Identification of Polymers with a Small Data Set of Mid-infrared Spectra: A Comparison between Machine Learning and Deep Learning Models

• Published in: Environmental science & technology letters Vol. 10; no. 11; pp. 1030 - 1035
• Main Authors: Tian, Xin, Beén, Frederic, Sun, Yiqun, van Thienen, Peter, Bäuerlein, Patrick S.
• Format: Journal Article
• Language: English
• Published: American Chemical Society 14.11.2023
• Subjects: Data Science; deep learning; LDIR; ensemble-supervised learning; microplastics; polymers; data science
• ISSN: 2328-8930; 2328-8930
• DOI: 10.1021/acs.estlett.2c00949

Identifying environmental polymers and microplastics is crucial for the scientific world, environmental agencies, and water authorities to estimate their environmental impact and increase efforts to decrease emissions. On the basis of different spectroscopy techniques, e.g., laser-directed infrared imaging and Raman spectroscopy, polymers can be observed and represented as spectroscopic signals. The latter can be further analyzed and classified by data science, in particular, machine learning (ML). Past studies applied a variety of ML models to identify polymers from small or large data sets. However, a comprehensive comparison of multiple models across different data set sizes is still needed, which is presented in this study. Furthermore, we also provide a practical data augmentation technique to generate synthetic samples when only a limited number of samples are available. Our results show that the ensemble ML model, compared to neural network models, takes the least training time to achieve the best performance, i.e., a classification accuracy of 99.5%. This study provides a generic framework for selecting ML models and boosting model performance to accurately identify polymers.