New augmented classical least squares methods for improved quantitative spectral analyses

A series of new augmented classical least squares (ACLS) methods have been developed that show considerable promise for improving multivariate spectral calibrations. The normal limitations of classical least squares (CLS) methods are largely overcome by using information derived from the spectral residuals during the CLS calibration in the development of the new ACLS models. The resulting models are able to provide prediction ability comparable to that of partial least squares (PLS) even when only concentration information about a single analyte is known in the calibration data set. By combining ACLS calibration with our recently described prediction augmented CLS (PACLS) method, we are able to rapidly update spectral calibration models during prediction for the presence of unmodeled chemical components in the unknown samples, system drift, or changes in spectrometers. With PACLS updating, prediction ability is often better than possible with PLS. One of the new calibration augmented CLS methods is combined with PACLS to demonstrate the ability of the method to transfer multivariate calibrations between near-infrared (NIR) spectrometers on a system of multi-component organic solvents. [Copyright &y& Elsevier]