Sorting Polyolefins with Near-Infrared Spectroscopy: Identification of optimal data analysis pipelines and machine learning classifiers

Published

October 15, 2024

Author(s)

Bradley Sutliff, Peter Beaucage, Debra Audus, Sara Orski, Tyler Martin

Abstract

Polyolefins (POs) are the largest class of polymers produced worldwide. Despite the intrinsic chemical similarities within this class of polymers, they are often physically incompatible. This combination presents a significant hurdle for high-throughput recycling systems that strive to sort various types of plastics from one another. Some research has been done to show that near-infrared spectroscopy (NIR) can sort POs from other plastics, but they generally fall short of sorting POs from one another. In this work, we enhance NIR spectroscopy-based sortation by screening over 12 000 machine-learning pipelines to enable sorting of PO species beyond what is possible using current NIR databases. These pipelines include a series of scattering corrections, filtering and differentiation, data scaling, dimensionality reduction, and machine learning classifiers. Common scattering corrections and preprocessing steps include scatter correction, linear detrending, and Savitzky-Golay filtering. Dimensionality reduction techniques such as principal component analysis (PCA), functional principal component analysis (fPCA) and uniform manifold approximation and projection (UMAP) were also investigated for classification enhancements. This analysis of preprocessing steps and classification algorithm combinations identified multiple data pipelines capable of successfully sorting PO materials with over 95 % accuracy. Through rigorous testing, this study provides recommendations for consistently applying preprocessing and classification techniques without over-complicating the data analysis. This work also provides a set of preprocessing steps, a chosen classifier, and tuned hyperparameters that may be useful for benchmarking new models and datasets. Finally, the approach outlined here is ready to be applied by the developers of materials sortation equipment so that we can improve the value and purity of recycled plastic waste streams.

Citation

Digital Discovery

Pub Type

Journals

Download Paper

https://doi.org/10.1039/D4DD00235K

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Keywords

machine learning, classification, circular economy, polyolefins, recycling

Citation

Sutliff, B. , Beaucage, P. , Audus, D. , Orski, S. and Martin, T. (2024), Sorting Polyolefins with Near-Infrared Spectroscopy: Identification of optimal data analysis pipelines and machine learning classifiers, Digital Discovery, [online], https://doi.org/10.1039/D4DD00235K, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=958208 (Accessed August 24, 2025)