AI and chemometrics are transforming spectroscopy into an intelligent analytical system, enhancing accuracy and interpretability across diverse applications. Innovations in explainable AI, generative modeling, and multimodal deep learning are key to advancing spectroscopic analyses. AI platforms. By Marie Freebody Developments in integrated laser technology and improvements in basic optics, shrinking electronics, and the personalization of computing power are converging in the modern spectroscopy workstation. In combination, these factors are broadening accessibility and cross-industry. The rapid advent of machine learning (ML) and artificial intelligence (AI) has catalyzed major transformations in chemistry, yet the application of these methods to spectroscopic and spectrometric data, referred to as Spectroscopy Machine Learning (SpectraML), remains relatively underexplored. Traditional chemometric approaches often face limitations when dealing with high-dimensional, nonlinear, and noisy spectral data.
