From Spectra to Usable Biochemical Information: Data Fusion and Data Augmentation Strategies for More Explainable Machine Learning

Vibrational spectroscopy has become a powerful tool for studying complex chemical systems, particularly biological ones. However, it generates large empirical datasets composed of highly overlapped spectral bands, which require multivariate analysis through an ever-expanding range of machine learning approaches. The increasing sophistication of these models often makes it difficult to directly relate spectral features to meaningful biochemical information. As a result, many approaches remain “black box” models, limiting the physical interpretability of spectral bands and the reliability of the extracted chemical insight. Explainable AI offers a pathway to address this challenge by developing more transparent models that provide both robust diagnostic tools and usable biochemical information to address fundamental scientific questions.

Here we present two complementary strategies to enhance the interpretability of spectral machine learning models. First, data fusion is used to correlate spectral information from different techniques, improving the robustness and interpretability of spectral–biochemical relationships. Second, data augmentation enables the generation of in silico spectra, allowing virtual simulation and optimization of experimental conditions while producing large and diverse datasets that capture relevant variability. Together, these approaches contribute to more explainable, reliable, and data-efficient machine learning frameworks for vibrational spectroscopy.