An enhanced algorithm for linear multivariate calibration

We present a new method of linear multivariate calibration that can generate better prediction results than those obtained by partial least squares (PLS). This is accomplished by incorporating the spectrum of the desired species into the calibration procedure. The method combines the advantages of different standard methods and is therefore called hybrid linear analysis (HLA). In side-by-side tests using both simulated and experimental data, HLA produced lower prediction errors than PLS in all instances. We recommend HLA over PIS in situations where the spectrum of the desired species is available.