63488 2025 eng lecture 0 -- -- -- High-resolution optical spectrometers generate spectra containing tens of thousands of data points per sample. Picometre-scale isotope shifts, matrix-induced line broadening, and strong inter-feature correlations render classical peak fitting unreliable. Current analytical challenges, therefore, require rigorous algorithms able to expose latent structure, quantify uncertainty, and remain chemically interpretable. The research program presented in this lecture integrates state-of-the-art spectrochemical instrumentation with mathematically disciplined data models. Principal Component Analysis and Partial Least Squares provide chemically meaningful latent variables, while gradient-boosted decision trees or deep neural networks (ANNDL) capture residual non-linearity without sacrificing traceability. All models are trained on isotope-enriched or synthetically generated spectra and distributed with full validation workflows. Chemisches Institutskolloquium, Humboldt-Universität zu Berlin Berlin, Germany 21.05.2025 1 publish false true Carlos Enrique Abad Andrade eng uncontrolled Spectrochemistry eng uncontrolled Isotopes eng uncontrolled Machine learning eng uncontrolled Battery eng uncontrolled Data fusion Analytische Chemie Angewandte Physik 1 Analytische Chemie; Referenzmaterialien 1.6 Anorganische Referenzmaterialien und Gasanalytik Energie Elektrische Energiespeicher und -umwandlung Chemie und Prozesstechnik Chemische Charakterisierung und Spurenanalytik Datei im Netzwerk der BAM verfügbar ("Closed Access") Präsentation