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单品种摩洛哥北部油橄榄的品质和化学成分分析:“摩洛哥 Picholine”品种的注册数据库开发与地理鉴别。

Quality and chemical profiles of monovarietal north Moroccan olive oils from "Picholine Marocaine" cultivar: registration database development and geographical discrimination.

机构信息

Department of Analytical Chemistry, Faculty of Science, University of Granada, Ave. Fuentenueva s/n, 18071 Granada, Spain; Agro-pôle Olivier, National School of Agriculture in Meknes, BP S/40 Meknès, Morocco.

Department of Analytical Chemistry, Faculty of Science, University of Granada, Ave. Fuentenueva s/n, 18071 Granada, Spain.

出版信息

Food Chem. 2015 Jul 15;179:127-36. doi: 10.1016/j.foodchem.2015.01.101. Epub 2015 Jan 28.

DOI:10.1016/j.foodchem.2015.01.101
PMID:25722147
Abstract

Current knowledge of the quality and composition of Moroccan olive oil is still incomplete and no consistent database compiling its properties is available. This study was carried out to achieve a comprehensive characterisation of north Moroccan olive oils. Thus, 279 olive samples of "Picholine Marocaine" cultivar grown in 7 Moroccan regions were collected, and oils extracted over two consecutive crop seasons (2011 and 2012) and analysed (considering physicochemical quality parameters and purity criteria). Results indicated that all the studied samples showed values fulfilling the established limits set by the International Olive Council (IOC) standards, with the exception of 32 samples that had a linolenic acid content higher than 1%, which is the maximum value fixed by the IOC regulation. Furthermore, the usefulness of the evaluated parameters for tracing the geographical origin of the studied samples was tested by using canonical discriminant analysis. A good rate of correct classification and prediction was achieved.

摘要

目前对摩洛哥橄榄油的质量和成分的了解仍不完整,也没有可用的统一数据库来编制其特性。本研究旨在对摩洛哥北部橄榄油进行全面的特性描述。因此,收集了 7 个摩洛哥地区种植的“Picholine Marocaine”品种的 279 个橄榄样本,并在两个连续的作物季节(2011 年和 2012 年)提取油并进行分析(考虑理化质量参数和纯度标准)。结果表明,所有研究样本均符合国际橄榄油理事会(IOC)标准规定的界限,只有 32 个样本的亚油酸含量高于 1%,这是 IOC 规定的最高值。此外,还通过使用典型判别分析来检验评估参数对研究样本地理来源的追溯能力。达到了很好的正确分类和预测率。

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