# Raman Spectroscopic Classification of Polyethylene Glycol Samples of Varying Molecular Weights Using Machine Learning

## TL;DR

This study shows that Raman spectroscopy combined with machine learning can distinguish PEG samples of different molecular weights.

## Contribution

The novel contribution is using Raman spectroscopy and machine learning to classify PEG molecular weights with high accuracy.

## Key findings

- A Support Vector Machine classifier achieved 93.4% accuracy in cross-validation and 72.6% on an independent test set.
- Discriminative information in Raman spectra comes mainly from line-shape and shoulder regions, not peak centers.
- Raman spectroscopy provides a reproducible, non-destructive method for differentiating PEG molecular weights.

## Abstract

Polyethylene glycol (PEG) is a widely used water-soluble polymer (WSP) whose properties such as crystallinity depend on molecular weight. This study explores whether Raman spectroscopy, combined with supervised machine learning, can differentiate PEG samples of defined molecular weights within the investigated molecular weight range. Eight PEG materials with molecular weights ranging from 1000 to 35,000 g/mol were analyzed by confocal Raman microscopy under standardized conditions. A Support Vector Machine (SVM) classifier achieved 93.4% accuracy in five-fold cross-validation and 72.6% on an independent test set, confirming that molecular-weight-dependent vibrational signatures are present in the Raman spectra. Principal component analysis followed by linear discriminant analysis (PCA–LDA) models supported these findings, revealing that discriminative information arises mainly from line-shape and shoulder regions rather than from peak centers, consistent with gradual increases in conformational order. Although sample morphology and drying behavior introduce variability, the results demonstrate that Raman spectroscopy provides a reproducible, non-destructive means of distinguishing between PEG samples of different molecular weights. The established workflow provides a foundation for future quantitative evaluations of spectral trends, cross-polymer generalization, and adaptation to variable measurement conditions to enhance applicability in analytical and industrial contexts.