Klockmann Sven, Reiner Eva, Cain Nicolas, Fischer Markus
Hamburg School of Food Science, Institute of Food Chemistry, University of Hamburg , Grindelallee 117, 20146 Hamburg, Germany.
J Agric Food Chem. 2017 Feb 22;65(7):1456-1465. doi: 10.1021/acs.jafc.6b05007. Epub 2017 Feb 9.
A targeted metabolomics LC-ESI-QqQ-MS application for geographical origin discrimination based on 20 nonpolar key metabolites was developed, validated according to accepted guidelines and used for quantitation via stable isotope labeled internal standards in 202 raw authentic hazelnut samples from six countries (Turkey, Italy, Georgia, Spain, France, and Germany) of harvest years 2014 and 2015. Multivariate statistics were used for detection of significant variations in metabolite levels between countries and, moreover, a prediction model using support vector machine classification (SVM) was calculated yielding 100% training accuracy and 97% cross-validation accuracy, which was subsequently applied to 55 hazelnut samples for the confectionary industry gaining up to 80% correct classifications compared to declared origin. The present method demonstrates the great suitability for targeted metabolomics applications in the geographical origin determination of hazelnuts and their applicability in routine analytics.
开发了一种基于20种非极性关键代谢物的靶向代谢组学液相色谱-电喷雾串联四极杆质谱(LC-ESI-QqQ-MS)方法,用于区分榛子的地理来源。该方法根据公认的指南进行了验证,并通过稳定同位素标记的内标物对来自六个国家(土耳其、意大利、格鲁吉亚、西班牙、法国和德国)的201份2014年和2015年收获的正宗榛子样品进行了定量分析。使用多元统计方法检测各国之间代谢物水平的显著差异,此外,还计算了一个使用支持向量机分类(SVM)的预测模型,其训练准确率为100%,交叉验证准确率为97%。随后,该模型应用于55份用于糖果行业的榛子样品,与申报的产地相比,正确分类率高达80%。本方法证明了靶向代谢组学在榛子地理来源测定中的高度适用性及其在常规分析中的应用价值。