Application of headspace sorptive extraction and gas chromatographic/mass spectrometric and chemometric methods to the quantification of pine nuts and pecorino in Pesto Genovese.

ABSTRACT

Headspace sorptive extraction and GC/MS, coupled with chemometric tools, were used to predict the amounts of pine nuts and Pecorino in Pesto Genovese, a typical Italian basil-based pasta sauce. Two groups of samples were prepared at different times and with ingredients from different batches for building the predicting models and testing their performances. Principal component analysis and partial least-squares regression (PLS) were applied to the chromatographic data. The 24 most-predictive variables were selected, and the application of PLS to the training set samples led to two models that explained approximately 70% of the variance in cross-validation, with prediction errors of 0.1 g for Pecorino and 0.6 g for pine nuts, thus confirming the reliability of the analytical method and the predicting ability of the models. The results obtained for the test set samples were not completely satisfactory, with a prediction error and a bias of 5.0 and -4.1 g, respectively, for Pecorino and corresponding values of 4.1 and 2.0 g for pine nuts. This preliminary study shows that the analytical methods used can allow construction of models with high predictive ability only if the great variability of the headspace composition of the ingredients and the effect of Twister are considered.