Combined use of conventional and second-derivative data in the SIMPLISMA self-modeling mixture analysis approach.

Simple-to-use interactive self-modeling mixture analysis (SIMPLISMA) is a successful pure variable approach to resolve spectral mixture data. A pure variable (e.g., wavenumber, frequency number, etc.) is defined as a variable that has significant contributions from only one of the pure components in the mixture data set. For spectral data with highly overlapping pure components or significant baselines, the pure variable approach has limitations; however, in this case, second-derivative spectra can be used. In some spectroscopies, very wide peaks of components of interest are overlapping with narrow peaks of interest. In these cases, the use of conventional data in SIMPLISMA will not result in proper pure variables. The use of second-derivative data will not be successful, since the wide peaks are lost. This paper describes a new SIMPLISMA approach in which both the conventional spectra (for pure variables of wide peaks) and second-derivative spectra (for pure variables of narrow peaks, overlapping with the wide peaks) are used. This new approach is able to properly resolve spectra with wide and narrow peaks and minimizes baseline problems by resolving them as separate components. Examples will be given of NMR spectra of surfactants and Raman imaging data of dust particle samples taken from a lead and zinc factory's ore stocks that were stored outdoors.