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利用色谱指纹图谱和化学计量学对阿贝基纳( Arbequina )品种特级初榨橄榄油的地理来源进行鉴定。

Authentication of the geographical origin of extra-virgin olive oil of the Arbequina cultivar by chromatographic fingerprinting and chemometrics.

机构信息

Chemometrics, Qualimetrics and Nanosensors Group, Department of Analytical and Organic Chemistry, Rovira i Virgili University, Marcel·l í Domingo s/n, 43007, Tarragona, Spain.

Department of Analytical Chemistry, University of Granada, c/ Fuentenueva, s.n., E-18071, Granada, Spain.

出版信息

Talanta. 2019 Oct 1;203:194-202. doi: 10.1016/j.talanta.2019.05.064. Epub 2019 May 18.

Abstract

This paper proposes to use chromatographic fingerprints coupled to multivariate techniques to authenticate the geographical origin of extra-virgin olive oils (EVOO) of the Arbequina botanical variety. This methodology uses the whole or part of the chromatogram as input data for the classification models but does not identify or quantify the chemicals constituents. Arbequina monovarietal EVOOs from three geographical origins were studied: two from adjacent European Protected Designation of Origin areas, Siurana and Les Garrigues, in Catalonia in the northeast of Spain; and the third from the south of Spain (Andalucia and Murcia). Three chromatographic fingerprints of each sample were obtained by both reverse and normal phase liquid chromatography coupled to charged aerosol detector (HPLC-CAD), and high temperature gas chromatography coupled to flame ionization detector [(HT)GC-FID]. Principal component analysis (PCA) was used as exploratory technique and soft independent modelling of class analogy (SIMCA) and partial least square-discriminant analysis (PLS-DA) were used as classification methods. High and low-level data fusion strategies were also applied to improve the classification results obtained when the data acquired from each analytical technique were separately used. The results were best for the PLS-DA model with low-level fusion of two techniques (HT)GC-FID with HPLC-CAD, independently of the phase mode. Sensitivity and specificity were 100% in almost all classes, error was 0% for all classes and an inconclusive ratio of just 4% was obtained for the Les Garrigues class due to double assignations.

摘要

本文提出使用色谱指纹图谱结合多元技术来验证阿贝吉纳(Arbequina)品种的特级初榨橄榄油(EVOO)的地理来源。该方法使用整个或部分色谱图作为分类模型的输入数据,但不识别或量化化学成分。研究了来自三个地理来源的 Arbequina 单品种 EVOO:两个来自西班牙东北部加泰罗尼亚的相邻欧洲受保护原产地名称地区(Siurana 和 Les Garrigues);第三个来自西班牙南部(安达卢西亚和穆尔西亚)。通过反相和正相液相色谱与带电气溶胶检测器(HPLC-CAD)以及高温气相色谱与火焰电离检测器[(HT)GC-FID],获得了每个样品的三个色谱指纹图谱。主成分分析(PCA)被用作探索性技术,软独立建模的类模拟(SIMCA)和偏最小二乘判别分析(PLS-DA)被用作分类方法。还应用了高低水平数据融合策略,以改善当分别使用从每种分析技术获得的数据时获得的分类结果。对于(HT)GC-FID 与 HPLC-CAD 的低水平融合的 PLS-DA 模型,结果最好,无论相模式如何,灵敏度和特异性均接近 100%,几乎所有类别的错误率均为 0%,Les Garrigues 类别的不确定率仅为 4%,这是由于双重分配。

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