Identification of Cannabis sativa L. (hemp) Retailers by Means of Multivariate Analysis of Cannabinoids

• Published in: Molecules (Basel, Switzerland) Vol. 24; no. 19; p. 3602
• Main Authors: Palmieri, Sara, Mascini, Marcello, Ricci, Antonella, Fanti, Federico, Ottaviani, Chiara, Lo Sterzo, Claudio, Sergi, Manuel
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 07.10.2019; MDPI
• Subjects: Acids; Calibration; Cannabidiol; Cannabinoids; Cannabinoids - analysis; Cannabinoids - chemistry; Cannabis; Cannabis - chemistry; Cannabis sativa; cannabis sativa l; Chromatography, High Pressure Liquid - methods; Classification; Datasets; Discriminant analysis; Forensic science; Forensic Sciences - methods; Hemp; High performance liquid chromatography; hplc-ms/ms analysis; Italy; Least-Squares Analysis; Liquid chromatography; Marijuana; Mass spectrometry; Mass spectroscopy; Multivariate Analysis; partial least squares discriminant analysis (pls-da); Phytochemicals; Principal components analysis; Retail stores; Seeds; Tandem Mass Spectrometry - methods; Tetrahydrocannabinol; THC; Variance analysis; Italy; cannabinoids; Cannabis sativa L; partial least squares discriminant analysis (PLS-DA); multivariate analysis; HPLC-MS/MS analysis
• DOI: 10.3390/molecules24193602
• ISSN: 1420-3049

In this work, the concentration of nine cannabinoids, six neutral cannabinoids (THC, CBD, CBC, CBG, CBN and CBDV) and three acidic cannabinoids (THCA CBGA and CBDA), was used to identify the Italian retailers of L. (hemp), reinforcing the idea that the practice of categorizing hemp samples only using THC and CBD is inadequate. A high-performance liquid chromatography/high-resolution mass spectrometry (HPLC-MS/MS) method was developed for screening and simultaneously analyzing the nine cannabinoids in 161 hemp samples sold by four retailers located in different Italian cities. The hemp samples dataset was analyzed by univariate and multivariate analysis with the aim to identify the hemp retailers without any other information on the hemp samples like strains, seeds, soil and cultivation characteristics, geographical origin, product storage, etc. The univariate analysis highlighted that the hemp samples could not be differentiated by using any of the nine cannabinoids analyzed. To evaluate the real efficiency of the discrimination among the four hemp retailers a partial least squares discriminant analysis (PLS-DA) was applied. The PLS-DA results showed a very good discrimination between the four hemp retailers with an explained variance of 100% and low classification errors in both calibration (5%) and cross validation (6%). A total of 92% of the hemp samples were correctly classified by the cannabinoid variables in both fitting and cross validation. This work contributed to show that an analytical method coupled with multivariate analysis can be used as a powerful tool for forensic purposes.