# Predicting Properties from Near-Infrared Spectra with Machine Learning for Improved Polyolefin Differentiation

## TL;DR

This paper uses machine learning with near-infrared spectra to better differentiate polyolefins for improved plastic recycling.

## Contribution

A novel ML approach that predicts polyolefin properties from NIR spectra, enhancing sorting accuracy and interpretability.

## Key findings

- Partial least squares regression provides high accuracy for predicting polyolefin properties from NIR spectra.
- Important wavenumbers for property prediction are linked to known CH3 vibrational absorption bands in polyolefins.
- The method confirms the relationship between polymer chain structure and properties using ML models.

## Abstract

As the scale and
variety of plastics produced continue
to grow,
plastics recycling will require innovative solutions. The industrial
state-of-the-art sorting technology, near-infrared (NIR) spectroscopy,
as currently used, cannot effectively differentiate polyolefins, the
single largest class of polymers by volume. Chemical similarity combined
with architectural diversity in polyolefins stymies subclass delineation,
such as differentiating low-density polyethylene from high-density
polyethylene, due to their spectral similarity and chemical overlap.
To address this challenge, we use machine learning (ML) to directly
predict density, crystallinity, and short-chain branching from NIR
spectra, enabling property-based sorting for more effective recycling.
After testing a variety of ML models, we find that partial least squares
regression provides high prediction accuracy with model simplicity.
Since the resulting model leverages the correlated intensities, we
develop a method to enhance interpretability by identifying the most
important wavenumbers for property prediction, which we then relate
to known polyolefin CH3 NIR vibrational absorption bands.
This approach provides a linkage between ML model predictions and
the underlying polyolefin chemistry and confirms that our models effectively
capture spectrum–structure–property relationships in
polyolefins, reinforcing the fundamental role of polymer chain structure
in determining properties. These findings significantly contribute
to the understanding of polyolefin differentiation using NIR spectroscopy,
which could inform future advancements in property-based sorting strategies
for plastic recycling efficiency.