Abstract

In chemistry, multiblock datasets are easily encountered with variables of different natures, or measured at different times for example, here, we use the sequential multiblock regression method GOMCIA-PLS1 to predict quantitative variables from several predictors gathered according to their nature and used simultaneously. In this article, it will be applied to predict a chemical variable from Near Infrared Spectrometry (NIRS) chemical and thermolyze data measured on different tobacco samples. The multiblock GOMCIA-PLS1 method is compared to other methods and its good performances are shown.

Keywords: GOMCIA-PLS, Multiblock PLS, Multivariate data analysis, Regression