectra. MVCP enhances identification confidence and reduces errors compared to single-view methods.

Keywords:

Raman spectroscopy microplastic microspectroscopy optical photothermal infrared particle identification statistical confidence targeted analysis

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Sampling and Identification of Microplastics in Groundwater

Sampling and Identification of Microplastics in Groundwater

Published on: November 7, 2025

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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

Sampling and Identification of Microplastics in Groundwater

Sampling and Identification of Microplastics in Groundwater

Published on: November 7, 2025

Area of Science:

Environmental Science
Analytical Chemistry
Spectroscopy

Background:

Microplastics (MPs) are globally pervasive, necessitating accurate identification and quantification methods.
Current MP identification relies on database matching, often using arbitrary thresholds.
Existing methods lack statistically robust ways to integrate data from multiple spectral techniques.

Purpose of the Study:

To develop a statistically sound method for identifying microplastics using multiple spectral data.
To introduce multiview conformal prediction (MVCP) for enhanced MP identification confidence.
To compare the efficacy of MVCP against single-view spectral analysis methods.

Main Methods:

Implemented multiview conformal prediction (MVCP) integrating photothermal infrared (PTIR) and Raman spectra.
Developed multidimensional thresholds for MP identification with statistical confidence.
Applied MVCP to analyze an ambient particle sample containing microplastics.

Main Results:

MVCP returned an average of one chemical identity, closer to the ideal number than single-view methods.
MVCP demonstrated improved performance when spectral data from one technique was compromised.
An MVCP threshold of 73% confidence maximized correct MP identification (0.80 ± 0.07) and minimized misidentification (0.10 ± 0.06) in real-world samples.

Conclusions:

MVCP offers a statistically confident approach to microplastic identification by combining spectral data.
The method provides reliable identification assurance based on user-defined uncertainty.
This study highlights the benefits of integrating multiple spectral techniques with advanced data analysis for environmental monitoring.