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基于液相色谱紫外吸收指纹图谱和化学计量学的人造黄油及脂肪涂抹制品地理来源鉴别

Authentication of the Geographical Origin of Margarines and Fat-Spread Products from Liquid Chromatographic UV-Absorption Fingerprints and Chemometrics.

作者信息

Bikrani Sanae, Jiménez-Carvelo Ana M, Nechar Mounir, Bagur-González M Gracia, Souhail Badredine, Cuadros-Rodríguez Luis

机构信息

Department of Chemistry, Faculty of Sciences, University of Abdelmalek Essaâdi, Av. Sebta, Mhannech II, 93002 Tetouan, Morocco.

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

出版信息

Foods. 2019 Nov 19;8(11):588. doi: 10.3390/foods8110588.

Abstract

Fat-spread products are a stabilized emulsion of water and vegetable oils. The whole fat content can vary from 10 to 90% (/). There are different kinds, which are differently named, and their composition depends on the country in which they are produced or marketed. Thus, having analytical solutions to determine geographical origin is required. In this study, some multivariate classification methods are developed and optimised to differentiate fat-spread-related products from different geographical origins (Spain and Morocco), using as an analytical informative signal the instrumental fingerprints, acquired by liquid chromatography coupled with a diode array detector (HPLC-DAD) in both normal and reverse phase modes. No sample treatment was applied, and, prior to chromatographic analysis, only the samples were dissolved in n‑hexane. Soft independent modelling of class analogy (SIMCA) and partial least squares-discriminant analysis (PLS-DA) were used as classification methods. In addition, several classification strategies were applied, and performance of the classifications was evaluated applying proper classification metrics. Finally, 100% of samples were correctly classified applying PLS-DA with data collected in reverse phase.

摘要

脂肪涂抹产品是水和植物油的稳定乳液。其总脂肪含量可在10%至90%之间变化(/)。有不同种类,名称各异,其成分取决于生产或销售所在的国家。因此,需要有确定地理来源的分析方法。在本研究中,开发并优化了一些多元分类方法,以区分来自不同地理来源(西班牙和摩洛哥)的脂肪涂抹相关产品,使用正相和反相模式下通过液相色谱与二极管阵列检测器联用(HPLC-DAD)获得的仪器指纹作为分析信息信号。未进行样品处理,在色谱分析之前,仅将样品溶解在正己烷中。使用类相关软独立建模(SIMCA)和偏最小二乘判别分析(PLS-DA)作为分类方法。此外,应用了几种分类策略,并使用适当的分类指标评估分类性能。最后,使用反相收集的数据应用PLS-DA时,100%的样品被正确分类。

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