Multivariate Curve Resolution in the Analysis of Vibrational Spectroscopy Data Files

T he ability of chemometric techniques to reduce and resolve large data matrices into responses and corresponding weights of these responses has been shown with a variety of data sets, and there exist a variety of approaches capable of assessing such bilinear data sets measured with vibrational spectroscopy.1–4 We have focused on using a form of principal factor analysis, which uses matrix decomposition as the initial step to generate abstract factors and scores. This initial estimate is then reŽ ned in a second step to produce spectra and intensity matrices. The second step uses an iterative constrained least-squares method referred to as alternating least squares. The technique, which is particularly useful in extracting estimates of pure analyte spectra and concentration proŽ les from spectral data matrices, is generally referred to