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
Current Analytical Chemistry
Title:Exploring a Tobacco Data Set with a Multiblock PLS Method
Volume: 8 Issue: 2
Author(s): Myrtille Vivien and Robert Sabatier
Affiliation:
Keywords: GOMCIA-PLS, Multiblock PLS, Multivariate data analysis, Regression
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.
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Cite this article as:
Vivien Myrtille and Sabatier Robert, Exploring a Tobacco Data Set with a Multiblock PLS Method, Current Analytical Chemistry 2012; 8 (2) . https://dx.doi.org/10.2174/157341112800392643
DOI https://dx.doi.org/10.2174/157341112800392643 |
Print ISSN 1573-4110 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-6727 |
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