Don’t choose between chemometric regression tools – combine ILS and CLS for a powerful synergistic approach.
Neal Gallagher |
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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