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  • 1 Department of Chemistry, Amman, Jordan
  • 2 Chemistry Department, Zarqa, Jordan
  • 3 Department of Chemistry and Biochemistry, USA
  • 4 National Center for Natural Products Research, USA
  • 5 Department of Pharmaceutics and Drug Delivery, USA
Open access

Abstract

In this research, cannabis varieties represent 23 USA States were assayed by GC-FID to generate their complex chemical profiles informative for plants clustering. Results showed that 45 cannabinoids and terpenoids were quantified in all plant samples, where 8 cannabinoids and 18 terpenoids were identified. Among organics, Δ9-THC, CBN (cannabinoids) and Fenchol (terpenoid) not only showed the highest levels overall contents, but also were the most important compounds for cannabis clustering. Among States, Washington, Oregon, California and Hawaii have the highest cannabis content. GC-FID data were subjected to PCA and HCA to find (1) the variations among cannabis chemical profiles as a result of growing environment, (2) to reveal the compounds that were responsible for grouping cultivars between clusters and (3) finally, to facilitate the future profile prediction and States clustering of unknown cannabis based on the chemical profile. The 23 cannabis USA States were grouped into three clusters based on only Δ9-THC, CBN, C1 and Fenchol content. Cannabis classification based on GC-profile will meet the practical needs of cannabis applications in clinical research, industrial production, patients' self-production, and contribute to the standardization of commercially-available cannabis cultivars in USA.

Abstract

In this research, cannabis varieties represent 23 USA States were assayed by GC-FID to generate their complex chemical profiles informative for plants clustering. Results showed that 45 cannabinoids and terpenoids were quantified in all plant samples, where 8 cannabinoids and 18 terpenoids were identified. Among organics, Δ9-THC, CBN (cannabinoids) and Fenchol (terpenoid) not only showed the highest levels overall contents, but also were the most important compounds for cannabis clustering. Among States, Washington, Oregon, California and Hawaii have the highest cannabis content. GC-FID data were subjected to PCA and HCA to find (1) the variations among cannabis chemical profiles as a result of growing environment, (2) to reveal the compounds that were responsible for grouping cultivars between clusters and (3) finally, to facilitate the future profile prediction and States clustering of unknown cannabis based on the chemical profile. The 23 cannabis USA States were grouped into three clusters based on only Δ9-THC, CBN, C1 and Fenchol content. Cannabis classification based on GC-profile will meet the practical needs of cannabis applications in clinical research, industrial production, patients' self-production, and contribute to the standardization of commercially-available cannabis cultivars in USA.

Introduction

The plant cannabis (Cannabis sativa L) is the most widely consumed and popular medicinal botanical drug product in the world due to its high usage and its diverse pharmacological properties [1]. Chemically, cannabis is a complex species containing large number of active constituents [1–3]. Herbal cannabis (known as marijuana), cannabis resin (hashish), and extracts of cannabis resin (hashish oil) are still the most illicit drugs in the world. About 8,000 tons of cannabis are intake in USA per year [1]. In many countries, cannabis is popular including Canada and North America [1, 2, 4]. In 2017, many USA states have legalized the medical use of cannabis, where, 38 licensed producers in Canada are authorized to produce and sell dried marijuana [4]. There has been a major increase of domestic production worldwide not only in USA and Canada, but also in Colombia, Mexico, Jamaica, and Thailand [1].

Cannabinoids and terpenoids are the active ingredients in cannabis [4, 5]. Both classes of compounds are known of their variables biological activities [6]. Terpenoids are of great interest because of their production by the plants reflects the immediate environment and they are responsible for cannabis' distinctive odor [7], whereas, cannabinoids tend to reveal genetic relationships [8].

Today, most nations worldwide regard cannabis as an illegal drug of abuse. Despite the abuse potential of cannabis and its illegal status at the federal level in the USA, research into its chemistry and pharmacology has demonstrated that it also has medicinal properties. Cannabis has a long history of human use as a medicinal plant, intoxicant, and ritual drug [9, 10]. Clinical trials into cannabis, pure cannabinoids, and synthetic analogs have demonstrated some effectiveness as analgesics for chronic neuropathic pain, appetite stimulants for cancer and AIDS patients, multiple sclerosis, pain, inflammation, depression, anticancer, palliative, epilepsy and infection [11–16]. The increased medical interest in these substances has prompted the development of various cannabis based medicines such as the oral Δ9-THC (delta-9-tetrahydrocannabinol) preparation Marinol®, a synthetic analog of Δ8-THC (delta-8-tetrahydrocannabinol) and an oral mucosal spray containing 1:1 ratio of Δ8-THC and CBD (cannabidiol) [17, 18].

There are three classification systems for cannabis. The first, is by species based on physical appearance, THC (tetrahydrocannabinol) content, and geographical origins since environmental factors and marijuana cultivated sources can induce different cannabis profiles [1, 8, 19–22]. The second classification is based on the ratio of two major cannabinoids THC and CBD which is decided by their corresponding allelic loci [23, 24]. The third is based on both cannabinoids and terpenoids for drug standardization and clinical research purposes [24]. Novotny et al. reported that data relative to the use of GC analysis of marijuana samples of different origin indicated that the chromatograms appeared to be different, so correlation between chromatographic data and geographical origin of marijuana might be possible [25]. Hazekamp et al. [26] reported the impact of changing the environmental conditions on the chemical composition and variability of terpenoids and cannabinoids in different cannabis varieties.

A wide variety of analytical techniques have been used for chemical profiling (i.e., fingerprinting) of cannabis. Thin Layer Chromatography [22], fingerprinting with HPLC [26–28], GC coupled with mass spectrometry [1] and 1HNMR have been used to fingerprint cannabis aqueous extracts and tinctures [29] as well as to chemically differentiate cannabis cultivars [30]. SFC also has been used to analyze cannabis [13, 31–34]. However, GC is the most commonly used instrument for analyzing cannabinoids and terpenoids [1, 13, 19, 20, 35]. GC has been used to differentiate cannabis from different countries, including Mexico, Colombia, Jamaica, Thailand, and the USA [1].

The current approaches for cannabis classification may be inadequate because they analyze cannabis from botanical perspectives based on only two cannabinoids; THC and CBD. Moreover, there is currently no available comprehensive chemical profiling for all USA states medical-type cannabis samples which is necessary to explore the similarities/differences if any among plants samples of different States. Therefore, this study was carried out.

In this study, a comprehensive work was carried out to identify the compounds most important in distinguishing cannabis varieties and to find the variation on cannabis chemical profiles as a result of growing plants in different environments and in growth time from 23 USA States that have enacted Medical Marijuana laws, including: Alaska, Arizona, California, Colorado, Delaware, Florida, Hawaii, Illinois, Maine, Maryland, Massachusetts, Michigan, Montana, Nevada, New York, Ohio, Oregon, Pennsylvania, Vermont, Washington, West Virginia, Wisconsin and Mississippi. GC-FID was applied for the chemical analysis. Cannabis plants samples obtained from each of the 23 USA States were collected, extracted and analyzed using GC-FID. The plant samples were analyzed to detect all possible cannabinoids and terpenoids from different cannabis seeds and origins which are necessary for cannabis fingerprinting. The method was validated and evaluated for selectivity and precision (i.e., repeatability). The advanced multivariate tools including Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) are efficient towards sample clustering [36–41]. Hence, these tools were performed in this study to; (1) identify the compounds most important in distinguishing cannabis varieties, (2) find the variation on cannabis chemical profiles as a result of growing plants in different States and with different in growth times, (3) confirm whether the cultivars (i.e., States) in the cluster analysis would also be grouped together, (4) reveal the compounds that were responsible for grouping cultivars between clusters and (5) develop a database that can predict the origins and type of unknown cannabis grown in the USA. To our knowledge, this study had never been carried out before.

Material and method

Cannabis plants

Cannabis samples (leaves and inflorescences) were collected from the supply of materials provided from seized samples by The Drug Enforcement Administration (DEA). The samples were obtained in tightly closed plastic bags and stored in a dry cool storage facility in the Coy Waller Complex at the University of Mississippi prior to analysis. The samples were selected from 23 States that have enacted Medical Marijuana laws, including: Alaska, Arizona, California, Colorado, Delaware, Florida, Hawaii, Illinois, Maine, Maryland, Massachusetts, Michigan, Montana, Nevada, New York, Ohio, Oregon, Pennsylvania, Vermont, Washington, West Virginia, Wisconsin and Mississippi.

Reagents and solutions

Twenty six standards of cannabinoids and terpenoids were provided from Sigma-Aldrich® (St. Louis, USA). Structural formula of organics are summarized in Table 1.

Table 1.

Structural formula of assayed cannabinoids and terpenoids

Phenanthrene (99% purity) used as the internal standard was supplied from Sigma-Aldrich®. All chemicals and solvents used for extraction and other preparations were of HPLC ultra-grade: acetone and ethyl acetate (≥99.7%), hexane (≥98.5%), ethanol (>98%), and methanol (≥99.8%) were purchased from Sigma-Aldrich®. Chloroform (≥99.8%) was provided from Fischer Scientific (New Jersey, USA). Ultrapure water (18 MΩ cm−1) generated by Milli-Q Plus water purification system (Millipore, Billerica, MA) was used to prepare aqueous solutions and dilutions.

Extraction of cannabinoids and terpenoids prior to GC analysis

Dried cannabis samples were grinded to get a homogenous mixture of leaf particles. A 100 mg portion was transferred to a test tube and 3.0 mL of extraction solution (methanol-chloroform 9:1 v/v spiked with 0.2 mg mL−1 phenanthrene) was added. Phenanthrene was used as both a retention time marker (Rt between the terpenoids and cannabinoids) and as a reference to calculate peak ratio of solute. The extraction tube was then placed in an ultrasonic water bath for 15 min to allow soluble cannabinoids and terpenes to dissolve in the extraction solution. The samples were then centrifuged for 30 s at 2,000 rpm. Finally, the extract was filtered using Acrodisc syringe filter (PAU-Gelman Lab, 0.45 μm, 25 mm diameter) and collected in a screw-capped amber vial. Samples were stored in a freezer (−10 °C) prior to analysis time. Duplicate extractions and injections were made for each cannabis sample.

Chromatographic analysis of samples

The chromatographic profiles of cannabis were all recorded in the splitless mode using an Agilent GC 6890 series system equipped with a 7683B autosampler. The GC column was an Agilent, DB-5, 30 m length, 0.25 mm internal diameter, film thickness 0.25 μm, (J&W Scientific Inc., Folsom, California, USA). The detailed experimental conditions applied in this study are available in our previous study [40].

Common standard stock solutions of cannabinoids and terpenoids were prepared at the concentration of 100 μg mL−1 in pure MeOH. Each solution was injected individually in two identical injections to determine the retention time of each component, and then the average was registered.

Chromatographic profiles of extracted samples

Selectivity was determined by injecting solvent blank to confirm that there were no false signal peaks at the targeted retention time. Intraday reproducibility was determined by injecting an aliquot of reference sample of β-pinene (100 μg mL−1) five times from the same vial in a single day (n = 5). The peak area ratio was calculated for each solute (Peak ratio = Peak area of solute/Peak area of IS) and further used to fingerprint the cannabinoids and terpenoids. The results reported as the average of two identical injections.

In the current investigation, 45 solutes were directly assayed by GC-FID compared to only 23 in our published work [40]. Hence, more comprehensive profile was adopted in this investigative. The identity of the new detected 22 solutes was identified from their retention times compared to the other common cannabinoids/terpenoids [1]. Hence, solutes detected after 30 min are related to cannabinoids-family [1].

Numerical analysis

Numerical analysis by PCA and HCA was carried out using Chemoface 1.61 software [42] which had been run under Matlab® (Mathworks, 8.6, USA). The size of chromatographic data used numerical analysis is 23 × 45 (23 samples × 45 organic solutes). Initially, the chromatographic data was loaded in Chemoface prior to PCA and HCA analysis.

Results and discussion

Quantitative analysis of cannabis varieties

For the comparison and the acquisition of a large number of complex chemical profiles, it is necessary to extract the maximum amount of plant contents. Therefore, a mixture of methanol: chloroform (9:1 v/v) was used to extract the highest amount of cannabinoids and terpenoids. Over the 23 USA cannabis samples, 45 active ingredients were detected. Among these ingredients, the identity of 26 compounds (eight cannabinoids and eighteen terpenoids) was established by comparing their retention times with authentic standards. The sesquiterpenoids symbolized as (Tn) and cannabinoids (Cn) have not been identified since no reference compounds were available for confirmation. The detected cannabinoids and terpenoids have variable proportions but comparable to those reported for Canadian cannabis [4]. All solutes were separated in 63 min. The sharp and intense GC peak positioned at ≈31.30 min was for phenanthrene (IS), and used for quantitative evaluation of the chemical profile contents (peak area ratio for each solute = Peak area of solute/Peak area of IS). The relative amount of separated solutes was reported as peak ratio rather concentration as reported in our previous work [40]. Moreover, numerical analysis by PCA and HCA was also based on peak ratios of solutes which is a common procedure. The position of phenanthrene encountered very small variations in both intensity and retention time during all injections. The proposed GC method was stable and convenient to quantify all the 45 organics.

In general, cannabinoid solutes such as CBN, Δ9-THC, CBG, and CBC have higher peak intensity and eluted at longer retention times compared to terpenoids, which would be attributed to the higher polarity of cannabinoids. Interday reproducibility was determined by injecting the same reference sample 12 times using fresh aliquots on each day (n = 12). The intra and inter-day precisions (%RSD) were 0.37% and 0.32%, respectively. These low RSD values indicate that the method was precise in terms of repeatability, reproducibility, and intermediate precision. Instrumental precision (RSD), defined as the variation in peak area of the IS to all solutes was found to be 1.22%. The retention time (min) and chemical profiles of cannabis samples (expressed as the solute/internal standard peak area ratios) were obtained from duplicate measurements by GC-FID as provided in Table 2.

Table 2.

Chemical profiles of cannabis samples obtained from the 23 USA States (Cn and Tn are symbols for unidentified cannabinoids and terpenoids, respectively).∗ The value represents the Peak ratio

SoluteRetention time (min)AlaskaWest VirginiaWisconsinWashingtonVermontPennsylvaniaOregonMontanaOhioMississippiNevadaNew YorkMassachusettsMichiganMarylandMaineIllinoisFloridaColoradoArizonaHawaiiDelawareCalifornia
α-Pinene9.9588∗211513433168355854774738429146696714316932181
Sabinene10.673342274833242923203224172720141518162123192316
β-Pinene11.0841112623181837212029461825183117242417114023100
Myrcene11.1248111923181432202016331729183318252617114023100
Limonene12.3020161310111240714131032181415181713281610251232
Cineol12.4511N.DN.DN.DN.DN.D1267N.D71056445652838
Terpinolene13.782651252223273225272617152526292226273811342836
Linalool14.1230562531222642230263731233627292128264521364634
Thujone14.93141311291827151724103198821519177716714
T115.15887291211869834104818447102772710
α-Terpineol16.9811N.DN.DN.D8N.D168858956766633749
Carveol17.391836917262017131436161729188292183117122717
Azulene20.741710346N.D132110189263873114107201710391010
β-Caryphyllene22.48979610149150105153155187561501751681361169114218113651223132247
T222.91551911417556132716812262850818614663408221411914220200234
T323.023431299537284321166468851171917724871048
α-Humulene23.7227691812348305448652060555549382746563218733964
T424.1213131127988154211168478154562717
T524.439192384413432524179233192018917271731132031
T625.694150339178753103548345276561543417985431692086
T725.8160504311521091010123153359604465119875149230110
Caryophylleneoxide26.7646243223404343219381323243131312430293821384933
Guaiol26.912420241934193727401540451925251719222513422427
T827.36107152306110339165213079296111272433734182685217731
T927.71811713328473749362018115379021808519515847691045
α-Bisabolol28.946052803540356356784066344267492533494542673583
Fenchol28.22115528961445341,027616240394332866492432493630674586
T1029.5578761078712658612717551377
Phenanthrene (IS)31.30
T1132.6376688N.DN.DN.DN.DN.D936676247531111024
T1234.09126137713147261351077101010131710
T1336.952020152321102833111810131413111331141114162024
CBDV40.8420221763427284219654539544591711717381363328
THCV41.425328805523265147502046153744573223561412810432128
CBL42.8712111721610128614178298161214131311101215291740
CBC44.4116322522853138172740270402101137183222176131354648201160159426203283
Δ8-THC46.651756161412933781810303131019171718141110211318
Δ9-THC47.544,6951,1832,3832,1912,2411,4425,4593,6463,9501,9504,5031,6632,2064,2163,9492,4463,7094,9852,2432,3886,3522,6848,035
C148.717247656,386375990765859149795342516420645827984045
CBG49.70343951541401241392212032409616581247236130119351139170125276199330
CBN50.091,0833866706164245231,1359558565028839782238659226035317135649001,138485494
C251.822230602911364928482274921785583714815427132724
C352.891310152271882520112714991418317105131321
C454.2263657021854152128713765695012589820374834694352
C555.8363721503366661379948381441013027879112345431892045
C658.0813813881150562110262218152091435227491321

As indicated in Table 2, forty five common compounds were detected in each sample, where, 8 cannabinoids and 18 terpenoids were identified based on standard comparison. Usually, the detected cannabinoids were related to six different classes: Δ8-Tetrahydrocannabinol (Δ8-THC), Δ9-Tetrahydrocannabinol (Δ9-THC and THCV), Cannabichromene (CBC), Cannabigerol (CBG), Cannabicyclol (CBL), and Cannabinol (CBN) [7]. The other unidentified cannabinoids and terpenoids were symbolized as Cn and Tn, respectively, were n represents a number. In the current investigation, 45 solutes were directly assayed by GC-FID compared to only 23 in our published work [40]. Hence, more comprehensive profile was adopted in this investigative. The identity of the new 22 detected solutes were identified from their retention times compared to the other common cannabinoids/terpenoids, hence, solutes detected after 30 min are related to cannabinoids-family [1].

The unidentified Cn and Tn have been determined as terpenoids and cannabinoids based on comparison studies [1, 43]. It is known that cannabinoids would be available in neutral and acidic forms and quantification of both forms will require silylation/methylation of the acidic ones before GC analysis [12]. Hence, the provided data in this work gave the total contents of neutral and acidic forms of cannabinoids as no silylation of the acidic groups was carried out. Moreover, most cannabinoids are available in their neutral form, for example, ten isolated forms are known for Δ9-Tetrahydrocannabinol and only two of these are in acidic form.

Among all cannabinoids, the total content in all states of Δ9-THC and CBN were remarkably higher than the rest of all other ingredients; 78,520 and 16,450, respectively. It is known that the psychoactive nature of cannabis is highly related to the level of Δ9-THC. In the same time, the high content of CBN indicated the long storage time of samples as CBN is generated from Δ9-THC with time. CBG, the first biogenic cannabinoid formed in the plant, was also available in acceptable amounts of 4,323. CBC content (5,774) found to be as intermediate level between Δ9-THC and CBG. It occurs mainly as cannabichromenic acid (CBCA, 2-COOH–CBC, CBC–COOH). Geranylpyrophosphate and olivetolic acid combine to produce cannabigerolic acid (CBGA; the sole intermediate for all other phytocannabinoids), which is cyclized by the enzyme CBCA synthase to form CBCA. Over time, or when heated above 200° F, CBCA is decarboxylated, producing CBC. Beside cannabinoids, terpenoids impart the scent of cannabis plants where most of the peaks are present in the terpenes region (retention time ≈ < 30 min).

The marked compound/s in the 23 states is/are the following: limonene, fenchol, linalool, Guaiol, and CBC are typical fingerprints for cannabis Oregon State. Regarding Nevada, it was marked for its high content of Fenchol, CBL, T3 and T7. Washington State can be distinguished easily due to its high content of CBL, C1 and Fenchol in comparison to other states, where it gives notable highest level overall states for C1 with a peak ratio of 6,386. T2 compound was characteristics for cannabis from Vermont State. THCV and CBC contents showed the highest level in Illinois State. Considering the contents obtained for Δ9-THC, all states detected it with a very large content, but Hawaii (6,352), Oregon (5,459) and California (8,035), respectively, have the highest peak ratios among all States. Concerning CBN rates as the second highest content after Δ9-THC, where its content considered high in all states with comparable values. The highest content among all 45 organics over all states was for California State (11,192), Oregon (11,511) and Washington (11,095) that has the highest notable content of cannabinoids, especially Δ9-THC and CBN.

For the rest of organics, comparable ratios for cannabis in the States have been observed. Finally, cineol, T11 and α-terpinol were not detected in all samples, and even if detected, their levels were very low. As shown, specific cannabinoids such as Δ9-THC, CBN, CBG, C1 and CBC are dominants in all States. This is referred to the strong influence of geographical position, maturity, age and storage conditions and the fluctuation of cannabinoids content between states with age that is numerous [1, 44, 45].

Regarding the terpenoids family, Fenchol was the most abundant one. Terpenoids were grouped to: monoterpenoids, sesquiterpenoids, and triterpenoids. Although the number of identified terpenoids was relatively high (18 solutes), the content of cannabinoids was higher (Table 2). For terpenoids, two main classes were identified: a) Sesquiterpene including (α-bisabolol/α-humulene/β-caryphyllene/caryophylleneoxide/guaiol), and b) Monoterpene including (α-pinene/β-pinene/α-terpineol/fenchol/linalool/myrcene/terpinolene/limonene/sabinene/carveol/cineol). The content of Fenchol was relatively high in the majority of collected samples. The explanation is that the quantities of Fenchol within cannabis plants, as well as other plants, vary significantly refers to: the growing conditions, including groundwater mineral content, soil/growing medium mineral content, soil condition, light, temperature, age of the plant, maturity of the plant, storage conditions and air pollution. Therefore, terpene's levels vary dramatically not just from one growing region to another, but from plant to plant within the same growing area [8, 46].

Based on the content of Δ9-THC, cannabis can be divided into three chemical phenotypes [47]: (i) drug type, in which the major compound Δ9-THC is about 1–20%; (ii) intermediate type, in which Δ9-THC are the leading cannabinoids and their concentration range is 0.3–1.0%; and (iii) fiber type mainly contains Δ9-THC is in the concentration <0.3%. Another method to distinguish between drug-type and fiber type cannabis has been defined by the UNODC [48] with a simple mathematic equation. According to this criterion, about 86% of the cannabis samples analyzed containing detectable amount of Δ9-THC belongs to the drug type. However, those classification methods are not accurate because an assumption was made that the acidic cannabinoids were completely converted to neutral cannabinoids during the decarboxylation process, for example, THCA acid decarboxylates as a result of high temperature during gas chromatography analysis to produce THC.

As a promising field combining computer science and analytical chemistry, chemometrics has increasingly found application in natural products chemistry, and has been used extensively for analytical data mining, graphical visualization, and class discrimination and prediction [37–41]. An important step in this research is data analysis where mathematical algorithms were used to extract useful information from huge data sets obtained from GC-FID as will be shown in the following section.

Data analysis and classification of states based on cannabinoids and terpenoids contents

Although Table 2 and Fig. 1 give comprehensive profile of all cannabinoids and terpenoids levels in each state sample, some compounds are more important to the clustering. In order to clearly differentiate among cannabis States and to specify the compounds responsible for clustering the groups, the GC-FID scan data were subjected to HCA and PCA analysis. PCA and HCA are unsupervised clustering techniques commonly employed to reduce the complexity of multivariate data sets without losing important information, observe variance in data sets, and visualize data clustering. In our study, 45 cannabinoids and terpenoids are the original variables (45 dimensions) in PCA. By calculating the covariance matrix between these 45 dimensions, PCA can generate 45 PCs that are orthogonal to each other and can explain 100% of the total variance of the orthogonal data. In this work, the first two PCs explain 89.73% of the total variance. Each PC is correlated with the original 45 variables. The chromatographic data was preprocessed using mean-center methodology for better interpretably of PCA and HCA outputs [49, 50]. All detected organics were rather necessary for states clustering. Accordingly, the number of variables used in clustering was 45 (detected compounds) × 23 (USA States cannabis samples). The resulted HCA clustering of states is provided in Fig. 2.

Fig. 1.
Fig. 1.

Total cannabinoids and terpenoids contents in each state based on the dendrogram and a full chemical profile

Citation: Acta Chromatographica Acta Chromatographica 2020; 10.1556/1326.2020.00767

Fig. 2.
Fig. 2.

Dendrogram obtained from the whole chromatographic data

Citation: Acta Chromatographica Acta Chromatographica 2020; 10.1556/1326.2020.00767

HCA results are shown in Fig. 2. This dendrogram was obtained by calculating the Euclidean distance among samples and grouping them by the complete linkage method. There are three main groups that are clearly discriminated; Group A includes 11 States; Alaska, California, Florida, Hawaii, Illinois, Maryland, Michigan, Montana, Nevada, Ohio, and Oregon. Group B contains only Washington. Where, group C clustered 11 States; Arizona, Colorado, Delaware, Maine, Massachusetts, New York, Pennsylvania, Vermont, West Virginia, Wisconsin and Mississippi. All states are clustered while shown no mixing in different USA States.

The results of PCA projection of the data of GC into the plan of the first two principles components are carrying an accumulative average of 89.73% of the total variance. Hence, loading, score and bi-plots can be viewed using two factors only.

PCA was applied to the matrix of 23 × 45 (23 USA States × 45 detected cannabis). The results of PCA projection of the data from the first two principle components are carrying 89.73% of the total variance as shown in Fig. 3A.

Fig. 3.
Fig. 3.

PCA outputs, (A) score plot, (B) loading plot, and (C) bi-plot obtained for cannabinoids and terpenoids components

Citation: Acta Chromatographica Acta Chromatographica 2020; 10.1556/1326.2020.00767

As indicated in Fig. 3A, the score plot indicated three main different clusters collecting different number of states-this clustering corresponds with cannabinoids and terpenoids content. For example, cluster A has 11 states as mentioned in HCA analysis which is related to the similar and/or comparable contents of cannabis samples. Cluster B has only Washington; accordingly, cannabis samples obtained from Washington is significantly different from the rest of samples from other states, due to distinct contents of C1 than the other states.

PC1 describes 77.27% of the variance of the data set, and as shown in Fig. 3A, PC1 has high positive loading for states of group C which includes 11 states and also positive loading on all cannabinoids and terpenoids except Δ9-THC, and negative loading for group A which contains 11 states (Fig. 3A) and one content which is Δ9-THC (Fig. 3B). On the other hand, PC2, accounting for 12.46% of the original information has a significance contribution from Washington State only and C1 component which makes PC2 a “cannabinoid” distinct item. Again, together, these 2 PCs account for 89.73% of the total variance in data.

Figure 3B, loading plot for PC1 and PC2, gives an intuitive explanation whereby the longer the radial separation of the compound from the center, the more important the compound is in distinguishing states. The mathematical explanation is that the radial equals the square sum of the compound's correlations with PC1 and PC2. From the loading plot, it can be seen that the Δ9-THC was responsible for isolating 11 states Hawaii, California, Florida, Michigan, Alaska, Illinois, Montana, Nevada, Oregon, Maryland and Ohio who all have high content of Δ9-THC > 3000 and (Table 2). The position of Washington highly depends on C1, since it showed the second highest content overall components of cannabis (6,386).

In conclusion, if States are separated along PC1, they contain a distinct amount of Δ9-THC, CBN and Fenchol. If cultivars are separated along PC2, they contain different amount of cannabinoids C1.

In PCA loading plot Fig. 3B. that has been illustrated to show the most significant solutes for states clustering. Δ9-THC and C1 were not correlated with other cannabinoids and terpenoids and more significant for samples clustering. While with low distinct for clustering; Fenchol and CBN showed lower impact on states separation. This result is supported by the results obtained above for the marker solutes and indeed with the our recently published outcomes for samples clustering, but when only 26 standards were injected as master solutes for clustering [40]. The other 41 cannabis contents were positioned close together and this indicating their limited usage for cannabis states classification. It was interesting to notice the limited performance of some important cannabinoids and terpenoids (CBC and CBG, pinene, etc…) for cannabis states clustering compared to Fenchol. In summary, Fenchol, CBN, C1 and Δ9-THC seems to be the most significant contents for cannabis clustering with comprehensive chemical profiles provided or/and only common contents included (Fig. 4) (Table 3).

Fig. 4.
Fig. 4.

Total cannabinoids and terpenoids contents in each cluster based on the dendrogram and a full profile for the: A) 42 cannabis without Fenchol, CBN and Δ9-THC, B) for Fenchol, CBN and Δ9-THC

Citation: Acta Chromatographica Acta Chromatographica 2020; 10.1556/1326.2020.00767

Table 3.

The average levels of the total 45 compounds in each cluster of Fig. 2

No.Compound nameCluster ACluster BCluster C
1.α-Pinene84413373
2.Sabinene23248287
3.β-Pinene40323214
4.Myrcene39423193
5.Limonene61610148
6.Cineol79029
7.Terpinolene30422290
8.Linalool71831360
9.Thujone24429145
10.T111929129
11.α-Terpinol91043
12.Carveol16217277
13.Azulene2146158
14.β-Caryphyllene1,645491,261
15.T21,21171,767
16.T367995205
17.α-Humulene57923575
18.T41712784
19.T53208244
20.T68029713
21.T798711527
22.Caryophylleneoxide33019255
23.Guaiol74923338
24.T859061998
25.T958628632
26.α-Bisabolol66935470
27.Fenchol2,007614455
28.T10100665
29.T1195881
30.T12947117
31.T1321023168
32.CBDV4336556
33.THCV87655362
34.CBL544216145
35.CBC3,578532,143
36.Δ8-THC55714176
37.Δ9-THC53,5002,19122,829
38.C17686,386587
39.CBG2,6341401,549
40.CBN9,5756166,259
41.C249529412
42.C318922131
43.C462721639
44.C578333719
45.C63108224

As depicted in Fig. 3C, Δ9-THC, CBN and Fenchol were of high efficiency to separate large number of states from the rest. On the other hand, C1 was dominant to separate Washington away from the rest of cannabis samples obtained from other states. Compared to terpenoids, number of cannabinoids for states clustering is more significant due to their: a) therapeutic uses including pain management and neurological disorders [4, 5, 7, 11], and b) large abundance in cannabis [7]. As shown in Table 2, Δ9-THC and CBN were available in large excess compared to the rest of compounds, 78,520, 16,450, respectively. Where, Δ9-THC and CBN have notably large difference in contents than other components. In fact, Δ9-THC is a common constituent with levels varying even within the same sample depending on the composition of the sample (i.e., leaves vs. bud, vs. mixture and the ratio of small leaves to large leaves). Hence, the variation in THC content among samples is expected. In the same time, CBN is a degradation product of THC and reflects the age of the sample, seeds source and storage environments. Therefore, with the obtained separation among the states, this data set could be used as a database set to simple and fast classification future of unknown sample of cannabis from any of the studied states.

Since Δ9-THC, CBN from cannabinoids, and Fenchol from terpenoids group showed the highest content over all components, the impact of excluding these 3 contents on states clustering is studied in the next section.

Examination of chemical profiles for distinguished peaks characteristics at specific states: Impact of Δ9-THC, CBN and Fenchol

To determine if certain distinct chemical “marker” compounds presence in cannabis plants from one State, but absent in plants from another State, will affect the clustering and cannabis classification, data were re-arranged and PCA was run again with excluding the highest three contents from the chemical profiles; Δ9-THC, CBN from cannabinoids, and Fenchol from terpenoids. PCA analysis carried out 95.98% of the total variance. PCA outputs showed that excluding Δ9-THC, CBN and Fenchol: 1) has improved the separation of Nevada, Oregon and Illinois from the rest of states as shown in the score plot in Fig. 5A. Interestingly, this states-origin cannabis when Δ9-THC, CBN and Fenchol were included was clustered in one cluster which is A (Fig. 2) and (Compare Figs 3A and 5A) with Alaska, Montana, Maryland, Ohio, Hawaii, California, Florida and Michigan, and this would indicate the importance of Δ9-THC, CBN, and Fenchol for these states. 2). In addition, removing Δ9-THC, CBN, and Fenchol merge cluster A and cluster B (Fig. 2) in only one group of 19 states (i.e., all 11 States of group C with 8 States of group A), which means that Δ9-THC, CBN and Fenchol are the main responsible for States and cannabis separation. Finally, 3). only Washington State cannabis-origin has not been change in position, which means that this state depends on C1 content and not other compounds since it was not affected upon excluding as shown in PCA (Compare Fig. 3A with Fig. 5A).

Fig. 5.
Fig. 5.

PCA outputs, (A) score plot, (B) loading plot, and (C) bi-plot obtained for the 42 cannabinoids and terpenoids components while excluding Δ9-THC, CBN, and Fenchol

Citation: Acta Chromatographica Acta Chromatographica 2020; 10.1556/1326.2020.00767

As shown in Table 2, Δ9-THC, CBN, C1 and CBC cannabinoids and Fenchol as terpenoids were available in large excess compared to the rest of compounds, 78,520, 16,450, 7,741, 5,774, and 3,076, respectively. Figure 5B indicated that after excluding Δ9-THC, CBN, and Fenchol, CBC and C1 were the most significant variables needed for samples clustering. This result supports the data obtained in Table 1 which showed that after Δ9-THC and CBN, compounds C1 and CBC becomes the second important and largest contents. On the other hand, CBG, CBL and THCV have same influence and both not highly correlated with C1 (angle 90o). The rest of variables (cannabis contents) were accumulated in the center indicating their limited applicability for samples clustering. In fact, this result confirms the reality and the rigidity of the outputs obtained in Table 2 and in Fig. 3 that Δ9-THC and CBN which have the highest contents; have the largest impact on cannabis clustering, and if excluded; CBC and C1 will be the responsible for cannabis states clustering (Fig. 5). To validate this result, bi-plot (Fig. 5C), has been demonstrated in 2PCs and 3PCs plots for clarification issue only since 2 PCs only has very overlapped data and difficult to be read. This figure indicated that CBC was necessary to separate Oregon while, C1 was necessary to isolate Washington from the rest of states. At this stage, it is clear that sample clustering is highly sensitive to the included cannabinoids, but with low distinct to terpenoids.

Conclusions

GC-FID was adopted to record the chemical profiling of 45 terpenoids and cannabinoids in 23 USA-cannabis samples. The obtained profiles were further used to cluster cannabis samples with the aid of PCA and HCA. The clustering results would uncover the geographic origin of grown cannabis specimen. Using HCA and PCA, the 23 USA cannabis plants were classified; group A consists of 11 states and also group C, where group B has only Washington State that showed totally different cannabis contents. Multivariate analysis showed also which contents are critical in discriminating cultivars since samples were grouped into 3 clusters; cluster A is THC dominant, cluster B is C1 dominant, and finally cluster C is Fenchol and CBN dominant. The results were different from cluster analysis using THC, CBN and Fenchol content only, which supports the hypothesis that classifications based exclusively on limited numbers of content may be insufficient when considering all medically relevant compounds in cannabis.

Conflicts of interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Acknowledgment

Dr. Ramia Al Bakain gratefully acknowledges the financial support for her Fulbright post doctorate year 2017/2018 at The University of Mississippi, USA, provided by (1) The Binational Fulbright Commission in Jordan, and (2) the University of Jordan. Indeed, great appreciation goes to Dr. Omar Marzouk for his experimental help and technical support. Many thanks also go to Dr. Mohamed Radwan, Elsayed Ibrahim, Avery Claire Jones, and Chandrani Gon Majumdar for their assistance.

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Appendix A Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1556/1326.2020.00767.

If the inline PDF is not rendering correctly, you can download the PDF file here.

Supplementary Materials

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    Brenneisen, R.; Elsohly, M. Chromatographic and spectroscopic profiles cannabis of different origins: part 1. J. Forensic. Sci. 1988, 33, 13851404.

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    http://medicalmarijuana.procon.org/view.resource.php?resourceID=000881.

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    Lee, D. C.; Schlienz, N. J.; Peters, E. N.; Dworkin, R. H.; Turk, D. C.; Strain, E. C.; Vandrey, R. Systematic review of outcome domains and measures used in psychosocial and pharmacological treatment trials for cannabis use disorder. Drug. Alchol. Depen. 2019, 194, 500517.

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  • 4.

    Jin, D.; Jin, S.; Yu, Y.; Lee, C.; Chen, J. Classification of cannabis cultivars marketed in canada for medical purposes by quantification of cannabinoids and terpenes using HPLC-DAD and GC-MS. J. Anal. Bioanal. Tech. 2017, 8, 19.

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    Sohly, M. E. Constituents of Cannabis Sativa; Handbook of Cannabis, 2014.

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    McPartland, J. M.; Russo, E. B. Cannabis and cannabis extracts. J. Cannabis Ther. 2001, 1, 103132.

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    Brenneisen, R. Chemistry and analysis of phytocannabinoids and other Cannabis constituents. Forensic Science and Medicine: Marijuana and the Cannabinoids; Humana Press Inc: Totowa, NJ, 2007.

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    • Export Citation
  • 8.

    ElSohly, M. A.; Stanford, D. F.; Murphy, T. P. Chemical fingerprinting of cannabis as a means of source identification. In Marijuana and the Cannabinoids, 2007, pp 5166.

    • Search Google Scholar
    • Export Citation
  • 9.

    Russo, E. B. History of cannabis and its preparations in saga, science, and sobriquet. Chem. Biodivers. 2007, 4, 16141648.

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    Pertwee, R. G. Handbook of Cannabis; Oxford University Press, 2014, pp 3322.

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    Russo, E. B. Taming THC: potential cannabis synergy and phytocannabinoid-terpenoid entourage effects. Br. J. Pharmacol. 2004, 163, 13441364.

    • Search Google Scholar
    • Export Citation
  • 12.

    Wang, M.; Wang, Y.-H.; Avula, B.; Radwan, M. M.; Wanas, A. S.; Mehmedic, Z.; van Antwerp, J.; ElSohly, M. A.; Khan, I. A. Quantitative determination of cannabinoids in cannabis and cannabis products using ultra-high-performance supercritical fluid chromatography and diode array/mass spectrometric detection. J. Forensic. Sci. 2017, 62, 602611.

    • Search Google Scholar
    • Export Citation
  • 13.

    Baron, E. Comprehensive Review of Medicinal Marijuana, Cannabinoids, and Therapeutic Implications in Medicine and Headache: What a Long Strange Trip It's Been, 2015.

    • Search Google Scholar
    • Export Citation
  • 14.

    Schier, A. R.; Ribeiro, N. P.; Silva, A. C.; Hallak, J. E.; Crippa, J. A.; Nardi, A. E.; Zuardi, A. W. Cannabidiol, a Cannabis sativa constituent, as an anxiolytic drug. Rev. Bras. Psiquiatr. 2012, 34, S104S117.

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    • Export Citation
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    • Search Google Scholar
    • Export Citation
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    http://www.hc-sc.gc.ca/dhp-mps/marihuana/med/infoprof-eng.php.

  • 17.

    Amar, M. B. Cannabinoids in medicine: A review of their therapeutic potential. J. Ethnopharmacol. 2016, 105, 125.

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    Hazekamp, A.; Grotenhermen, F. Review on clinical studies with cannabis and cannabinoids 2005–2009. Cannabinoids 2010, 5, 121.

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    Hillig, K. W.; Mahlberg, P. G. A chemotaxonomic analysis of cannabinoid variation in cannabis (cannabaceae). Am. J. Bot 2004, 91, 966975.

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    • Export Citation
  • 20.

    Hillig, K. W. A chemotaxonomic analysis of terpenoid variation in Cannabis. Biochem. Syst. Ecol. 2004, 32, 875891.

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    Hillig, K. Genetic evidence for speciation in Cannabis (Cannabaceae). Genet. Resour. Crop Evol. 2005, 52, 161180.

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    Szabady, B.; Hidvégi, E.; Nyiredy, Sz. Determination of neutral cannabinoids in hemp samples by overpressured-layer chromatography. Chromatographia 2002, 65, S165S168.

    • Search Google Scholar
    • Export Citation
  • 23.

    Zuardi, A. W.; Hallak, J. E. C.; Crippa, J. A. S. Interaction between cannabidiol (CBD) and Δ 9-tetrahydrocannabinol (THC): influence of administration interval and dose ratio between the cannabinoids. Psychopharmacology 2012, 219, 247249.

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    • Export Citation
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    • Search Google Scholar
    • Export Citation
  • 25.

    Novotny, M.; Lee, M.L.; Low, C.-E.; Raymond, A. Analysis of marijuana samples from different origins by high-resolution gas-liquid chromatography for forensic application. Anal. Chem. 1976, 48, 2429.

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    • Export Citation
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    Hazekamp, A.; Fischedickm, J. T. Cannabis—from cultivar to chemovar, Drug Test. Analysis; Wiley Online Library, 2012.

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    Ndjoko, K.; Wolfender, J.-L.; Hostettmann, K. Analysis of cannabinoids by liquid chromatography—Thermospray mass spectrometry and liquid chromatography—Tandem mass spectrometry. Chromatographia 1998, 47, 7276.

    • Search Google Scholar
    • Export Citation
  • 28.

    Van der Kooy, F.; Maltese, F.; Choi, Y. H.; Kim, H. K.; Verpoorte, R. Quality control of herbal material and phytopharmaceuticals with MS and NMR based metabolic fingerprinting. Planta. Med. 2009, 75, 763775.

    • Search Google Scholar
    • Export Citation
  • 29.

    Politi, M.; Peschel, W.; Wilson, N.; Zloh, M.; Prieto, J. M.; Heinrich, M. Direct NMR analysis of cannabis water extracts and tinctures and semiquantitative data on D9-THC and D9-THC-acid. Phytochemistry 2008, 69, 562570.

    • Search Google Scholar
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  • 30.

    Choi, Y. H.; Kim, H. K.; Hazekamp, A.; Erkelens, C.; Lefeber, A. W. M.; Verpoorte, R. Metabolomic differentiation of Cannabis sativa cultivars using 1H NMR spectroscopy and principal component analysis. J. Nat. Prod. 2004, 67, 953957.

    • Search Google Scholar
    • Export Citation
  • 31.

    Breitenbach, S.; Rowe, W. F.; McCord, B.; Lurie, I. S. Assessment of ultra high performance supercritical fluid chromatography as a separation technique for the analysis of seized drugs: Applicability to synthetic cannabinoids. J. Chrom. A 2016, 1440, 201211.

    • Search Google Scholar
    • Export Citation
  • 32.

    Toyo'oka, T.; Kikura-Hanajiri, R. A reliable method for the separation and detection of synthetic cannabinoids by supercritical fluid chromatography with mass spectrometry, and its application to plant products. Chem. Pharm. Bull. 2015, 63, 762769.

    • Search Google Scholar
    • Export Citation
  • 33.

    Bäckström, B.; Cole, M. D.; Carrott, M. J.; Jones, D. C.; Davidson, G.; Coleman, K. A preliminary study of the analysis of Cannabis by supercritical fluid chromatography with atmospheric pressure chemical ionisation mass spectroscopic detection. Sci. Justice 1997, 37, 9197.

    • Search Google Scholar
    • Export Citation
  • 34.

    Later, D. W.; Richter, B. E.; Knowles, D. E.; Andersen, M. R. Analysis of various classes of drugs by capillary supercritical fluid chromatography. J. Chromatogr. Sci. 1986, 24, 249253.

    • Search Google Scholar
    • Export Citation
  • 35.

    Lehmann, T.; Brenneisen, R. High performance liquid chromatographic profiling of cannabis products. J. Liq. Chrom. 1995, 18, 689700.

  • 36.

    Crispino, C.; Fernandes, K.; Kamogawa, M.; Nóbrega, J.; Nogueira, A. R.; Ferreira M. Multivariate classification of cigarettes according to their elemental content determined by inductively coupled plasma optical emission spectrometry. Anal. Sci. 2007, 23, 435438.

    • Search Google Scholar
    • Export Citation
  • 37.

    Al Bakain, R.; Rivals, I.; Sassiat, P.; Thiébaut, D.; Hennion, M.-C.; Euvrard, G.; Vial, J. Comparison of different statistical approaches to evaluate the orthogonality of chromatographic separations: Application to reverse phase systems. J. Chrom. A 2011, 1218, 29632975.

    • Search Google Scholar
    • Export Citation
  • 38.

    Al Bakain, R.; Rivals, I.; Sassiat, P.; Thiébaut, D.; Hennion, M.-C.; Euvrard, G.; Vial J. Impact of the probe solutes set on orthogonality evaluation in reverse phase chromatographic systems. J. Chrom. A 2012, 1232, 231241.

    • Search Google Scholar
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Senior editors

Editor(s)-in-Chief: Kowalska, Teresa

Editor(s)-in-Chief: Sajewicz, Mieczyslaw

Editors(s) Danica Agbaba (University of Belgrade, Belgrade, Serbia);
Ivana Stanimirova-Daszykowska (University of Silesia, Katowice, Poland),
Monika Waksmundzka-Hajnos (Medical University of Lublin, Lublin, Poland)

Editorial Board

R. Bhushan (The Indian Institute of Technology, Roorkee, India)
J. Bojarski (Jagiellonian University, Kraków, Poland)
B. Chankvetadze (State University of Tbilisi, Tbilisi, Georgia)
M. Daszykowski (University of Silesia, Katowice, Poland)
T.H. Dzido (Medical University of Lublin, Lublin, Poland)
A. Felinger (University of Pécs, Pécs, Hungary)
K. Glowniak (Medical University of Lublin, Lublin, Poland)
B. Glód (Siedlce University of Natural Sciences and Humanities, Siedlce, Poland)
A. Grochowalski† (Cracow University of Technology, Kraków, Poland)
K. Kaczmarski (Rzeszow University of Technology, Rzeszów, Poland)
H. Kalász (Semmelweis University, Budapest, Hungary)
R. Kaliszan† (Medical University of Gdańsk, Gdańsk, Poland)
I. Klebovich (Semmelweis University, Budapest, Hungary)
A. Koch (Private Pharmacy, Hamburg, Germany)
Ł. Komsta (Medical University of Lublin, Lublin, Poland)
P. Kus (Univerity of Silesia, Katowice, Poland)
D. Mangelings (Free University of Brussels, Brussels, Belgium)
E. Mincsovics (Corvinus University of Budapest, Budapest, Hungary)
G. Morlock (Giessen University, Giessen, Germany)
J. Namiesnik† (Gdańsk University of Technology, Gdańsk, Poland)
J. Sherma (Lafayette College, Easton, PA, USA)
R. Skibiński (Medical University of Lublin, Lublin, Poland)
B. Spangenberg (Offenburg University of Applied Sciences, Germany)
T. Tuzimski (Medical University of Lublin, Lublin, Poland)
Y. Vander Heyden (Free University of Brussels, Brussels, Belgium)
A. Voelkel (Poznań University of Technology, Poznań, Poland)
B. Walczak (University of Silesia, Katowice, Poland)
W. Wasiak (Adam Mickiewicz University, Poznań, Poland)

KOWALSKA, TERESA
E-mail: kowalska@us.edu.pl

SAJEWICZ, MIECZYSLAW
E-mail:msajewic@us.edu.pl