SELF-MODELING MIXTURE ANALYSIS OF 2ND-DERIVATIVE NEAR-INFRARED SPECTRAL DATA USING THE SIMPLISMA APPROACH

One of the major methods used to resolve spectral data by self-modeling techniques requires the presence of pure variables. A pure variable is a variable that has an intensity contribution from only one of the components in the data set. For spectral data obtained in the near-infrared (near-IR) region (ca. 1-2.5 mum), pure variables are often not available, due to the width of the spectral peaks and the presence of a baseline. The application of self-modeling mixture analysis techniques has to be used with caution for these data. In this paper, it will be shown that, despite the absence of pure variables in near-IR data, It is possible to resolve the data properly by using the second-derivative spectra as an intermediate step. The basic technique will be demonstrated with the recently developed SIMPLISMA (SIMPLe-to-use Interactive Self-modeling Mixture Analysis) approach using a simulated data set. A complete example is given for a five-component solvent mixture using near-IR data.