ISSN :2582-9793

Machine Learning of polymer types from the spectral signature of Raman spectroscopy microplastics data

Original Research (Published On: 15-Feb-2023 )
Machine Learning of polymer types from the spectral signature of Raman spectroscopy microplastics data
DOI : 10.54364/AAIML.2023.1144

Sheela Ramanna, Danila Morozovskii, Sam Swanson and Jennifer Bruneau

Adv. Artif. Intell. Mach. Learn., 3 (1):647-668

Sheela Ramanna : University of Winnipeg

Danila Morozovskii : University of Winnipeg

Sam Swanson : Compound Connect

Jennifer Bruneau : Compound Connect

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DOI: 10.54364/AAIML.2023.1144

Article History: Received on: 13-Jan-23, Accepted on: 06-Feb-23, Published on: 15-Feb-23

Corresponding Author: Sheela Ramanna

Email: s.ramanna@uwinnipeg.ca

Citation: Sheela Ramanna (2023). Machine Learning of polymer types from the spectral signature of Raman spectroscopy microplastics data. Adv. Artif. Intell. Mach. Learn., 3 (1 ):647-668


Abstract

    

The tools and technology that are currently used to analyze chemical compound structures that identify polymer types in microplastics are not well-calibrated for environmentally weathered microplastics. Microplastics that have been degraded by environmental weathering factors can offer less analytic certainty than samples of microplastics that have not been exposed to weathering processes. Machine learning tools and techniques allow us to better calibrate the research tools for certainty in microplastics analysis. In this paper, we investigate whether the Raman shift values are distinct enough such that well studied machine learning (ML) algorithms can learn to identify polymer types using a relatively small amount of labeled input data when the samples have not been impacted by environmental degradation. Several ML models were trained on a well-known repository, Spectral Libraries of Plastic Particles (SLOPP), that contain Raman shift and intensity results for a range of plastic particles, then tested on environmentally aged plastic particles (SloPP-E) consisting of 22 polymer types. After extensive preprocessing and augmentation, the trained random forest model was then tested on the SloPP-E dataset resulting in an improvement in classification accuracy of 93.81% from 89%.

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