Chemometric calibration of infrared spectrometers: selection and validation of variables by non-linear models

Data from spectrophotometers form spectra that are sets of a great number of exploitable variables in quantitative chemical analysis; calibration models using chemometric methods must be established to exploit these variables. In order to design these calibration models which are specific to each analyzed parameter, it is advisable to select a reduced number of spectral variables. This paper presents a new incremental method (step by step) for the selection of spectral variables, using linear regression or neural networks, and based on an objective validation (external) of the calibration model; this validation is carried out on data that are independent from those used during calibration. The advantages of the method are discussed and highlighted, in comparison to the current calibration methods used in quantitative chemical analysis by spectrophotometry.