1H-NMR and isotopic fingerprinting of olive oil and its unsaponifiable fraction. Tracing the geographical origin of virgin olive oils by pattern recognition

ALONSO SALCES, ROSA MARIA; SEGEBARTH, N.; GARMÓN-LOBATO, S.; HOLLAND, M. V.; MORENO-ROJAS, J. M.; FERNANDEZ-PIERNA, J. A.; BAETEN, V.; FUSELLI, S. R.; GALLO, B.; BERRUETA, L. A.; RENIERO, F.; GUILLOU, C.; HÉBERGER, K.

EUROPEAN JOURNAL OF LIPID SCIENCE AND TECHNOLOGY

WILEY-V C H VERLAG GMBH

Lugar: Weinheim; Año: 2015 vol. 117 p. 1991 - 2006

1438-7697

1HNMR spectral data and H and C isotope abundances of virgin olive oils (VOOs) and their unsaponifiable fractions were analysed by pattern recognition techniques, such as principal component analysis (PCA) and partialleast squares discriminant analysis (PLSDA). The aim was to develop chemical tools for the authentication of VOOs according to their geographical origin or Protected Designation of Origin (PDO), as well as to detect the mislabelling of the provenance of VOOs, at the regional or national level, or the mislabelling of nonPDO oils as PDO VOOs. The relationship between stable isotope abundances of the VOOs and their unsaponifiable fractions and the latitude of the VOO geographical origin was confirmed; but these criteria were not completely discriminant to differentiate VOOs according to their geographical origin. However, delta2H and/or delta13C data provided complementary geographical information to 1HNMR data in the PLSDA binary classification models afforded for VOOs from Greece, Spain, Italy, Izmir (Turkey), Crete (Greece), and the PDOs Riviera Ligure (Italy) and Huile d´olive d´AixenProvence (France). 2H/1H and 13C/12C ratios of the unsaponifiable fractions of VOOs are reported here for the first time. The present approach for PDO Riviera Ligure VOOs, based on 1HNMR data and C isotope abundance of the bulk oil and its unsaponifiable fraction, outperformed the previously reported classification models. Moreover, the PLSDA models to authenticate VOOs from Greece and detect nonGreek VOOs achieved over 93 % of correct predictions.