A new approach for interactive self-modeling mixture analysis

Recently, a new SIMPLISMA approach was described where both the conventional spectral intensities (for pure variables of wide spectral features) and second derivative spectral intensities (for pure variables of narrow spectral features, overlapping with wide spectral features) are used. The term conventional is used to contrast the original data to the second derivative data. This new SIMPLISMA approach is able to resolve spectra with wide and narrow peaks properly and minimizes baseline problems by resolving them as separate components. A problem with this approach is that the mathematically calculated spectra shown during the interactive process are difficult to interpret. For example, a background spectrum may have a significant negative contribution from the other components in the mixture. In this paper, an alternative approach will be described based on a different algorithm which will avoid some of the problems with SIMPLISMA. Furthermore, this new approach is also capable to deal with concentrations that are constant in the data set. thod is based on the determination of the maximum angle between variables and is called stepwise maximum angle calculations (SMAC). es will be given of NMR spectra of bound and free surfactants and Raman microspectrometry data of dust particles taken from ore stocks of a lead and zinc factory.