Nontargeted LC-HRMS-Based Strategies as a Tool to Study the Behavior of Chemical Attribution Signatures of
Carla Orlandi1,2, Grégoire Delaporte3, Christine Albaret3
1Univ Toulouse , EI-Purpan, ENVT, INRAE, Toxalim, ToulouseF-31027, France.
Abstract:
In chemical forensics, chemical source inference is crucial for the determination of the origin of toxic agents through various analytical and chemometric methodologies. Recent advancements in the study of organophosphorus compounds as nerve agents have mainly focused on the identification of specific production pathways and source materials. Using Chlorpyrifos as a model organophosphorus compound and previously generated data, the present work aims to assess the robustness of the approach for the discrimination of chemical production routes by introducing environmental matrix complexity. Various crude Chlorpyrifos synthesis samples were spiked onto three complex environmental matrices (sand, soil, and surface river water) to simulate real-case samples. Experiments were carried out at two concentrations (20 ng/μL and 1 ng/μL) in crude samples. Sampling and extraction protocols were developed, and recovery rates and matrix effects were assessed to track down impurity attribution signatures linked to the synthetic pathways from each matrix. Ultra-high-performance liquid chromatography (UHPLC) coupled to a hybrid trapped ion mobility-quadrupole time-of-flight system (TIMS-Q-ToF) was used in a metabolomics-based analytical strategy. Synthetic routes for contaminated environmental samples were predicted using a training data set from various raw Chlorpyrifos synthetic pathways. Precision and the number of discriminant features in samples from Non-Targeted Analysis (NTA) versus Suspect Screening Analysis (SSA) approaches were evaluated with confusion matrices and univariate analysis to assess the relevance of the features employed. Finally, highly concentrated explosive residues were added to evaluate analytical perturbance. This application in complex environmental conditions represents a key step toward validating the proof of concept's transferability and exploring its analytical limitations.
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