Multivariate Curve Resolution of incomplete data multisets

In this paper the application of the Multivariate Curve Resolution Alternating Least Squares method (MCR-ALS) to incomplete data multisets is explored. The experimental incomplete data multiset studied in this work is taken from a previous multiannual atmospheric monitoring study of the changes of ozone and nitrogen oxide concentrations in an air quality sampling station located in the city of Barcelona, in which some of the individual data sets were missing. Based on the preliminary results obtained in this study, new data multisets, complete and incomplete, with different levels of noise were simulated and analysed by a new variant of the MCR-ALS method which optimises a combined error function including all possible complete data subsets derived from the original incomplete data multiset. Conclusions are drawn about the effects of data completeness on the results obtained for different noise levels and on the viability of trilinear models.

Graphical abstractMultivariate Curve Resolution is applied for the first time to incomplete data multisets. New data multisets, complete and incomplete, with different levels of noise were simulated and analysed by a new variant of the MCR-ALS method which optimises a combined error function including all possible complete data subsets derived from the original incomplete data multiset.Figure optionsDownload full-size imageDownload as PowerPoint slide