Metabolomics Approach on Non-Targeted Screening of 50 PPCPs in Lettuce and Maize

Abstract

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−1) 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 t-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 0.4~2.0 µg kg−1 and 0.3~2.1 µg kg−1 in lettuce and maize matrices, respectively.

A Support Vector Machine-Assisted Metabolomics Approach for Non-Targeted Screening of Multi-Class Pesticides and Veterinary Drugs in Maize

“…The metabolomics method has successfully been applied to non-targeted contaminant screening in plant-derived foods such as tea [27], lettuce [29], maize [29], and orange juice [33]. In contrast to these studies, which only focused on pesticides or veterinary drugs, our study expands the domain of target compounds to simultaneously screen multi-class P&VDs.…”

“…Metabolomics has shown great promise in screening unexpected contaminants in the food safety field due to its advantages in handling massive data with complicated characteristics, such as small sample sizes, mass interferents, and high noise [25][26][27][28][29]. Marker compounds on behalf of unexpected contaminants in foods were screened and identified by in-house or network databases (e.g., Massbank, SciFinder, ChemSpider, PubChem, and Metlin) during metabolomics analysis in order to achieve non-targeted screening of contaminants [28,29].…”