November 13 - 16, 2006, Easter Analytical Symposium and Exhibit (EAS), Somerset, NJ

An Application of SIMPLISMA for the Deterministic Variable Selection in Multivariate Regression Analysis

Michel Hachey, Andrey Bogomolov, Michael Boruta

Abstract

A new approach for the pre-selection of wavelength, which can be applied with Partial Least Squares or other multivariate regression techniques, will be presented. This variable selection method utilizes the purity function from the SIMPLe-to-use Interactive Self-modeling Mixture Analysis (SIMPLISMA) algorithm to help determine the most significant and influential regions. The selected intervals are then individually tested in practical modeling and prediction to obtain the best possible subset of variables. This algorithm is simple, intuitive, and more readily relates to spectroscopically and chemically understandable features than iterative variable searches based solely on statistical selection criteria.

The new method was tested on a set of infrared spectra for proteins. The goal was to use region selection to improve the quantitative determination of the fractions of two secondary structure elements, the α-helices and β-sheets in the protein polypeptide chain. Comparison will be made to the results obtained through interval PLS (i-PLS), an iterative search-based algorithm.

Download the presentation in MS PowerPoint (2.11 Mb ZIP file).
 

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