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Techniques & Tools Chemical, Data Analysis, Spectroscopy

Chemometrics United

Chemometrics can be thought of as signal processing for measurements made on chemical systems, and the tools available range from simple to dizzyingly complex. The best tool for a given task depends both on the objective and on how the measured signal manifests. If the signal is reasonably described by the linear mixture model, it’s common to rely on multivariate linear regression tools, such as partial least squares and classical least squares (CLS) for quantification. Partial least squares is one member of a broad class of inverse least squares (ILS) methods and CLS is often referred to as ‘forward least squares’. In the recent past, chemometricians have favored ILS methods, dwelling on the disadvantages of CLS while ignoring the downside of ILS. I believe that a solid understanding of the pros and cons of both methods eliminates the apparent conflict between ILS and CLS, and instead allows them to be used in synergy.

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About the Author

Neal Gallagher

Neal Gallagher received B.S. degrees in Chemical Engineering and Engineering Physics from the University of Colorado in 1985, an M.S. in Chemical Engineering from the University of Washington in 1987, and a Ph.D. in Chemical Engineering with a mathematics minor from the University of Arizona in 1992. Neal is Vice President and co-founder of Eigenvector Research, Inc. Neal has worked on an extremely wide variety of chemometrics projects for a number of companies, national laboratories and academic institutions. He specializes in chemometrics consulting, algorithm development for detection, classification and quantification, chemometrics research, short courses and software. He is a co-author of PLS_Toolbox for use with MATLAB, it’s companion stand-alone product Solo and other advanced chemometrics software packages. And has been working extensively in developing algorithms for hyperspectral image analysis with an emphasis on anomaly and target detection.

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