Identification of Pesticides Using Experimental and Computational Approaches based on Ion Mobility Mass Spectrometry Measurements

Abstract

Mass spectrometry is known to be a powerful analytical technique due its high sensitivity, specificity, speed, and versatility. It has the capability of detecting and identifying numerous analytes in sample matrices. In addition, several complementary techniques have been coupled to mass spectrometry for improved analyte identification such as chromatography and ion mobility. This thesis focuses on (i) synergistic experimental (LC-MS/MS) and computational (DFT calculations) studies on the fragmentation pattern of two types of organophosphate pesticides for the prediction of possible degradation products. (ii) identifying pesticides and degradation products using experimental approaches; liquid chromatography ion mobility mass spectrometry (LC-IM-MS) and paper spray ion mobility mass spectrometry (PS-IM-MS) and supporting the experimental findings with theoretical calculations. Tandem liquid chromatography-mass spectrometry (LC-MS/MS) is typically utilized in the analysis of pesticides and degradation products. However, this technique is costly and time consuming. Also, some degradation products (which are often overlooked during screening of pesticides in food) of pesticides are reported to be more harmful than the parent pesticides. Hence, there is a need for rapid and efficient techniques for the screening of pesticides, and their degradation products in agricultural produce. Isomeric and non-isomeric parent pesticides and their degradation products were screened using LC-IM-MS. Due to time constraints of the LC method (time-consuming), an alternative rapid method, PS-IM-MS, was employed for the screening and identification of the analytes using their accurate mass measurements and collision cross-section values. The ion mobility separation method was utilized to investigate structures of parent pesticides and their corresponding degradation products. The structures were identified by comparing the measured collision cross section values with those predicted from DFT computations.