Discrimination of the variety and region of origin of extra virgin olive oils using C-13 NMR and multivariate calibration with variable reduction

被引:87
作者
Shaw, AD
diCamillo, A
Vlahov, G
Jones, A
Bianchi, G
Rowland, J
Kell, DB
机构
[1] IST SPERIMENTALE ELAIOTECN, I-65013 CITTA S ANGELO, PESCARA, ITALY
[2] UNIV WALES, DEPT COMP SCI, ABERYSTWYTH SY23 3DB, CEREDIGION, WALES
关键词
olive oil; adulteration; chemometrics; PLS; variable reduction;
D O I
10.1016/S0003-2670(97)00037-8
中图分类号
O65 [分析化学];
学科分类号
070302 [分析化学]; 081704 [应用化学];
摘要
There is strong evidence that consumption of olive oil, especially extra virgin olive oil, reduces the risk of circulatory system diseases. Such oil is generally more expensive than other edible oils, Italian-and in particular Tuscan-oils being particularly favoured by connoisseurs, and commanding an even higher price. There is therefore a great temptation to adulterate olive oil with a cheaper oil, or falsify its origin or grade. An easy and reliable method to identify different types of olive oil is required. Our work has focused on discriminating extra virgin olive oils by their region and variety. We have applied Principal Components Analysis (PCA), Principal Components Regression (PCR) and Partial Least Squares (PLS) to discriminate olive oils on the basis of their C-13 NMR spectra. Variable Selection was used in order to reduce the number of variables in the data. Two main methods of variable 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 number of variables varied between datasets. PCR2 and PLS2 models, in which a single model is used to predict each variety or each region simultaneously, 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 variety of all but 1 of 66 samples was correctly predicted, It is clear that multiple, specialised models perform much better than ''global'' ones, and that the inclusion of certain variables can be highly detrimental to the multivariate calibration process.
引用
收藏
页码:357 / 374
页数:18
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