TY  - CONF
A1  - Abad Andrade, Carlos Enrique
T1  - Data-Driven Spectrochemical Methods for Elemental and Isotopic Analysis
N2  - 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.
T2  - Chemisches Institutskolloquium, Humboldt-Universität zu Berlin
CY  - Berlin, Germany
DA  - 21.05.2025
KW  - Spectrochemistry
KW  - Isotopes
KW  - Machine learning
KW  - Battery
KW  - Data fusion
PY  - 2025
AN  - OPUS4-63488
LA  - eng
AD  - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER  -