A Machine Learning Approach for Efficiently Predicting Polymer Aging from UV-Vis Spectra

This research has introduced an innovative approach that proficiently forecasts the alterations in ultraviolet-visible spectroscopy (UV-Vis) of polymer solutions during the aging effect. This method combines readily accessible feature descriptors with classical machine learning (ML) algorithms. Traditional spectral measurements, while precise in analyzing physical properties, are limited by their cost and efficiency. Therefore, this paper introduces a method that utilizes wavelength and the blue (B), green (G), and red (R) color values of the solutions as input features. We employed seven different ML models to train on these features with 10-fold cross-validation to ensure the reliability and generalizability of our results. After comparative analysis, all of the models performed excellently. Among them, the ExtraTree model demonstrated particularly high precision and excellent predictive ability on the testing set, with a Pearson correlation coefficient (r) of 0.9859 and a mean absolute error (MAE) of 0.0457. This study offers a practical solution for the rapid and cost-effective evaluation of polymer solutions' aging effect.