Technical Center of Dalian Customs, Dalian 116000, China.
Molecules. 2022 Jul 23;27(15):4711. doi: 10.3390/molecules27154711.
The metabolomics approach has proved to be promising in achieving non-targeted screening for those unknown and unexpected (U&U) contaminants in foods, but data analysis is often the bottleneck of the approach. In this study, a novel metabolomics analytical method via seeking marker compounds in 50 pharmaceutical and personal care products (PPCPs) as U&U contaminants spiked into lettuce and maize matrices was developed, based on ultrahigh-performance liquid chromatography-tandem mass spectrometer (UHPLC-MS/MS) output results. Three concentration groups (20, 50 and 100 ng mL) to simulate the control and experimental groups applied in the traditional metabolomics analysis were designed to discover marker compounds, for which multivariate and univariate analysis were adopted. In multivariate analysis, each concentration group showed obvious separation from other two groups in principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) plots, providing the possibility to discern marker compounds among groups. Parameters including S-plot, permutation test and variable importance in projection (VIP) in OPLS-DA were used for screening and identification of marker compounds, which further underwent pairwise -test and fold change judgement for univariate analysis. The results indicate that marker compounds on behalf of 50 PPCPs were all discovered in two plant matrices, proving the excellent practicability of the metabolomics approach on non-targeted screening of various U&U PPCPs in plant-derived foods. The limits of detection (LODs) for 50 PPCPs were calculated to be 0.42.0 µg kg and 0.32.1 µg kg in lettuce and maize matrices, respectively.
代谢组学方法已被证明在实现食品中未知和意外（U&U）污染物的非靶向筛选方面具有广阔的应用前景，但数据分析往往是该方法的瓶颈。在本研究中，基于超高效液相色谱-串联质谱（UHPLC-MS/MS）的输出结果，开发了一种通过寻找 50 种药品和个人护理产品（PPCPs）中作为 U&U 污染物的标记化合物来分析植物基质中污染物的代谢组学新方法。设计了三个浓度组（20、50 和 100ng/mL）来模拟传统代谢组学分析中的对照组和实验组，以发现标记化合物，采用多元和单变量分析方法。在多元分析中，每个浓度组在主成分分析（PCA）和正交偏最小二乘判别分析（OPLS-DA）图中与其他两个组明显分离，为组间标记化合物的鉴别提供了可能性。OPLS-DA 中的 S-plot、置换检验和变量重要性投影（VIP）参数用于筛选和鉴定标记化合物，进一步进行了单变量分析的两两检验和倍数变化判断。结果表明，两种植物基质中均发现了代表 50 种 PPCPs 的标记化合物，证明了代谢组学方法在植物源性食品中对各种 U&U PPCPs 的非靶向筛选具有优异的实用性。50 种 PPCPs 的检出限（LOD）在生菜和玉米基质中分别为 0.42.0μg/kg 和 0.32.1μg/kg。
