Exploring a Tobacco Data Set with a Multiblock PLS Method

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.