Synthetic multivariate models to accommodate unmodeled interfering spectral components during quantitative spectral analyses

Abstract

The analysis accuracy and precision of any multivariate calibration method will be severely degraded if unmodeled sources of spectral variation are present in the unknown sample spectra. A synthetic method for correcting errors generated by the presence of unmodeled components or other sources of unmodeled spectral variation has been developed. If the spectral shape of the unmodeled spectral component can be obtained and mathematically added in variable amounts to the original calibration spectra, then a new synthetic multivariate calibration model can be generated from the augmented data to accommodate the presence of the unmodeled source of spectral variation.