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Abstract

An automated method for modeling spectral data is provided, wherein the spectral data generated by one of diffuse reflectance, clear transmission, or diffuse transmission. The method includes accessing a set of spectral data, the set of spectral data including, corresponding spectral data for each of a plurality of samples, the spectral data for each of the plurality of samples having associated therewith at least one constituent value, the at least one constituent value being a reference value for a target substance in the sample which is measured by a independent measurement technique. A plurality of data transforms are applied to the set of spectral data to generate, for each sample, a set of transformed and untransformed spectral data. The set of transformed and untransformed spectral data, with its associated constituent values, is divided into a calibration sub-set and a validation sub-set, and one or more of a partial least squares, a principal component regression, a neural net, or a multiple linear regression analysis is applied to the transformed and untransformed calibration data sub-sets to obtain corresponding modeling equations for predicting the amount of the target substance in a sample. The modeling equation which provides the best correlation between the spectral data in the validation sub-set and the corresponding constituent values in the validation sub-set is identified, preferably as a function of the SEE and SEP.

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