Patent Document ID: 8494818
Application ID: 12736929
Patent Flag: 1

Claim One:
1. A method of analyzing spectral data for the selection of the best calibration model from a plurality of calibration models, relating spectra of a substance to a physical or a chemical parameter of the substance, over a predetermined range of the physical or chemical parameters, comprising the steps of: a) capturing spectral data of the substance with respective values of the physical or chemical parameter over the predetermined range; b) building a plurality of calibration models, using the captured spectral data from step a, in dependence upon the values of the physical or chemical parameter, said plurality of calibration models being based on said spectral data and physical or chemical parameter from step a and using statistical resampling methods to assess a predictive quality of the models; c) calculating tolerance intervals of the predicted results obtained using the models built in step b at reference level for each calibration model, d) displaying the tolerance intervals at each reference level for each calibration model; and e) selecting at least one of the calibration models upon a predefined criterion; wherein the calibration model comprises a data regression model and a data pretreatment; wherein the data regression model comprises any spectral data pretreatment followed by selecting at least one of the following available choices comprising a multiple linear regression model, a partial least squares model, a principal component regression model and an artificial neuronal network model; wherein the predefined criterion comprises at least one of the following available choices comprising a tolerance interval corresponding to a small relative error of the calibration model, an acceptance limit of the physical parameter corresponding to a small relative total error of the calibration model and a Fitting Model Index of the calibration model that is close to 1; and wherein the methodology to compute a tolerance interval is selected from the available choices of frequentist or Bayesian.