Journal:Identification of Cannabis sativa L. (hemp) retailers by means of multivariate analysis of cannabinoids
| Full article title | Identification of Cannabis sativa L. (hemp) retailers by means of multivariate analysis of cannabinoids |
|---|---|
| Journal | Molecules |
| Author(s) |
Palmieri, Sara; Mascini, Marcello; Ricci, Antonella; Fanti, Federico; Ottaviani, Chiara; Sterzo, Claudo L.; Sergi, Manuel |
| Author affiliation(s) | University of Teramo |
| Primary contact | Email: msergi at unite dot it |
| Editors | Nikas, Spyros P. |
| Year published | 2019 |
| Volume and issue | 24(19) |
| Page(s) | 3602 |
| DOI | 10.3390/molecules24193602 |
| ISSN | 1420-3049 |
| Distribution license | Creative Commons Attribution 4.0 International |
| Website | https://www.mdpi.com/1420-3049/24/19/3602/htm |
| Download | https://www.mdpi.com/1420-3049/24/19/3602/pdf (PDF) |
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This article should be considered a work in progress and incomplete. Consider this article incomplete until this notice is removed. |
Abstract
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 Cannabis sativa 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 associated hemp retailers without using any other information on the hemp samples such as Cannabis 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 very good discrimination between the four hemp retailers, with an explained variance of 100% and few 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 helps to show that an analytical method coupled with multivariate analysis can be used as a powerful tool for forensic purposes.
Keywords: Cannabis sativa L., HPLC-MS/MS analysis, cannabinoids, multivariate analysis, partial least squares discriminant analysis (PLS-DA)
References
Notes
This presentation is faithful to the original, with only a few minor changes to presentation. Some grammar and punctuation was cleaned up to improve readability. In some cases important information was missing from the references, and that information was added.
