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In recent years, the analysis of chromatographic and metabolomic data has become increasingly demanding due to the complexity and high dimensionality of datasets produced by modern analytical platforms such as LC–MS and GC-MS. Researchers frequently encounter challenges related to dimensionality of data, co-eluting peaks, retention time shifts, instrumental variability, feature extraction, and the presence of false or redundant features, emphasizing the importance of a solid understanding of chemometric strategies for reliable and reproducible data interpretation.
The School on Chemometric Strategies for Chromatographic Signal in Targeted and Untargeted Analysis is designed to provide participants with both theoretical knowledge and practical competence in managing and interpreting complex analytical data.
During the school, participants will explore a range of classical and modern multivariate methods, from data preprocessing and normalization to advanced signal resolution and component analysis. Particular attention will be given to approaches such as MCR-ALS (Multivariate Curve Resolution – Alternating Least Squares), ASCA (ANOVA–Simultaneous Component Analysis), and PARAFAC (Parallel Factor Analysis), which represent powerful tools for resolving overlapping signals, modeling structured experimental designs, and interpreting multiway data.
Through lectures, guided examples, and interactive data challenges, attendees will have the opportunity to apply these chemometric strategies to real chromatographic and metabolomic datasets, comparing targeted and untargeted approaches and evaluating their performance in realistic analytical contexts.
By the end of the school, participants will have developed a comprehensive understanding of how to handle chromatographic signals from raw data to meaningful interpretation, gaining the skills required to ensure data quality, reproducibility, and robust marker discovery in modern metabolomics.