DISCRIMINATION OF THE VARIETY AND REGION OF ORIGIN OF EXTRA VIRGIN OLIVE OILS USING C-13 NMR AND MULTIVARIATE CALIBRATION WITH VARIABLE REDUCTION

There is strong evidence that consumption of olive oil, especially ext ra virgin olive oil, reduces the risk of circulatory system diseases. Such oil is generally more expensive than other edible oils, Italian-a nd in particular Tuscan-oils being particularly favoured by connoisseu rs, and commanding an even higher price. There is therefore a great te mptation to adulterate olive oil with a cheaper oil, or falsify its or igin or grade. An easy and reliable method to identify different types of olive oil is required. Our work has focused on discriminating extr a virgin olive oils by their region and variety. We have applied Princ ipal Components Analysis (PCA), Principal Components Regression (PCR) and Partial Least Squares (PLS) to discriminate olive oils on the basi s of their C-13 NMR spectra. Variable Selection was used in order to r educe the number of variables in the data. Two main methods of variabl e selection have been used; these are the Fisher Ratio, and the ratio of Inner Variance to Outer Variance or Characteristicity CW. Eshuis, P .G. Kistemaker and H.L.C. Meuzelaar, in C.E.R. Jones and C.A. Cramers (Eds.), Analytical Pyrolysis, Elsevier, Amsterdam, 1977, pp. 151-156.] . Both these methods proved successful in improving the PCA clustering , and the prediction results of PCR and PLS, although the optimal numb er of variables varied between datasets. PCR2 and PLS2 models, in whic h a single model is used to predict each variety or each region simult aneously, achieved a successful prediction rate of some 70%. However, multiple PLS1 models routinely achieved successful predictions of over 90% and in many cases 100% of the data in test sets. Indeed the varie ty of all but 1 of 66 samples was correctly predicted, It is clear tha t multiple, specialised models perform much better than ''global'' one s, and that the inclusion of certain variables can be highly detriment al to the multivariate calibration process.