Suspect screening strategy for pesticide application history based on characteristics of trace metabolites

Screening and identification of unknown chemical contaminants in food based on liquid chromatography-high-resolution mass spectrometry and machine learning

Unknown or unexpected chemical contaminants and/or their transformation products in food that may be harmful to humans need to be discovered for comprehensive safety evaluation. Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is a powerful tool for detecting chemical contaminants in food samples. However, identifying all of peaks in LC-HRMS is not possible, but if class information is known in advance, further identification will become easier. In this work, a novel MS2 spectra classification-driven screening strategy was constructed based on LC-HRMS and machine learning. First, the classification model was developed based on machine learning algorithm using class information and experimental MS2 data of chemical contaminants and other non-contaminants. By using the developed artificial neural network classification model, in total 32 classes of pesticides, veterinary drugs and mycotoxins were classified with good prediction accuracy and low false-positive rate. Based on the classification model, a screening procedure was developed in which the classes of unknown features in LC-HRMS were first predicted through the classification model, and then their structures were identified under the guidance of class information. Finally, the developed strategy was tentatively applied to the analysis of pork and aquatic products, and 8 chemical contaminants and 11 transformation products belonging to 8 classes were found. This strategy enables screening of unknown chemical contaminants and transformation products in complex food matrices.

Identification and Coexposure of Neonicotinoid Insecticides and Their Transformation Products in Retail Cowpea (Vigna unguiculata)

There is growing evidence that the transformation products of emerging contaminants in foodstuffs may pose a health risk to humans. However, the exact identities, levels, and estimated dietary intake (EDI) of neonicotinoid transformation products in crops remain poorly understood. We established an extended suspect screening strategy to investigate neonicotinoid insecticides and their transformation products in retail cowpea from 11 cities in Hainan Province, China. Forty-nine transformation products were identified in retail cowpea, of which 22-36 were found in 98.6% of the samples. Notably, 31 new transformation products were derived from new processes or a combination of different transformation processes. The mean concentrations of neonicotinoids and nine of the transformation products (with authentic standards) were in the ranges of 0.0824-5.34 and 0.0636-1.50 ng/g, respectively. The cumulative EDIs of the quantified transformation products were lower than those of parent neonicotinoids with the exception of clothianidin desmethyl, which had a ratio of 1157%. However, the coexistence of the other 40 transformation products (without authentic standards) in cowpea suggested that the exposure risk from all of the transformation products might be higher. This study demonstrated that pesticide transformation products should be considered in food chain risk assessments and included in future regulatory management.