Multivariate response modeling, numerical

In this situation, it would be ideal to produce a calibration on only one of the analyzers, and simply transfer it to all of the other analyzers. There are certainly cases where this can be done effectively, especially if response variability between different analyzers is low and the calibration model is not very complex. However, the numerous examples illustrated above show that multivariate (chemometric) calibrations could be particularly sensitive to very small changes in the analyzer responses. Furthermore, it is known that, despite the great progress in manufacturing reproducibility that process analyzer vendors have made in the past decade, small response variabilities between analyzers of the same make and... 

As a multivariate statistical method, partial least square (PLS) is of particular interest in the QSAR field [3]. PLS can analyze data with strongly collinear, noisy, and numerous X variables, while simultaneously modeling several response variables Y. PLS can also provide several prediction regions and diagnostic plots as statistical measures. Using such an approach, QSAR scientists can extract the patterns embedded in the structure-activity data. 

Multivariate regression. A statistical technique used for the modeling and analysis of numerical data consisting of values of a dependent (response) variable and one or more independent (explanatory) variables. 

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