Demystifying the Black Box: Making Machine Learning Models Explainable in Spectroscopy

Machine learning models, particularly deep learning approaches, have achieved remarkable performance in spectral analysis, including atomic, near-infrared (NIR), Raman, and infrared spectroscopy. However, their interpretability remains a major challenge. This tutorial reviews key XAI techniques that provide insights into model predictions, allowing researchers to understand which spectral regions contribute to analytical outcomes. Detailed matrix-based formulations of these techniques are provided, along with illustrative examples. We conclude with a discussion of current limitations, best practices, and future research directions in explainable spectroscopy.