This report summarizes a research project regarding analysis of cannabinoids and samples with more complicated matrices, such as edibles and topicals, through the use of liquid chromatography or ultra-high performance liquid chromatography; the document lays out the research methodology, data collection and analysis, and results.
This document reports on a project that had six major goals that dealt progressively with higher number of cannabinoids (from 12 to 20) and samples with more complicated matrices (concentrates, plant materials, edibles, and topicals) using either liquid chromatography (LC) or ultra-high performance liquid chromatography (UHPLC) to separate the cannabinoids and diode array detector (DAD), electrospray ionization time-of-flight mass spectrometry (ESI/TOFMS) or electrospray ionization tandem mass spectrometry (ESI/MS/MS) to detect the cannabinoids. The six major goals of the project were: the development of a validated method for high throughput quantification of 12 cannabinoids in hemp oil using LC-UV; the development of a validated UHPLC-DAD method with optional ESI-TOFMS detection for rapid quantification of up to 16 cannabinoids in hemp concentrates; the development of a validated LC-DAD method with optional ESI/TOFMS for high throughput quantification of up to 20 cannabinoids in plant materials in cannabis; the validation of a high throughput LC-ESI/MS/MS method for accurate measurement of a Δ9-THC and a Δ9-THCA among 18 cannabinoids in plant materials of cannabis; the development of a validated UHPLC-DAD method with optional ESI/TOFMS detection for rapid quantification of up to 16 cannabinoids in hemp infused products; and the validation of high throughput UHPLC-ESI/MS/MS method for quantification of up to 18 cannabinoids in hemp infused products. Six research questions guided the project. The report describes the research design, methodology, analytical and data analysis techniques, and research results. A discussion of outcomes and limitations precedes the listing of project artifacts, data sets generated, and dissemination activities.