Date of Award
Fall 12-15-2025
Document Type
Thesis
Degree Name
Master of Science (MS)
Department/Program
Forensic Science
Language
English
First Advisor or Mentor
Ana Pego
Second Reader
Marta Concheiro-Guisan
Third Advisor
Emanuele Alves
Abstract
Synthetic cannabinoids represent one of the most prevalent and high-risk classes of New Psychoactive Substances (NPS), continuously evolving in structure and potency. Their widespread presence in seized materials and toxicology casework underscores the growing need for reliable analytical methods capable of detecting these compounds in biological matrices. Hair offers several advantages for drug testing, including a long detection window, minimal invasiveness, and resistance to adulteration; however, detecting synthetic cannabinoids in hair remains challenging due to their low incorporation levels and structural diversity. This study focuses on the method development and optimization of an LC-MS/MS technique for the detection of selected synthetic cannabinoids in hair. The work includes systematic optimization of sample preparation, extraction efficiency, chromatographic separation, and mass spectrometric conditions. Methanol was identified as the most effective extraction solvent, yielding the highest recoveries when paired with PVDF filtration and Falcon tubes, which minimized analyte loss from surface adsorption. The final LC-MS/MS conditions, employing an EVO C18 column and a 50:50 MPA: MPB reconstitution solvent, provided strong separation, stable retention, and excellent sensitivity for all targeted analytes. Together, these parameters produced the most robust and consistent analytical performance. The goal of this research was to develop a sensitive, robust, and efficient method for detecting synthetic cannabinoids in hair, contributing to forensic toxicology and improving the interpretation of drug exposure in real-world cases.
Recommended Citation
Obeng, Linda K., "Method Development and Optimization for the Detection of Synthetic Cannabinoids in Hair using LC-MSMS" (2025). CUNY Academic Works.
https://academicworks.cuny.edu/jj_etds/371
