Explainable Deep Learning and Targeted Spectral Augmentation for Mid-Infrared Classification of Additive-Containing Polymers

), a mineral filler, were extruded using a melt-mixer and incorporated as "chemistry-informed" training data. This targeted experimental augmentation improved the prediction accuracy of standard machine learning classifiers, including random forest, k-nearest neighbors, support vector machine, and logistic regression, from 79.4% to 86.6% without altering the training, validation, or testing parameters. Overall, this work establishes an integrated workflow that combines explainable deep learning, low-dimensional embedding, and targeted experimental data acquisition to improve MIR-based classification of additive-containing polymers. More broadly, the approach provides a practical foundation for autonomous material classification technologies designed for small, chemically heterogeneous spectral data sets.