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采用非靶向脂质组学方法研究黑皮诺葡萄酒中的脂质并预测葡萄酒产地。

Untargeted lipidomic approach in studying pinot noir wine lipids and predicting wine origin.

机构信息

Department of Food Science and Technology, Oregon State University, 100 Wiegand Hall, Corvallis, OR 97331, United States.

Department of Food Science and Technology, Oregon State University, 100 Wiegand Hall, Corvallis, OR 97331, United States.

出版信息

Food Chem. 2021 Sep 1;355:129409. doi: 10.1016/j.foodchem.2021.129409. Epub 2021 Mar 10.

Abstract

An untargeted lipidomic profiling approach based on ultra - performance liquid chromatography - time-of-flight tandem mass spectrometry (UPLC-TOF-MS/MS) was successfully used to study the origin of commercial Pinot noir wines. The total wine lipids were extracted using a modified Bligh-Dyer method. In all wine samples, the total lipids were less than 0.1% (w/w) of wine. The wines analyzed consisted of 222 lipids from 11 different classes. 48 commercial Pinot noir wine samples were collected from producers in Burgundy, California, Oregon, and New Zealand. Lipidomic data was studied using advanced multivariate analysis methods, random forest, k-nearest neighbor (k-NN), and linear discriminant analysis. The overall classification accuracy was 97.5% for random forest and 90% for k-NN. Wine lipids showed a strong potential for classifying wines by origin, with the top 58 lipids contributing to the discrimination. This information could potentially be used for further study of the impacts of lipids on wine characteristics and authenticity.

摘要

基于超高效液相色谱-飞行时间串联质谱(UPLC-TOF-MS/MS)的非靶向脂质组学分析方法成功地用于研究商业黑皮诺葡萄酒的起源。采用改良的布莱迪-戴尔法提取总酒脂质。在所有酒样中,总脂质含量均低于酒的 0.1%(w/w)。分析的葡萄酒包含来自 11 个不同类别的 222 种脂质。从勃艮第、加利福尼亚、俄勒冈和新西兰的生产商处收集了 48 种商业黑皮诺葡萄酒样品。使用先进的多元分析方法、随机森林、k-最近邻(k-NN)和线性判别分析研究了脂质组学数据。随机森林的总体分类准确率为 97.5%,k-NN 的为 90%。葡萄酒脂质具有通过起源对葡萄酒进行分类的强大潜力,前 58 种脂质对区分有贡献。这些信息可能有助于进一步研究脂质对葡萄酒特征和真实性的影响。

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