Classification of Mass Spectral Data to Assist in the Identification of Novel Synthetic Cannabinoids.

ABSTRACT: Detection and characterization of newly synthesized cannabinoids (NSCs) is challenging due to the lack of availability of reference standards and chemical data. In this study, a binary classification system was developed and validated using partial least square discriminant analysis (PLS-DA) by utilizing readily available mass spectral data of known drugs to assist in the identification of previously unknown NCSs. First, a binary classification model was developed to discriminate cannabinoids and cannabinoid-related compounds from other drug classes. Then, a classification model was developed to discriminate classical (THC-related) from synthetic cannabinoids. Additional models were developed based on the most abundant functional groups including core groups such as indole, indazole, azain

SUBMITTER: Evans-Newman KC 

PROVIDER: S-EPMC11478242 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature