Olive oil fluorescence is related to oil composition. Here it is shown that the natural clustering of different types of commercial Spanish olive oils depends on their fluorescence excitation-emission matrices (EEMs). Fifty-six commercial samples of olive oil (29 virgin olive oils, 20 pure olive oils, and 7 olive-pomace oils) were used. The clustering method was hierarchical agglomerative clustering using the Euclidean distance as a similarity measure and the average linkage. Two spectral ranges were considered (which either contained the fluorescence peak of the chlorophylls or did not), and various methods for preprocessing the fluorescence spectra were compared. The oils were clearly distinguished using the unfolded EEMs measured between lambda(ex) = 300-400 nm and lambda(em) = 400-600 nm. The optimal preprocessing was normalization of the unfolded spectra followed by column autoscaling. Also shown are the advantages of using second-order data (EEMs) instead of first-order data (a single fluorescence spectrum) for each sample.
This paper shows the potential of excitation-emission fluorescence spectroscopy (EEFS) and three-way methods of analysis [parallel factor analysis (PARAFAC) and multiway partial least-squares (N-PLS) regression] as a complementary technique for olive oil characterization. The fluorescence excitation-emission matrices of a set of Spanish extra virgin, virgin, pure, and olive pomace oils were measured, and the relationship between them and some of the quality parameters of olive oils (peroxide value, K232, and K270) was studied. N-PLS was found to be more suitable than PARAFAC combined with multiple linear regression for correlating fluorescence and quality parameters, yielding better fits and lower prediction errors. The best results were obtained for predicting K270. EEFS allowed detection of extra virgin olive oils highly degraded at early stages (with high peroxide value) and little oxidized pure olive oils (with low K270). The proposed methodology may be used as an aid to analyze doubtful samples.
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