Department of Food and Nutrition, P.O. Box 66, 00014, University of Helsinki, Finland; Department of Computer Science, University of Helsinki, 00014 Helsinki, Finland; Department of Analytical Chemistry, Applied Chemometrics and Molecular Modelling, Vrije Universiteit Brussel (VUB), Laarbeeklaan 103, B-1090 Brussels, Belgium.
Department of Analytical Chemistry, Applied Chemometrics and Molecular Modelling, Vrije Universiteit Brussel (VUB), Laarbeeklaan 103, B-1090 Brussels, Belgium.
J Chromatogr A. 2022 May 10;1670:462972. doi: 10.1016/j.chroma.2022.462972. Epub 2022 Mar 20.
Argan (Argania spinosa L.) fruit kernels' composition has been poorly studied and received less research intensity than the resulting Argan oil. The Moroccan Argan kernels contain a wealth of metabolites and can be investigated for nutritional and health aspects as well as for economic benefits. Ultra-Performance Liquid Chromatography Mass Spectrometry (UPLC-MS) was employed to trace the geographical origin of Argan kernels based on secondary-metabolite profiles. One-hundred and twenty Argan fruit kernels from five regions ('Agadir', 'Ait-Baha' 'Essaouira', 'Tiznit' and 'Taroudant') were studied. Characterization and quantification of 36 secondary metabolites (33 polyphenolic and 3 non-phenolic) were achieved. Those metabolites are highly influenced by the geographic origin. Then, the untargeted UPLC-MS fingerprint was decomposed by metabolomic data handling tools, such as multivariate curve resolution alternating least squares (MCR-ALS) and XCMS. The two resulting data matrices were pretreated and prepared separately by chemometric tools and then two data fusion strategies (low- and mid-levels) were applied on them. The four data sets were comparatively investigated. Principal component analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA), and Soft Independent Modeling of Class Analogies (SIMCA) were used to classify samples. The exploration or classification models demonstrated a good ability to discriminate and classify the samples in the geographical-origin based classes. Summarized, the developed fingerprints and their metabolomics-based data handling successfully allowed geographical traceability evaluation of Moroccan Argan kernels.
阿甘（Argania spinosa L.）果实的核仁成分研究较少，研究强度也低于其产生的阿甘油。摩洛哥阿甘核仁富含代谢物，可从营养和健康以及经济利益方面进行研究。超高效液相色谱-质谱联用技术（UPLC-MS）用于根据次生代谢物图谱追踪阿甘核仁的地理来源。研究了来自五个地区（阿加迪尔、艾特本哈、埃萨乌伊拉、提兹尼特和塔鲁丹特）的 120 个阿甘果实核仁。实现了 36 种次生代谢物（33 种多酚和 3 种非酚类化合物）的特征描述和定量。这些代谢物高度受地理来源的影响。然后，采用代谢组学数据处理工具（如多变量曲线分辨交替最小二乘法（MCR-ALS）和 XCMS）对非靶向 UPLC-MS 指纹图谱进行分解。对两个结果数据矩阵分别采用化学计量学工具进行预处理和准备，然后对它们应用两种数据融合策略（低级和中级）。比较研究了这四个数据集。采用主成分分析（PCA）、偏最小二乘判别分析（PLS-DA）和软独立建模分类法（SIMCA）对样品进行分类。探索或分类模型能够很好地区分和分类基于地理起源的样品。总之，所开发的指纹图谱及其基于代谢组学的数据处理成功地实现了对摩洛哥阿甘核仁的地理溯源评估。
