1. A method comprising: decomposing a training set corresponding to spectral data obtained for an interfering substance using an infrared spectrometry system to obtain a principal component matrix having a plurality of principal component vectors; generating a sample spectrum vector for a sample mixture using the infrared spectrometry system; variably rejecting portions of the sample spectrum vector that do not correspond to a selected one of the plurality of principal component vectors by incrementally: selecting a sub-region of the sample spectrum vector and a corresponding sub-region of the selected principal component vector; and multiplying the selected sub-region of the sample spectrum vector with the corresponding sub-region of the selected principal component vector to provide a coefficient indicative of the weighting of the selected principal component vector for the selected sub-regions; excluding sub-regions of the sample spectrum vector and corresponding principal component vector based on the incrementally provided coefficients; multiplying the sample spectrum vector with the principal component matrix for the non-excluded sub-regions to provide a weighting vector indicative of the contribution of the principal component matrix; multiplying the weighting vector by the principal component matrix to provide a predicted interference vector, the predicted interference vector corresponding to the interfering substance in the sample mixture, the interfering substance being capable of masking a presence or an absence of one or more chemicals of interest in the sample mixture; subtracting the predicted interference vector from the sample spectrum vector to provide a corrected spectrum vector; and comparing the corrected spectrum vector to known spectrum data for one or more chemicals of interest to determine if the corrected spectrum vector is representative of a chemical of interest.