Multivariate curve resolution-based data fusion approaches applied in H NMR metabolomic analysis of healthy cohorts.

Background: Metabolomics plays a critical role in deciphering metabolic alterations within individuals, demanding the use of sophisticated analytical methodologies to navigate its intricate complexity. While many studies focus on single biofluid types, simultaneous analysis of multiple matrices enhances understanding of complex biological mechanisms. Consequently, the development of data fusion methods enabling multiblock analysis becomes essential for comprehensive insights into metabolic dynamics.

Fusing data of different orders for environmental monitoring.

In the present work a novel application of data fusion to an environmental monitoring study is proposed. This paper involves the joint analysis of zeroth-, first- and second-order data measured on a particular environmental system. The main advantage of this methodology is the possibility of analyzing the relationships of the different order data provided by several analytical techniques.

Chemometric modeling of organic contaminant sources in surface waters of a mediterranean river basin.

Chemometric methods are applied to the analysis and interpretation of large multivariate datasets obtained in environmental monitoring studies. Concentrations of multiple organic compounds were measured in river samples taken from several sampling sites, at various geographical locations, during a number of campaigns and/or sampling time periods. Samples were collected and analyzed as part of an extensive multi-annual monitoring program from a mediterranean river basin (in Catalonia, at the northeast of Spain) by the Water Quality Regional Agency.