New approaches in the chemometric analysis of infrared spectra of extra-virgin olive oils

The aim of this paper is to apply new chemometric approaches to obtain quantitative information from near and mid infrared spectra of Andalusian extra-virgin olive oils, using gas chromatography as a classical reference analytical technique. Estimations of the content in saturated, monounsaturated and polyunsaturated fatty acids are given using partial least squares regression from the near and mid infrared data matrices as well as their concatenated matrix. The different estimations are evaluated in terms of goodness of fit (calibration) and prediction (validation), as a function of the number of partial least squares factors in the regression model and the used matrix of data. Furthermore, the nature, systematic or random, of the prediction errors is studied by a decomposition of their mean squared error. Finally, procedures of cross-validation are implemented in order to generalize the previous results.