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Analytical and Bioanalytical Chemistry

, Volume 409, Issue 12, pp 3153–3163

First Online: 23 February 2017Received: 28 November 2016Revised: 23 January 2017Accepted: 10 February 2017DOI: 10.1007-s00216-017-0256-3

Cite this article as: Mudge, E.M., Murch, S.J. & Brown, P.N. Anal Bioanal Chem 2017 409: 3153. doi:10.1007-s00216-017-0256-3

Abstract

There is an explosion in the number of labs analyzing cannabinoids in marijuana Cannabis sativa L., Cannabaceae but existing methods are inefficient, require expert analysts, and use large volumes of potentially environmentally damaging solvents. The objective of this work was to develop and validate an accurate method for analyzing cannabinoids in cannabis raw materials and finished products that is more efficient and uses fewer toxic solvents. An HPLC-DAD method was developed for eight cannabinoids in cannabis flowers and oils using a statistically guided optimization plan based on the principles of green chemistry. A single-laboratory validation determined the linearity, selectivity, accuracy, repeatability, intermediate precision, limit of detection, and limit of quantitation of the method. Amounts of individual cannabinoids above the limit of quantitation in the flowers ranged from 0.02 to 14.9% w-w, with repeatability ranging from 0.78 to 10.08% relative standard deviation. The intermediate precision determined using HorRat ratios ranged from 0.3 to 2.0. The LOQs for individual cannabinoids in flowers ranged from 0.02 to 0.17% w-w. This is a significant improvement over previous methods and is suitable for a wide range of applications including regulatory compliance, clinical studies, direct patient medical services, and commercial suppliers.

KeywordsGreen chemistry Single-laboratory validation Cannabis Cannabinoids Medical marijuana Electronic supplementary materialThe online version of this article doi:10.1007-s00216-017-0256-3 contains supplementary material, which is available to authorized users.





Author: Elizabeth M. Mudge - Susan J. Murch - Paula N. Brown

Source: https://link.springer.com/







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