Journal:Identification of Cannabis sativa L. (hemp) retailers by means of multivariate analysis of cannabinoids

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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)

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 chromatographytandem 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)

Introduction

In recent years, Cannabis sativa L. has become one of the most studied plants around the world.[1] Cannabis sativa L is a chemically complex plant which contains several classes of natural compounds, e.g., flavonoids, mono- and sesquiterpenes, steroids, nitrogenous compounds, and cannabinoids, associated with the medicinal properties of the plant.[2][3]

The main cannabinoid constituents are Δ9-tetrahydrocannabinol (THC), which possesses significant psychotropic properties, and other compounds with less or no psychotropic activity. This includes neutral cannabinoids like cannabidiol (CBD), cannabigerol (CBG), cannabichromene (CBC), cannabinol (CBN), and cannabidivarin (CBDV), as well as acidic cannabinoids like tetrahydrocannabinolic acid (THCA), cannabidiolic acid (CBDA), and cannabigerolic acid (CBGA). The species sativa is recognized as a monotypic classification that can be divided into different chemotypes based on the specific cannabinoid profile.[4][5]

The interest in Cannabis sativa L. has increased in Italy mainly due to December 2016 legislation (Legge 2 Dicembre 2016, n. 242). The legislation uses the concentration of THC to classify two types of Cannabis sativa L.: a fiber-type plant (hemp) with low levels of THC (<0.2% w/w) and a prohibited drug-type plant where the level of THC is >0.6% w/w.[6] There are hundreds of hemp strains available in the marketplace based on aroma, plant size, different cultivation, characteristic of the soil, and overall yield.[4][7][8] Therefore, there is a request to develop cost effective and easy-to-use quantitative and qualitative methods for the identification and classification of hemp products.[6]

THC and CBD are the traditional reference cannabinoids to extrapolate the phytochemical composition of hemp, but different works have proved that strains with similar THC/CBD content have different effects on human physiology.[9][10][11]

The present work focused on examining the chemical content of cannabinoids as a convenient tool to identify retailers of hemp without knowing any other relevant information about the sample, including strain, seed, soil and cultivation characteristics, geographical origin, product storage, etc. Moreover, the results of this work support the theory that using only concentrations of THC and CBD is not enough to discriminate among samples of commercial hemp.[9] The identification and quantification of cannabinoids was carried out by means of high-performance liquid chromatographytandem mass spectrometry (HPLC-MS/MS), overcoming the issue with phytocannabinoid decomposition due to heating found in methods like gas chromatography (GC).[12][13]

A partial least squares discriminant analysis (PLS-DA) approach correctly identified the hemp retailer associated with a sample by examining nine specific cannabinoids of 161 hemp samples, showing that an analytical method coupled with multivariate analysis can lead to a powerful tool for forensic purposes. Additionally encouraging is that such chemometric models have already been used to understand phytochemical diversity, showing the advantages of multivariate analysis.[9][14]

A number of works in the literature have reported on methods for the determination of THC and CBD concentration in hemp samples[8][15]; however, to the best of our knowledge, there is a lack of information regarding the evaluation of the comprehensive cannabinoid profile of hemp products with HPLC-MS/MS.[7][16][17]

Results and discussion

The dataset used for both univariate and multivariate analysis (see Table S1 in the supplementary material) was composed of 161 hemp samples processed in 2018 from four Italian retailers. The concentration of the the six neutral cannabinoids (THC, CBD, CBC, CBG, CBN, and CBDV) and the three acidic cannabinoids (THCA, CBGA, and CBDA) was determined for all of the samples.

As reported in Table 1, the hemp samples were sold in four Italian cities from three Italian regions. The hemp samples were acquired without any strain, seed, soil and cultivation characteristics, geographical origin, product storage, etc. information reported with the acquisition.

Table 1. Summary of the dataset used in this work. The 161 hemp samples were classified as sold by the four Italian retailers. Region and city of retailers were also reported.
Retailer Number of samples Region City Label
A 63 Lombardy Milan A1–63
B 43 Lombardy Mantova B1–43
C 38 Lazio Pomezia C1–38
D 17 Abruzzo Tortoreto D1–17

Data vectors belonging to the same hemp retailer were firstly evaluated by the analysis of variance via the graphical representation of a box and whisker plot, and afterwards, by two multivariate techniques, the unsupervised principal component analysis (PCA) and supervised PLS-DA.

As shown in Figure 1, the use of the nine cannabinoid variables led to no statistical differences between the hemp samples grouped as sold by the hemp retailers. Except for the high average concentration of THCA and CBGA found in the samples of retailer D, the cannabinoid analytical profile was in all cases the same, with high average concentrations of CBD and CBDA and low average concentrations of the other seven cannabinoids. CBN average concentration was found particularly high in the samples of retailers A and B. Interestingly, CBN is an oxidation product of THCA, and the high content of this cannabinoid can also be a marker of inflorescence quality. The average concentration of THC was in all cases below 0.35% w/w.



[18]

References

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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.