Detalle de: From LC-HRMS raw data to compound annotation: a chemometrics perspective for non-target MS analysis

Over the past decade, Liquid Chromatography–High Resolution Mass Spectrometry (LC-HRMS) non-target screening has emerged as one of the most powerful methods in analytical chemistry. However, it generates large volumes of data, which pose substantial computational and analytical challenges. Advanced computational methods are therefore required to filter the generated datasets and to extract the sought chemical information, primarily following two distinct approaches: feature-based approaches and chemometric component resolution. This review covers the following aspects from a component resolution perspective in LC-HRMS non-target analysis: (i) MS data acquisition and transformation to open data formats; (ii) MS data filtering and compression, with emphasis on the Region of Interest (ROI) approach; (iii) MS component resolution (sample componentization), with particular focus on Multivariate Curve Resolution (MCR)-based methods; and (iv) computational post-treatment methods aimed at semi-quantitative chromatographic analysis, statistical assessment, compound annotation and chemical structures prediction. This review also discusses key differences, advantages, and limitations of chemometric methods in comparison with other commonly used open-source tools feature-based approaches. Finally, this review provides a general overview of emerging trends and future developments in chemometric tools within the context of new mass spectrometric technologies, such as Trapped Ion Mobility Spectrometry.