Evaluating the potential of phenolic profiles as discriminant features among extra virgin olive oils from Moroccan controlled designations of origin

被引:30
作者
Bajoub, Aadil [1 ]
Ajal, El Amine [2 ]
Fernandez-Gutierrez, Alberto [1 ]
Carrasco-Pancorbo, Alegria [1 ]
机构
[1] Univ Granada, Dept Analyt Chem, Fac Sci, Ave Fuentenueva S-N, E-18071 Granada, Spain
[2] Prov Dept Agr Azilal, POB 13, Azilal 22000, Morocco
关键词
Phenolic compounds; Chemometrics; Geographical origin authentication; Moroccan monovarietal olive oils; Geographical origin indication systems; Liquid chromatography-mass spectrometry; PERFORMANCE LIQUID-CHROMATOGRAPHY; TIME-OF-FLIGHT; PROTECTED DESIGNATION; FATTY-ACID; SENSORY CHARACTERISTICS; GEOGRAPHICAL ORIGIN; CULTIVAR; CLASSIFICATION; QUALITY; ESTABLISHMENT;
D O I
10.1016/j.foodres.2016.03.010
中图分类号
TS2 [食品工业];
学科分类号
0832 ;
摘要
Herewith, the potential of an approach based on the combination of the determination of phenolic compounds and the use of chemometric treatments has been evaluated to establish, for the first time, promising models to authenticate the provenance of Moroccan monovarietal olive oils produced under different geographical origin indication systems. To achieve this purpose, 136 commercial extra virgin olive oil samples from three diverse production areas (Meknes territory; the Protected Geographical Indication Ouazzane; and the Protected Designation of Origin Tyout-Chiadma) were collected over two consecutive crop seasons (2012/2013 and 2013/2014). Their phenolic fraction composition was investigated by using high performance liquid chromatography coupled to mass spectrometry (HPLC-ESI-IT MS). The results showed that geographical provenance and harvest season had a marked influence on the content of identified phenolic compounds. Principal components analysis (PCA) and linear discriminant analysis (LDA) were applied to test the potential of the determined compounds as geographical discriminant features, achieving a noticeable discrimination among the three evaluated regions. The contribution of each analyte to the statistic model has been evaluated in depth. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码:41 / 51
页数:11
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