| 摘要 |
An automated method for modeling spectral data includes accessing a set of spectral data, corresponding to each of a plurality of samples, each set having associated therewith at least one independently measured constituent value (201).Data transforms are applied to the set of spectral data to generate, for each sample, a set of transformed and untransformed spectral data, which with its associated constituent values, is divided into a calibration sub-set (231) and a validation sub-set (232). One or more of a partial least squares, principal component regression, neural net, or a multiple linear regression analysis is applied to the calibration data sub-sets to obtain corresponding modeling equations for predicting the target substance amount in a sample. The modeling equation with 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. |
