کد مقاله کد نشریه سال انتشار مقاله انگلیسی ترجمه فارسی نسخه تمام متن
4561146 1628464 2016 11 صفحه PDF سفارش دهید دانلود رایگان
عنوان انگلیسی مقاله ISI
Evaluating the potential of phenolic profiles as discriminant features among extra virgin olive oils from Moroccan controlled designations of origin
ترجمه فارسی عنوان
ارزیابی پتانسیل پروفایل های فنلی به عنوان ویژگی های متمایز در میان روغن زیتون فوق العاده از نامگذاری های کنترل شده مراکش
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موضوعات مرتبط
علوم زیستی و بیوفناوری علوم کشاورزی و بیولوژیک دانش تغذیه
چکیده انگلیسی


• Quali-quantitative characterization of olive oil phenolic comp. from PDO-oils.
• A LDA model for geographical classification of samples was constructed.
• A perfect discrimination between studied samples was achieved.
• Authenticating origin of Moroccan EVOOs under geographical indication labels.

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 (Meknès 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.

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ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Food Research International - Volume 84, June 2016, Pages 41–51
نویسندگان
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