Spectral Model eXplainer: a chemically-grounded explainability framework for spectral-based machine learning models

ArXi:2605.02684v1 Announce Type: new Spectral-based machine learning models have been increasingly deployed in chemometrics and spectroscopy, where predictive accuracy is as important as explainability. Current employed eXplainable Artificial Intelligence (XAI) methods are largely adapted from tabular or generic multivariate domains, assigning relevance to isolated spectral variables rather than to the chemically meaningful spectral zones.