Hamburg School of Food Science, Institute of Food Chemistry, University of Hamburg, Grindelallee 117, 20146 Hamburg, Germany.
J Agric Food Chem. 2020 Dec 9;68(49):14343-14352. doi: 10.1021/acs.jafc.0c01204. Epub 2020 Apr 13.
The low reproducibility of non-targeted liquid chromatography-mass spectrometry-based metabolomics approaches represents a major challenge for their implementation in routine analyses, because it is impossible to compare individual measurements directly with each other, if they were not analyzed in the same batch. This study describes a normalization process based on housekeeping metabolites in plant-based raw materials, which are present in comparatively constant concentrations and are subject to no or only minor deviations as a result of exogenous influences. As a model, an authenticity study was selected to determine the origin of white asparagus (). Using three model data sets and one test data set, we were able to show that samples that have been measured independently of one another can be correctly assigned in terms of origin after the normalization with housekeeping metabolites. The procedure does not require internal standards or the measurements of further reference samples and can also be applied to other matrices and scientific issues.
基于非靶向液相色谱-质谱的代谢组学方法的低重现性是其在常规分析中应用的主要挑战,因为如果不在同一批次中进行分析,则不可能直接比较各个测量值。本研究描述了一种基于植物性原料中管家代谢物的归一化过程,这些代谢物的浓度相对恒定,并且由于外源性影响,其变化很小或没有变化。作为一个模型,选择了一个真实性研究来确定白芦笋的产地()。使用三个模型数据集和一个测试数据集,我们能够表明,经过管家代谢物归一化后,即使是彼此独立测量的样品,也可以根据其起源进行正确分配。该程序不需要内标物或进一步参考样品的测量,也可以应用于其他基质和科学问题。