Journal:Chemical profiling and characterization of different cultivars of Cannabis sativa L. inflorescences by SPME-GC-MS and UPLC-MS
| Full article title | Chemical profiling and characterization of different cultivars of Cannabis sativa L. inflorescences by SPME-GC-MS and UPLC-MS |
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| Journal | Separations |
| Author(s) | Cicaloni, Vittoria; Salvini, Laura; Vitalini, Sara; Garzoli, Stefania |
| Author affiliation(s) | Toscana Life Sciences Foundation, Università degli Studi di Milano, Sapienza University |
| Primary contact | Email: v dot cicaloni at toscanalifesciences dot org |
| Editors | Miguel Ángel Rodríguez-Delgado |
| Year published | 2022 |
| Volume and issue | 9(4) |
| Article # | 90 |
| DOI | 10.3390/separations9040090 |
| ISSN | 2297-8739 |
| Distribution license | Creative Commons Attribution 4.0 International |
| Website | https://www.mdpi.com/2297-8739/9/4/90/htm |
| Download | https://www.mdpi.com/2297-8739/9/4/90/pdf?version=1648894245 (PDF) |
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Abstract
The chemical profile of Cannabis sativa L. female inflorescences is rather complex, being characterized by a large number of molecules belonging to different chemical classes. Considering the numerous applications of cannabis in various fields—including the medical and pharmaceutical sectors, which have seen an increasing use for the Cannabis genus in recent years—a precise characterization of the matrices is essential. In this regard, the application of adequate and suitable sampling and analysis techniques becomes important in order to provide an identification of the metabolites characterizing the profile of the sample under examination.
The goal of this work is to provide additional information on the chemical composition of the inflorescences of five different C. sativa cultivars grown in Emilia Romagna (Italy) through the application of sophisticated analysis techniques such as solid-phase microextraction gas chromatography–mass spectrometry (SPME-GC-MS) and ultra-performance liquid chromatography–mass spectrometry (UPLC-MS). The obtained data highlighted the presence of a high number of volatile and non-volatile compounds, thus allowing a comparative evaluation of the different samples. Furthermore, an in-depth statistical survey by principal component analysis (PCA) with heat maps, hierarchical cluster analysis (HCA), and partial least squares discriminant analysis (PLS-DA-VIP) was conducted to consider any correlations between the investigated cultivars. The findings of this study may help to provide more information on the C. sativa inflorescences and prove useful for potential applications of their metabolites in scientific research.
Keywords: cannabinoids, non-cannabinoids, volatile and non-volatile compounds, chromatographic analyses, multivariate statistical analysis
Introduction
Cannabis sativa L. (Cannabaceae) was one of the first non-food crops to be cultivated. It is an annual flowering herbaceous plant native to temperate central Asia, where its use seems to date back to around 4500 B.C. [1,2] This species is found in different habitats ranging from sea level to the temperate and alpine foothills of the Himalayas. [3] Its domestication probably occurred independently in several centers of East Asia in early Neolithic times. [4] Around 1000 B.C., it rapidly spread throughout Asia and Europe following the migration of nomads and the movements of traders. [2,5] C. sativa has a long history as a medicinal plant, used, for example, in traditional Tibetan and Ayurvedic medicine, as well as in social and religious rituals. The strong, mildew-resistant fiber has long been used by humans in the construction of ropes and sails. In China, the seeds are still commonly eaten roasted or raw. [6,7] In recent years, renewed interest in the therapeutic effects of C. sativa has led to legitimate medicinal use through clinical studies demonstrating its efficacy. [8] Moreover, C. sativa is currently an agricultural commodity grown to be used in the production of foods and beverages, nutritional supplements, cosmetics and personal care products, textiles, paper, insulation materials, and other manufactured goods. [9,10]
From a chemical point of view, C. sativa is a complex species with numerous (>500) reported secondary metabolites—both cannabinoid and non-cannabinoid constituents—obtained from all plant parts (i.e, leaves, flowers, bark, seeds, and roots). [11,12,13,14] The former are a specific chemical class found in the Cannabis genus, a group of compounds with a characteristic C21 terpenophenolic backbone and divided into several sub-classes. [15] The latter belong to various chemical classes including alkaloids, flavonoids, non-cannabinoid phenols, terpenes, and more. [12,15,16,17] While the stems provide cellulosic and woody fibers, and the seeds are exploited in the feed and food industry for their high content of fatty acids and proteins, the leaves and inflorescences of C. sativa are a rich source of phytochemicals. [18]
In this work, the inflorescences of five organic C. sativa commercial cultivars were chemically investigated. In particular, V1 CBD, Banana Hybrid, Green Poison, Candy BUD, and Gorilla CBD (Figure 1) were analyzed, all with a high cannabidiol content and characterized by medium/large sized buds that are compact and containing a good amount of resin. The samples were obtained from greenhouse crops located in the Tuscan-Emilian Apennines (Italy), where the entire processing cycle is carried out strictly by hand and the tanning process is particularly long in order to obtain and enhance their unique taste. In detail, Green Poison gives off an unusual smell due to contrasting sugary and bitter aromas, leaving a very pleasant aftertaste. As the name suggests, Banana Hybrid releases sweet banana fragrances. Both V1 CBD and Candy BUD have a dense and particularly fragrant smell, with fruity notes reminiscent of citrus fruits. Gorilla CBD smells of grass and pine. [19] With the aim of providing a detailed description of their chemical composition, we applied solid-phase microextraction gas chromatography–mass spectrometry (SPME-GC-MS) and ultra-performance liquid chromatography–mass spectrometry (UPLC-MS) techniques. The findings were useful for carrying out a comparative assessment also thanks to a sophisticated statistical survey that highlighted qualitative and quantitative differences in volatile and non-volatile chemical profiles.
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Materials and methods
Materials
The inflorescences of C. sativa were a kind gift from the Apennino Farm—Gaggio Montano (BO) 40041, Italy. The inflorescences were high-quality samples collected from mature female plants of five cultivars grown in full compliance with the law and were grown without the use of pesticides or chemical additives. After slow drying, they were delivered to the laboratory during January 2021, packed in paper bags (5.0 g each), and stored in a dry and dark place until use. The voucher specimens (No. CSAFBO 130-134) were deposited at the Department of Agricultural and Environmental Sciences of the Milan State University (Milan, Italy).
Formic acid, acetonitrile, ethanol, and water were purchased from Sigma–Aldrich (Milan, Italy), and they were all liquid chromatography–mass spectrometry (LC-MS) grade.
SPME sampling
To investigate the volatile chemical composition of the C. sativa inflorescences, an SPME sampling technique was used as described by Vitalini et al. [20], with some modifications. About 2 g of each variety were placed inside a 20 mL glass vial with PTFE-coated silicone septum. A SPME device from Supelco (Bellefonte, PA, USA) with 1 cm fiber coated with 50/30 μm DVB/CAR/PDMS (divinylbenzene/carboxen/polydimethylsiloxane) was used to obtain the volatiles extraction. Before use, the fiber was conditioned at 270 °C for 30 minutes. The equilibration time for all samples of inflorescence was obtained heating to 50 °C for 10 minutes. After this time, the fiber was exposed to the headspace of the samples for 30 minutes at 50 °C to capture and concentrate the volatiles. Lastly, the SPME fiber was inserted in the gas chromatography (GC) injector maintained at 250 °C in split mode (1:20) for desorption of the collected compounds.
GC-MS analysis
The analyses of the headspace from C. sativa inflorescences were carried out on a Clarus 500 model Perkin Elmer (Waltham, MA, USA) gas chromatograph coupled with a mass spectrometer equipped with a FID (flame detector ionization). The capillary column was a Varian Factor Four VF-1. The GC and MS parameters were as described by Iannone et al. [21] Briefly, the oven-programmed temperature was set initially at 60 °C and then increased to 220 °C at 6°/minute and finally held for 15 minutes. Helium was used as carrier gas at a constant rate of 1 mL/minute. MS detection was performed with electron ionization (EI) at 70 eV operating in the full-scan acquisition mode in the m/z range 40–500 amu. The volatile compounds were identified by the comparison of the MS-fragmentation pattern of the analytes with those of pure components stored in the Wiley 2.2 and NIST/EPA/NIH Mass Spectral Library (NIST 02) database. Further, the linear retention indices (LRIs) were calculated using a series of alkane standards (C8–C25 n-alkanes) analyzed under the same chromatographic conditions described above. LRIs were then compared with available retention data reported in the literature. The relative amounts of the components were expressed as percent peak area relative to total peak area without the use of an internal standard and any factor correction. All analyses were carried out in triplicate.
UPLC-MS analysis
All the samples were analyzed using an Ultimate 3000 UPLC system (Thermofisher Scientific) that was controlled with Thermo Xcalibur software (Thermo Fisher Scientific, Waltham, MA, USA). The samples were prepared by following this method: 100 mg of sample powder was ultrasonicated for 30 minutes with 5 mL of 70% ethanol, followed by centrifugation (15,000 rpm, 4 °C) for 10 minutes. The resulting supernatant of the samples was injected into the UPLC-Q-Exactive Plus system. The samples were separated using an Acquity UPLC BEH C18 column (2.1 mm × 15 cm, 1.7 μm, Waters). The mobile phases consisted of solvent A (0.1% formic acid in water) and solvent B (0.1% formic acid in acetonitrile). The gradient started with 30% of B, that was maintained constant for three minutes. Then the organic phase was increased up to 60% in 50 minutes and then raised again up to 90% in two minutes. The phase B was maintained at 90% for another two minutes and then returned to the initial conditions. The flow rate was kept at 0.2 mL/minute, the sample injection volume was 10 μL, and the column temperature was maintained at 50 °C.
Mass spectrometry analyses were performed on a Q-Exactive Plus quadrupole-Orbitrap mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA) in negative and positive ion mode. The scan mass range was set at m/z 200–2000. HR-MS spectra were recorded in positive and negative ion mode using the following parameters: spray voltage 3.5 kV (positive) and 3.0 kV (negative), sheath gas 20 (arbitrary units), auxiliary gas 5.0 (arbitrary units), capillary temperature 320 °C, and resolution 35,000. MS/MS spectra were obtained by a higher energy collision dissociation (HCD) of 30 (arbitrary units). The accuracy error threshold was fixed at 5 ppm. The final annotated metabolome dataset was generated by Compound Discoverer 3.3 (Demo version, Thermo Fisher, Waltham, MA, USA). The retention time RT = 0.2 minutes, mass = 10 ppm, and other parameters were selected as the default values for peak extraction and peak alignment.
Statistical analysis
The resulting data matrix was imported into the MetaboAnalyst 5.0 online platform [22] and graphically displayed by using several R packages (“ComplexHeatmap” version 2.11.1, “Circlize’ version 0.4.13, and “ColorRamps” version 2.3). The obtained data were normalized by sum. First, principal component analysis was applied to provide an exploratory data analysis. PCA is effective for separating features into groups based on commonality and reports the weight of each component’s contribution to the separation. The mechanism underlying PCA is an orthogonal transformation transferring a set of correlated variables into a new set of uncorrelated variables. PCA is a preliminary step in a multivariate analysis to provide an unsupervised overview of the samples. An unsupervised PCA analysis on MetaboAnalyst 5.0 was carried out to determine how metabolites differ from each other, and which compounds contribute the most to this difference. Moreover, a hierarchical cluster analysis (HCA) was performed to obtain a dendrogram of varieties, based on Euclidean distance, using the metabolome dataset. Lastly, the differences in the metabolites were detected using partial least squares discriminant analysis (PLS-DA). The corresponding VIP values were calculated using the PLS-DA model. The VIP value represents the difference between the considered variables. A VIP value above 1.5 indicated components that play an important role in differentiating between samples. Only components with VIP > 1.5 and p < 0.05 were selected as potential markers. Additionally, a heat map was plotted to visualize the variations in potential markers for separating samples with processing times into different groups. To measure the linear correlation between datasets, a correlation matrix based on the Pearson correlation coefficient was performed by using MetaboAnalyst 5.0. The result has a value between −1 (strong inverse correlation) and 1 (strong direct correlation).
Results
GC-MS chemical composition
The SPME-chromatographic analyses allowed the identification of 31 volatile components (Table 1). Sesquiterpenoids were the predominant class of compounds in Green Poison (89.8%), V1 CBD (62.4%), Banana Hybrid (54.6%), and Candy BUD (73.6%); on the contrary, terpenoids prevailed in Gorilla CBD (68.4%). Quantitative differences were found between the investigated inflorescences. β-myrcene was the major component in V1 CBD (29.6%) and Gorilla CBD (40.8%), while β-caryophyllene was in Green Poison (34.5%), Banana Hybrid (28.9%), and Candy BUD (21.6 %). Other relevant differences concerned the percentage contents of α-pinene, humulene, and selina-3,7(11)-diene. In detail, α-pinene reached 13.7% and 19.7% in Banana Hybrid and Gorilla CBD, respectively, compared to detected percentages less than 1% in the other cultivars. The percentage values of humulene ranged from 6.0% to 11.4%, but a lower value equal to 3.1% in Gorilla BUD was recorded. In contrast, for seline-3,7(11)-diene, the lowest value was recorded in Banana Hybrid (0.7%) contrary to the higher percentage contents found in Candy BUD (16.7%) and Green Poison (17.1%). From a qualitative point of view, it is interesting to highlight the differences due to the presence of compounds in some cultivars and absent in the others. In particular, guaia-3,9-diene was detected in Green Poison (9.3%) and Candy BUD (10.2%); δ-guaiene in Banana Hybrid (6.4%) and Candy BUD (1.8%); as well as δ-cadinene only in Green Poison (2.3%) and V1 CBD (0.8%). Further similar differences, visible in Table 1, concern detected compounds with percentage values lower than 1% such as camphene, y-langene, α-santalene and α-bergamotene. On the other hand, specific compounds were characteristic of only one species such as β-eudesmene (1.2%: Banana Hybrid) and α-selinene (2.5%: Green Poison). Furthermore, other components such as γ-terpinene (0.2%), β-citronellol (0.2%) and alloaromadendrene (0.3%) were found in the Gorilla CBD cultivar and missing in the others.
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UPLC-MS chemical composition
UPLC analyses allowed the identification of 47 non-volatile compounds listed in Table 2. Among these, δ-9-cis-tetrahydrocannabinol, (-)- was the major detected compound in Green Poison (27.4%), Banana Hybrid (25.4%), Gorilla CBD (51.9%), and Candy BUD (26.7%), while its content reached 5.0% value in the V1 CBD cultivar, where the principal component was ananolignan J (14.1%).
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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.
