Combinative multivariate calibration that enhances prediction ability through removal of over-modeled regions

Abstract

A novel multivariate model for analysis of absorbance spectra allows for each wavelength or spectral region to be modeled with just enough factors to fully model the analytical signal without the incorporation of noise by using excess factors. Each wavelength or spectral region is modeled utilizing its own number of factors independently of other wavelengths or spectral regions. An iterative combinative PCR algorithm allows a different number of factors to be applied to different wavelengths. In an exemplary embodiment, a three-factor model is applied over a given spectral region. The residual of the three-factor model is calculated and used as the input for an additional five-factor model. Prior to the additional five factors being applied, some of the wavelengths are removed. This leads to a three-factor model over the first region and an eight-factor model over the second region. This analysis of residuals can be repeated such that a one to n factor model could be applied to any given wavelength, or rather any number of factors may be employed to model any given frequency or spectral region. A method of predicting concentration of a target analyte from sample spectra applies a calibration developed using the inventive PCR algorithm to a matrix of sample spectral to generate a vector of predicted concentrations for the target analyte.