1. A method for analyzing multivariate spectral data, comprising the steps of: a) creating a calibration model for a calibration set of multivariate spectral data A by: i) obtaining a set of reference component values C representative of at least one of the spectrally active components in the calibration set of multivariate spectral data A, ii) estimating pure-component spectra {circumflex over (K)} for the at least one of the spectrally active components according to {circumflex over (K)}&equals;(C T C) −1 C T A&equals;C &plus; A, iii) obtaining spectral residuals E A according to E A &equals;A−C{circumflex over (K)}, and iv) augmenting the estimated pure-component spectra {circumflex over (K)} with at least one vector of the spectral residuals E A to obtain augmented pure-component spectra 49 K ~ ^ ; and b) predicting a set of component values 50 C ~ ^ for a prediction set of multivariate spectral data A P by: i) further augmenting the augmented pure-component spectra 51 K ~ ^ with at least one vector representing a spectral shape that is representative of at least one additional source of spectral variation in the prediction set, and ii) predicting the set of component values 52 C ~ ^ using the further augmented pure-component spectra 53 K ~ ^ according to 54 C ~ ^ = A P &it; K ~ ^ T &af; ( K ~ ^ &it; K ~ ^ T ) - 1 = A P &af; ( K ~ ^ T ) +.