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.