Research Unit of Mathematical Sciences, University of Oulu, FI-90014 Oulu, Finland; Department of Food and Nutrition, P.O. Box 66, FI-00014, University of Helsinki, Finland; Department of Analytical Chemistry, Applied Chemometrics and Molecular Modelling, Vrije Universiteit Brussel (VUB), Laarbeeklaan 103, B-1090 Brussels, Belgium.
Chemometrics and Analytical Technology, Faculty of Science, University of Copenhagen, Rolighedsvej 26, 1958 Frederiksberg, Denmark.
Food Chem. 2022 Jul 30;383:132565. doi: 10.1016/j.foodchem.2022.132565. Epub 2022 Feb 25.
Recognized for its nutritional and therapeutic use, extra-virgin Argan Oil (EVAO) is frequently adulterated. Selected-Ion Flow-Tube Mass Spectrometry (SIFT-MS) spectra were applied to quantify adulterants (i.e., Argan oil of lower quality (LQAO), olive oil (OO), and sunflower oil (SO)) in EVAO. Four data sets, i.e., using HO, NO, O reagent ions, and the combined data were considered. Soft independent modelling of class analogy (SIMCA), and partial least squares discriminant analysis (PLS-DA) were assessed to distinguish adulterated- from pure EVAO. The effectiveness of SIFT-MS associated with PLS and support vector machine (SVM) regression to quantify trace adulterants in EVAO was evaluated. Variable Importance in Projection (VIP), and interval-PLS (iPLS) were also investigated to extract useful features. Different models were built to predict the EVAO authenticity and the degree of adulteration. High accuracy was achieved. SIFT-MS spectra handled with the appropriate chemometric tools were found suitable for the quality evaluation of EVAO.
特级初榨阿甘(Argan)油(EVAO)因其营养价值和治疗用途而广受欢迎,但也经常被掺假。本研究采用选择离子流管质谱(SIFT-MS)技术对掺杂物(即低质量阿甘油(LQAO)、橄榄油(OO)和葵花籽油(SO))进行定量分析。本研究共考虑了 4 个数据集,即分别使用 HO、NO、O 反应离子,以及综合数据。通过软独立建模分类分析(SIMCA)和偏最小二乘判别分析(PLS-DA)来区分掺假和纯 EVAO。此外,还评估了 SIFT-MS 与偏最小二乘(PLS)和支持向量机(SVM)回归相结合定量检测 EVAO 中痕量掺杂物的效果。还研究了变量重要性投影(VIP)和区间偏最小二乘(iPLS)以提取有用特征。建立了不同的模型来预测 EVAO 的真实性和掺假程度。结果表明,SIFT-MS 光谱结合适当的化学计量学工具,可用于评估 EVAO 的质量。