Bajoub Aadil, Medina-Rodríguez Santiago, Olmo-García Lucía, Ajal El Amine, Monasterio Romina P, Hanine Hafida, Fernández-Gutiérrez Alberto, Carrasco-Pancorbo Alegría
Department of Analytical Chemistry, Faculty of Sciences, University of Granada, Ave. Fuentenueva, s/n, 18071 Granada, Spain.
Laboratory of Bioprocess and Bio-Interfaces, Faculty of Science and Technology, 23000 Beni Mellal, Morocco.
Int J Mol Sci. 2016 Dec 28;18(1):52. doi: 10.3390/ijms18010052.
Olive oil phenolic fraction considerably contributes to the sensory quality and nutritional value of this foodstuff. Herein, the phenolic fraction of 203 olive oil samples extracted from fruits of four autochthonous Moroccan cultivars ("Picholine Marocaine", "Dahbia", "Haouzia" and "Menara"), and nine Mediterranean varieties recently introduced in Morocco ("Arbequina", "Arbosana", "Cornicabra", "Frantoio", "Hojiblanca", "Koroneiki", "Manzanilla", "Picholine de Languedoc" and "Picual"), were explored over two consecutive crop seasons (2012/2013 and 2013/2014) by using liquid chromatography-mass spectrometry. A total of 32 phenolic compounds (and quinic acid), belonging to five chemical classes (secoiridoids, simple phenols, flavonoids, lignans and phenolic acids) were identified and quantified. Phenolic profiling revealed that the determined phenolic compounds showed variety-dependent levels, being, at the same time, significantly affected by the crop season. Moreover, based on the obtained phenolic composition and chemometric linear discriminant analysis, statistical models were obtained allowing a very satisfactory classification and prediction of the varietal origin of the studied oils.
橄榄油中的酚类成分对这种食品的感官品质和营养价值有很大贡献。在此,对从摩洛哥四个本土品种(“摩洛哥皮乔利”、“达比亚”、“豪齐亚”和“梅纳拉”)以及最近引入摩洛哥的九个地中海品种(“阿贝基纳”、“阿尔博萨纳”、“科尔尼卡布拉”、“弗拉托伊奥”、“霍吉布兰卡”、“科罗内基”、“曼萨尼拉”、“朗格多克皮乔利”和“皮夸尔”)的果实中提取的203个橄榄油样品的酚类成分,在连续两个作物季(2012/2013和2013/2014)中通过液相色谱-质谱法进行了研究。共鉴定并定量了属于五个化学类别(裂环烯醚萜类、简单酚类、黄酮类、木脂素类和酚酸类)的32种酚类化合物(以及奎尼酸)。酚类成分分析表明,所测定的酚类化合物呈现出品种依赖性水平,同时也受到作物季的显著影响。此外,基于所获得的酚类成分和化学计量学线性判别分析,获得了统计模型,能够对所研究油的品种来源进行非常令人满意的分类和预测。