| Summary: | The development of sustainable and environmentally friendly analytical methods for agri-food products and the modification of reference methods is an essential issue to be treated in green analytical chemistry. The potential application of non-destructive spectroscopic techniques with chemometrics tools to achieve these principles are examined in this work. In this study a new sustainable analytical approach based on the use of fluorescence spectroscopy and multivariate analysis methods of Machine-Learning(Support Vector Machine regression) and chemometrics (Partial Least Square regression) have been developed to control the quality of virgin olive oils in Morocco according to their shelf life. The spectral data of 45 samples were first analyzed by principal component analysis method (PCA), the PCA method shows an important classification of the three groups of olive oil according to their shelf life. The use of the regression methods SVM and PLS shows a high ability to predict the quality of olive oils, this ability is shown by the high value of R-square and the low value of root mean square error of calibration and crossvalidation (RMSEC, RMSECV), the validation of these models by cross-validation shows the potential of this sustainable analytical approach in the determination of the quality of virgin olive oils.
|
|---|
