Differentiation of Solanaceae psychoactive plants based on GC-MS analysis supported by chemometric tools

Microchemical Journal 150 (2019) 104098

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Differentiation of Solanaceae psychoactive plants based on GC-MS analysis supported by chemometric tools

T

Monika Ciechomskaa, Michał Woźniakiewicza, , Karolina Machlowskaa, Piotr Klepackib, Paweł Kościelniaka ⁎

a b

Laboratory for Forensic Chemistry, Department of Analytical Chemistry, Faculty of Chemistry, Jagiellonian University, Gronostajowa 2, 30-387 Kraków, Poland Institute of Botany, Faculty of Biology and Earth Sciences, Jagiellonian University, Kopernika 27, 31-501 Krakow, Poland

ARTICLE INFO

ABSTRACT

Keywords: Chemometrics PCA CA Atropine Scopolamine Solanaceae

Among Solanaceae plants, there are many which contain psychoactive or toxic compounds as atropine and scopolamine. They could be legally planted as ornamental plants or might be found growing widely. This is why they are a frequent subject of abuse sometimes followed by intoxication, and thus they are a subject of forensic examination. The differentiation between particular genera using only the concentration of atropine and scopolamine might be misleading as the variability between particular plants is not negligible. In this work, the chemometric methods were used to solve this problem. Principal component analysis and hierarchical cluster analysis have been employed in the discrimination between leaves and seeds originated from such Solanaceae plants as Datura metel, Datura stramonium, Brugmansia pittieri, Datura inoxia, Scopolia carniolica, Scopolia lurida, Atropa belladonna and Hyoscyamus niger using the microwave-assisted extraction followed by the GC–MS analysis. Analytical data were processed according to the self-build GC–MS database for components recognition. This enabled the differentiation between plant samples and moreover, some differences between plants growing area have been also exemplified.

1. Introduction Solanaceae (nightshades) psychoactive plants like e.g. Datura L., Brugmansia Pers., Atropa L., Scopolia Jacq., Hyoscyamus L. contain tropane alkaloids such as atropine and scopolamine, thus despite being used for medical purposes are also abused as hallucinogenic agents [1]. Because some of these plants are popular ornamental flowers in gardens (Datura, Brugmansia) and some grow wildly also as ruderal species, they are treated as a legal alternative to illicit drugs, especially by teenagers who share their experience on the Internet [2,3]. Nevertheless, the adverse effects after taking Solanaceae psychoactive plant leaves or seeds can be very serious (e.g. tachycardia, consciousness disorder) sometimes also leading to decease [1,4]. It is also likely that dried plant organs (e.g. leaves) of that species can be used as a substrate for new psychoactive substances sold as so-called “designer drugs”. Therefore, there is an immense need for developing a method that enables to chemically distinguish between Solanaceae psychoactive leaf and seed samples, among genus or species as well as a different place of growth, even in a tiny amount of sample (e.g. secured at the crime scene). There were a few published papers that described the determination of tropane alkaloids using GC–MS [5–7], HPLC [8–14] and chemical ⁎

composition [10,15–17] of the psychoactive Solanaceae plants. It was postulated that atropine and scopolamine content differs in various plant species and organs and it depends on various conditions such as weather, climate or plant age. However, none of them focused on analyzing the possibility of the differentiation between those plants based on their constituents as well as atropine and scopolamine concentration. Within the framework of this research, the microwave-assisted extraction (MAE) was used as a simple and rapid isolation method of all compounds present in plant material together with QuEChERS (Quick, Easy, Cheap, Effective, Rugged and Safe) dispersive solid phase extraction as a clean-up method in quantitative analysis of atropine and scopolamine. Atropine, scopolamine and other matrix constituents, were determined after the derivatization step, using GC–MS. Our previous work [18] proved that this method provided high precision, accuracy, and reduction of the matrix effect and that it can be used for qualitative, semi-quantitative and quantitative purposes with only slight modifications. Moreover, GC–MS with EI ionization enabled identification of compounds (mostly previously unknown) present in Solanaceae samples based on the fragmentation spectra with no need to use standard solutions.

Corresponding author. E-mail address: [email protected] (M. Woźniakiewicz).

https://doi.org/10.1016/j.microc.2019.104098 Received 10 April 2019; Received in revised form 12 July 2019; Accepted 12 July 2019 Available online 18 July 2019 0026-265X/ © 2019 Elsevier B.V. All rights reserved.

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In order to compare the results obtained for all tested samples of psychoactive Solanaceae leaves and seeds, and compare samples with each other, the authors decided to use chemometric tools – principal component analysis (PCA) and cluster analysis (CA). To the best of authors' knowledge, there have been no scientific reports about using those methods for the nightshade plants, but there were some reports about their successful application in the case of other psychoactive plants as well as for legal highs discrimination [19–21]. In the case of those methods, the information about the detailed structure of each compound was not needed. Atropine and scopolamine concentration as well as other matrix constituents analytical signal (peak area) constituted for the only sufficient information. All compounds were detected based on the mass spectra and retention time.

Table 1 Information about tested samples of Solanaceae plants. No.

Codea

Leaves 1 LBP

2. Materials and methods 2.1. Materials and samples Methanol (MS purity grade), atropine (Atr), scopolamine hydrobromide (Sco), caffeine (IS1) and Supel™ QuE sorbents: PSA + MgSO4, Z-Sep, and Z-Sep+ were purchased from Sigma-Aldrich (St. Louis, MO, USA). Atropine-D3 (IS2) was purchased from LGC Standards (Bury, Lancashire, UK). Carbon powder SimpliQ Carbon SPE Bulk Sorbent was bought from Agilent Technologies (Santa Clara, CA, USA), while Chlorofiltr® from BioAnalytic (Gdańsk, Poland). Ultrapure water was generated in our laboratory in a Mili-Q system from Merck-Milipore (Darmstadt, Germany). Samples of Solanaceae leaves and seeds were collected in gardens in Northern (N1-N3) and Southern (S1) Poland as well as in the Botanic Garden of Jagiellonian University in Kraków (BG), Poland (Table 1). The plant material was dried at 40 °C, placed in paper bags, and stored in a dry and dark place. After that, the plant material was ground (5–15 μm) and transferred into amber glass vials and kept in a desiccator.

2 3 4 5 6

LBP1 LBP2 LBS1 LBS2 LBS3

7

LBS4

8

LBN1

9

LBN2

10 11 12 13 14 15 16 17 18 19

LDM LDS1 LDS2 LDS3 LDS4 LDSN LDI LSC LSL LAB

Seeds 20 SBA 21 SDMp1 22 SDMp2 23 SDMN1

2.2. Analytical method The MAE/QuEChERS/GC–MS method of Atr and Sco determination and qualitative analysis of the plant matrix constituents in tested samples was previously developed by Ciechomska et al. [18]. The open cavity microwave-assisted extraction was carried out in a CEM 5 microwave-assisted sample preparation system (CEM, USA) equipped with Xpress® PFA extraction vessels (50 mL). The ground plant material (100 mg of leaves, 50 mg of seeds) was extracted in 10 mL of methanol. The temperature of extraction was set up to 50 °C for leaf and 60 °C for seed extraction. The extraction was carried out for 22 min in the case of leaves and 13 min in the case of seeds (including the ramping temperature time – 10 min for both). The GC–MS system consisting of a 6850 Series II gas chromatograph and a 5975C MSD mass spectrometer (Agilent Technologies, USA) was employed. A 30 m HP-5 ms capillary column (30 m long, 0.25 mm i.d., 0.25 μm film thickness, Agilent Technologies, USA) was used. In order to determine atropine and scopolamine, all extracts were purified by the QuEChERS method using 5 mg of graphitized carbon black (GCB) and 70 mg of Z-Sep + per 500 μL of leaf extracts; 100 mg of Z-Sep + per 500 μL of seed extracts. The GC–MS analysis was carried out in a SIM mode whereas the detection of other matrix constituents was conducted without the clean-up stage, using a scan mode. All samples were derivatized with BSTFATMCS. The oven temperature program was set up to 75 °C for 0.5 min, increased to 300 °C (rate: 30 °C min−1) and held for 3 min. Helium was used as carrier gas (1.0 mL min−1). Splitless injection of 1 μL was performed at 280 °C. The MS transfer line temperature was set up to 280 °C and the ion source temperature to 230 °C. Electron impact ionization operated at the energy of 70 eV, and the mass range for the MS detector in scan mode (for qualitative analysis) was from 45 to 450 m/z. In the SIM mode (for quantitative analysis), following m/z values were

24

SDMN2

25 26 27 28 29

SDMS1 SDMS2 SDMS3 SDS SDSN1

30

SDSN2

31 32 33

SSL SAB SHN

Species Brugmansia pittieri (Saff.) Moldenke Mixture of different specimen Brugmansia pittieri (Saff.) Moldenke Brugmansia pittieri (Saff.) Moldenke Brugmansia pittieri (Saff.) Moldenke Brugmansia pittieri (Saff.) Moldenke Brugmansia pittieri (Saff.) Moldenke Brugmansia pittieri (Saff.) Moldenke Brugmansia pittieri (Saff.) Moldenke Brugmansia pittieri (Saff.) Moldenke Datura metel L. Datura stramonium L. Datura stramonium L. Datura stramonium L. Datura stramonium L. Datura stramonium L. Datura inoxia Mill. Scopolia carniolica Jacq. Scopolia lurida Dun. Atropa belladonna L. Brugmansia arborea L. Datura metel L. Datura metel L. Datura metel L. Young Datura metel L. Old Datura metel L. Datura metel L. Datura metel L. Datura stramonium L. Datura stramonium L. Young Datura stramonium L. Old Scopolia lurida Dun. Atropa belladonna L. Hyoscyamus niger L.

Place of sample collectinga N1 N1 N1 S1 S1 S1 S1 N2 N2 N1 BG BG BG BG N3 N2 BG BG BG Purchased Purchased Purchased N1 N1 S1 S1 S1 BG N3 N3 BG Purchased Purchased

Place of growth: N – northern Poland; S – southern Poland; N1 – Bartoszyce, Poland; N2 – Krawczyki, Poland; N3 – Białystok, Poland; S1 – Kraków, Poland; BG – Botanic Garden of Jagiellonian University in Kraków, Poland. a Numbers at the end of the code indicate the number of the specimen; the lack of the number means that only one specimen was investigated.

acquired: 138 for Sco-TMS, 124 for Atr-TMS, 127 for IS2-TMS and 194 for IS1. During the qualitative analysis, the addition of internal standards (caffeine and atropine-D3) was not taken into account in the chemometric analysis. Samples of LBN1, LBN2, and SAB required additional treatment, as the concentration of atropine and scopolamine was below LOQ of the ordinary method. In these cases the extracts were 10 times concentrated before derivatization step, referring to the method described in our previous work (only for a quantitative analysis). All chromatograms and mass spectra were deconvoluted using AMDIS 2.7 (NIST, USA) software. In the case of every plant material, three samples were collected, extracted, and then analyzed using GC–MS in triplicate (three consecutive injections per sample). The final concentration values were expressed as an average from all 9 results of Atr and Sco concentration for each sample. In the case of other detected compounds, the value of the average peak area of each compound in each sample was calculated and treated as the analytical response in further analysis. 2

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2.3. Chemometric methods

Table 2 Concentration of atropine and scopolamine in Solanaceae plants.

Chemometric analysis of acquired results was performed using Statistica 12.5 software (StatSoft Inc., Tulsa, OK, USA). The principal component analysis (PCA) was carried out based on the correlation matrix. The number of principal components was chosen according to a scree plot test. The hierarchical cluster analysis was conducted using Ward's agglomeration method and Euclidean distance. The number of groups was chosen based on Mojena stopping rule [22,23]. Since, according to the Lilliefors test for normality, the obtained data (Atr and Sco concentration as well as other matrix constituents peak area) was not normally distributed, the variables were transformed into log(xi + 1), where xi was the single analytical signal. All transformed data was subsequently standardized.

Sample ID Brugmansia pittieri leaves LBP LBP1 LBP2 LBS1 LBS2 LBS3 LBS4 LBN1 LBN2 Datura metel leaves LDM Datura stramonium leaves LDS1 LDS2 LDS3 LDS4 LDSN Datura inoxia leaves LDI Scopolia carniolica leaves LSC Scopolia lurida leaves LSL Atropa belladonna leaves LAB Brugmansia arborea seeds SBA Datura metel seeds SDMp1 SDMN1 SDMN2 SDMp2 SDMS1 SDMS2 SDMS3 Datura stramonium seeds SDS SDSN1 SDSN2 Scopolia lurida seeds SSL Atropa belladonna seeds SAB Hyoscyamus niger seeds SHN

3. Results and discussion 3.1. Detected and determined compounds According to the results of the qualitative MAE/GC–MS analysis of tested samples, 172 compounds (including atropine and scopolamine) were detected. The mass spectra of all detected compounds were collected in the database [24]. 28 among detected compounds were unambiguously identified based on NIST 11 database (NIST, USA), with the verification conducted by an experienced MS analyst. In the case of 81 compounds, it was possible to classify them as the type of a chemical compound (saccharide, carboxylic acid, etc.) or recognize them as isomers of identified compounds. It was possible as the NIST 11 search engine pointed them out e.g. as several isomeric saccharides with similar match index. Nevertheless, 63 of detected compounds were not identified. Among all detected compounds, 156 were present in leaves and 76 in seeds whereas 96 only in leaves and 16 only in seeds (Table S1, Supplementary Electronic Material). Among leaves (Table S-2, Supplementary Electronic Material) 105 compounds were present in Brugmansia, 84 in Datura, 74 in Atropa, 74 in Scopolia. Moreover, 23 compounds were found only in Brugmansia, 8 only in Datura, 17 only in Atropa, 8 only in Scopolia. In the case of seeds (Table S-3, Supplementary Electronic Material) 25 compounds were found in Brugmansia, 59 in Datura, 22 in Atropa, 19 in Scopolia, 10 in Hyosyamus. Furthermore, 4 compounds were detected only in Brugmansia, 29 in Datura, 1 in Atropa, 3 in Scopolia, 5 in Hyosyamus. Atr and Sco were determined in all tested samples and the found concentrations were collected in Table 2 and shown in Fig. S-1 (see Electronic Supplementary Material). The results of that study indicate that there is a difference in the concentration of Atr and Sco, depending on the examined plant organ as well as the genus and species. The highest concentration of Atr was determined in the case of leaves in Scopolia lurida (LSL, 2427 ± 365 μg g−1) and in the case of seeds in Atropa belladonna (SAB, 4403 ± 298 μg g−1). The highest concentration of Sco in leaves and seeds was found in Brugmansia pittieri (LBS3, 1098 ± 114 μg g−1) and Datura metel (SDMN1, 9127 ± 237 μg g−1), respectively. The general observation, similarly to some other reports [5,6,8,11,12], is that the concentration of analytes was higher in seeds rather than in leaves. While analyzing the results obtained for seed samples of Datura genera one can find out that in the case of D. stramonium concentration of Atr exceeded Sco, while in D. metel this relation was inverted, however with some exceptions. It is also identifiable that samples of D. metel collected in southern Poland were characterized by higher levels of Atr than those from the northern region, on the other hand, this relation was opposite in the case of D. stramonium seeds. Analysis of the results obtained for leaves leads to an observation that in the case of D. stramonium, similarly to the seeds, the concentration of Atr is higher that Sco, whereas in Brugmansia genera in almost all tested samples Sco was found on higher levels (see Fig. 2-S in Electronic Supplementary Material). It is particularly worth mentioning, that the discrepancies were observed also among different specimen of the same

Atropine [μg g−1]

Scopolamine [μg g−1]

64.7 ± 6.5 34.9 ± 3.7 36.9 ± 1.23 574.9 ± 40.2 248.0 ± 5.4 501.1 ± 7.3 162.2 ± 7.3 9.4 ± 0.3 10.5 ± 0.8

448.2 ± 8.4 43.3 ± 4.2 33.3 ± 2.9 463.2 ± 19.8 348.7 ± 21.5 1098 ± 114 514.5 ± 45.9 33.9 ± 2.6 43.2 ± 6.2

69.9 ± 7.3

840.4 ± 109.2

758.3 612.3 325.8 173.8 670.1

± ± ± ± ±

19.6 21.0 22.3 25.0 26.3

127.5 ± 4.4 396.4 ± 21.1 179.2 ± 22.4 63.0 ± 3.3 56.3 ± 2.3

195.3 ± 12.2

424.8 ± 9.0

1437 ± 177

965.4 ± 42.5

2427 ± 365

397.4 ± 24.0

279.0 ± 39.0

18.7 ± 1.0

196.7 ± 6.1

1092 ± 52

1924 ± 78 1022 ± 34 932.3 ± 66.8 1983 ± 92 1697 ± 52 2528 ± 78 1778 ± 71

1552 ± 79 9127 ± 237 80.7 ± 7.6 4534 ± 348 6136 ± 83 5833 ± 698 5314 ± 339

469.6 ± 16.5 2204 ± 82 1351 ± 78

142.1 ± 8.8 126.6 ± 2.6 170.5 ± 5.6

1979 ± 51

597.0 ± 22.0

4403 ± 298

2.3 ± 0.4

121.4 ± 8.4

95.0 ± 15.5

species collected from the same place and they were sometimes more significant than the differences between species. However, when comparing seeds collected from young and older specimens, it can be observed that the concentration of analytes was higher in younger plants such as SDMN1 or SDSN1 than in older ones like SDMN2 or SDSN2, respectively (with exception of Sco concentration in SDSN1 and SDSN2). This phenomenon is in accordance with the fact that the biosynthesis of analytes starts around two weeks after the beginning of germination, increases up till the tenth week of growth and start decreasing after that time [8]. On the other hand, mentioned alkaloids can be converted into each other during the biosynthesis, thus the interpretation of the values of their concentration is not always unequivocal [25]. The analyte concentration range coincides with the previously reported [5–10,12–14], however with a few exceptions, which should be highlighted. The Atr concentration in D. metel seed samples, D. inoxia leaf samples, A. belladonna seed samples and Scopolia samples were higher than previously reported, whereas in D. metel leaf samples – lower. In the case of the majority of D. metel samples, the concentration of scopolamine was higher than previously described, while in the case of D. inoxia leaf samples and A. belladonna samples, the concentration of Sco was lower than values published before. The determination of Atr 3

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and Sco in B. pittieri leaves, B. arborea seeds and H. niger seed samples was, according to our best knowledge, never published before. From the forensic as well as medical point of view the crucial fact is that the concentration of Atr and Sco depends on numerous factors like species, place of growth or the plant age but also climate or weather conditions [5]. As one can notice the highest concentration of atropine determined in leaves (2427 ± 365 μg g−1) was around 258 times higher than the lowest one (9.4 ± 0.3 μg g−1) whereas scopolamine in seed samples 3900 times higher (the highest concentration: 9127 ± 237 μg g−1; the lowest: 2.3 ± 0.4 μg g−1). Among Datura seeds samples, which according to Internet forums are one of the most popular Solanaceae plant of recreational use [3], the difference between the highest and the lowest concentration was 2 mg g−1 of Atr and around 9 mg g−1 of Sco. Taking into consideration that the lethal dosage of each of these

alkaloids is about 100 mg for an adult but even a few milligrams in the case of a child, and lower doses can also lead to very severe adverse effects like tachycardia or consciousness disorder, it should be clearly stated that there is nothing like the average safe amount of Solanaceae plant material that can be taken to induce only pseudo-positive effects, like hallucinations. 3.2. Data preprocessing, correlation and ANOVA analysis The first stage of the chemometric insight was the analysis of correlations between the compounds detected in Solanaceae leaves and seeds. It was found out that 109 out of 172 compounds correlated with at least one other compound with an absolute value of correlation coefficient (|r|) higher than 0.75. Those compounds were divided into 19 groups, according to a rule that inside each group every compound correlates with at least one other with a |r| higher than 0.75. The decision tree for constructing the groups of correlated compounds was presented in Fig. S-3 (Electronic Supplementary Material). The same analysis was conducted for leaves and seeds separately. In the case of leaves 76 out of 156 compounds and in the case of seeds 33 out of 76 compounds correlated with at least one other compound with an absolute value of correlation coefficient (|r|) higher than 0.75. Inside each group, the compounds that were present in the highest number of samples as well as the compounds present in the samples in which other group members were absent, were chosen as indicative compounds of the group. In the case of cluster analysis, 57 compounds which were not found as indicative for both leaves and seeds, 44 for leaves and 22 for seeds were not taken into account as the input variables in CA. Group members and correlation coefficient values were presented in Electronic Supplementary Material (Tables S-2–S-45). Moreover, the ANOVA analysis of variance was conducted to evaluate whether there were statistically significant differences of the values of each variable (peak area of detected sample compound, concentration of Atr and Sco) among sample groups of different plant organs and species. The evaluation was followed by post-hoc Tukey RIR test (α = 0.05). In the case of the differences of variance, KruskalWallis non-parametric test (α = 0.05) was used to establish if there were any statistically significant differences between average values of each variable. According to the obtained results, it was decided to exclude 34 compounds from PCA and CA analysis, as they did not describe the variability of the tested samples (see Table S-1). 3.3. Principal component analysis PCA was used to find the discrepancies between samples of different genus, species and the place of growth. Leaves and seeds can be distinguished from each other botanically but also because of the chlorophyll content in leaves. However, when the sample is finely ground the distinguishing process may be difficult. Thus, PCA was conducted for leaves and seeds analyzed together (PCA-LS) and for leaves (PCA-L) and seeds (PCAeS), separately. PCA-LS was performed based on the correlation matrix obtained for 138 compounds as the input (34 compounds were excluded after ANOVA pre-analysis, see Section 3.2). PCA-L was conducted for 109 compounds (47 excluded after ANOVA pre-analysis, 16 not detected in leaves) whereas PCA-S for 50 compounds (26 excluded after ANOVA pre-analysis, 96 not present in seeds). The chosen principal component scatterplots were shown in Figs. 1–3. According to the scree plot obtained for PCA-LS (Fig. 4-S), 9 new principal components (PCs) can be used without losing a significant amount of information as they explain 75.9% of the variance. Separating the PCA analysis of leaves and seeds enabled reduction of the number of PCs down to eight for PCA-L (explaining 82.0% of the variance, see Fig. 5-S) and six for PCA-S (explaining 82.0% of the variance, see Fig. 6-S).

Fig. 1. Principal component analysis scatterplot of Solanaceae leaf samples calculated taking into account Atr and Sco concentration as well as other matrix constituents. PC-L – principal components established taking into account leaves samples only, PC-LS principal components established taking into account leaf and seed samples. 4

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species mentioned above was in some cases higher than among the species. This phenomenon was the result of the different place of growth (Fig. 2) or different specimen of plants. However, when the analysis of the score plot was obtained according to PC-L 1 and PC-L 4, the separation of B. pittieri and D. stramonium was unambiguous (see Fig. 1). This observation proves, that even though PC 1 and PC 2 individually explain more variance than other principal components, using only those two principal components was not sufficient for the proper interpretation of the gained results. Although, PCs higher than PC 5 did not add any further information to the differentiation of examined samples (data not shown). One can also notice that according to the presented score plots, distinguishing between D. stramonium and D. inoxia is not unequivocal, especially when comparing LDI and LDS4 which grew in a different place (more enshadowed) than other D. stramonium plants (data not shown). It was speculated that different conditions of D. stramonium growth had an impact on the similarity of LDS4 and LDI. Nonetheless, other reasons like e.g. genetic determinant cannot be excluded, yet an explanation of the observed phenomenon needs further and more complex research. As it was mentioned above, leaf samples of Datura and Brugmansia were collected from places located in the North and South of Poland. In the case of Datura samples the separation between those collected in different places was unclear or even impossible, however the differentiation between Brugmansia samples from northern and southern Poland was unequivocal according to PC-LS 2, PC-LS 4 and PC-LS 6 as well as PC-L 1, PC-L 2, PC-L 3, which was visualized in Fig. 2. 3.3.2. Principal component analysis of seed samples The analysis of the principal components obtained for seeds (Fig. 3) established that PC-LS 1 and PC-LS 3 separated A. belladonna, S. lurida and H. niger from the rest of the samples. This score plot showed also that distinguishing between D. metel, D. stramonium, and B. arborea was not possible, however this samples formed 4 groups. Group number I and number III described two different specimens of D. stramonium collected in northern Poland. Group number II described D. metel and D. stramonium samples collected in southern Poland as well as D. metel of the unknown origin whereas group number IV contained D. metel samples collected in the North of Poland and D. metel and B. arborea that were purchased from the garden stores. Therefore, the differences between samples among one species but of various specimen and place of growth were sometimes more significant than the systematic classification. When the analysis was performed separately for seeds, H. niger, A. belladonna and B. arborea were unequivocally separated according to PC-S 1 and PC-S 2, which was not possible during PCA-LS. S. lurida was separated by taking also PC-S 3 into consideration. While analyzing the score plots, one can notice that D. metel cannot be separated from D. stramonium even when taking into account more variables. Samples of D. metel collected in northern Poland, as well as those purchased from the garden store varied from D. metel, collected from the South and those of unknown origin. Samples of D. stramonium formed a group with the latter ones, however one specimen of D. stramonium collected in the northern part of Poland (SDSN2) was different from all other Datura samples. Therefore, similarly to leaves, place of growth, as well as specimen, also had an impact on the sample content. It is worth mentioning, that in the case of seeds, the analysis conducted separately for these samples gave more unambiguous results that the PCA-LS.

Fig. 2. Principal component analysis scatterplot of Brugmansia pittieri samples collected in Northern and Southern areas of Poland calculated from Atr and Sco concentration as well as other matrix constituents. PC-L – principal components established taking into account leaves samples only, PC-LS principal components established taking into account leaf and seed samples.

3.3.1. Principal component analysis of leaf samples Starting the analysis from the leaf samples (Fig. 1), one can notice that PC-LS 1 separated A. belladonna samples from others and PC-L 1 separated also S. lurida and S. carnolica. While using only PCA-LS, the second principal component (PC-LS 2) was needed to visualize the differences between the plants mentioned above. However, there are some difficulties in distinguishing between B. pittieri and D. stramonium. It could be the result of the fact that Datura and Brugmansia were previously reported as one genus because of similar morphology so one may assume that they share also chemical compounds which contributes to the highest loadings by PC-LS 1 and PC-LS 2. Secondly, one can notice that among samples of B. pittieri as well as D. stramonium a few groups were formed. The similarity of the samples of different

3.4. Cluster analysis The data obtained according to PCA was complemented with cluster analysis which was found to be sufficient as some of the samples could not be distinguished based only on PCA. 3.4.1. Cluster analysis of leaves and seeds together As a result of hierarchical CA, that was carried out for leaves and seeds together (CA-LS), twelve clusters were formed (Fig. 4). Except for 5

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Fig. 3. Principal component analysis scatterplot of Solanaceae seed samples calculated taking into account Atr and Sco concentration as well as other matrix constituents. PC-L – principal components established taking into account leaves samples only, PC-LS principal components established taking into account leaf and seed samples.

one case (CA-LS 6), leaves and seeds samples were unequivocally separated from each other, as seeds were described by clusters CA-LS 1–CA-LS 4 and leaves by clusters CA-LS 5, CA-LS 7–CA-LS 12. The CALS 1 contained D. metel seeds but also one sample of D. stramonium seeds (SDS). According to PCA, this D. metel sample was different than all other Datura samples. Samples of S. lurida seeds, S. carniolica leaves and A. belladonna leaves could be easily distinguished from the others, similarly to PCA, as they formed separated clusters – CA-LS 2, CA-LS 8 and CA-LS 9, respectively. On the other hand, samples of S. lurida leaves, separated according to PCA from other leaf samples, formed one cluster with D. inoxia leaves (CA-LS 7) which according to PCA were similar to D. stramonium (SDS4) which formed another cluster CA-LS 10. Thus D. inoxia could be separated from D. stramonium according to CA, and from S. lurida according to PCA. According to CA-LS, it was possible to distinguish between the specimen of the different age in the case of D. metel seeds (SDMN1, SDMN2, in CA-LS 1 and CA-LS 4, respectively) and D. stramonium seeds (SDSN1, SDSN2). The cluster CA-LS 3 was recognized as very interesting, as it contained samples of various plants seeds that were purchased from the garden stores as well as one sample of D. stramonium seeds (SDSN1). All phenomena mentioned above show that PCA and CA can be treated as complementary methods of Solanaceae sample discrimination. Similarly to PCA, it was possible to discriminate between B. pittieri leaves collected from northern (CA-LS 5) and southern Poland (CA-LS 6, CA-LS 11, CA-LS 12). In the case of the latter ones, the authors noticed the distinction between different specimens, which was proved also by PCA.

respectively. One can notice that the results obtained for CA-L and CA-S correspond to the PCA results more than CA-LS did. In the case of CA-L, samples of LDI and LDS4 were classified in one cluster (CA-L 4) and LSC samples formed a separated cluster (CA-L 1). Thus, these results could be compared to PCA-LS and PCA-L results more than to CA-LS. Furthermore, D. stramonium samples were unequivocally distinguished from B. pittieri – CA-L 5 contained only D. stramonium samples in contrast to CA-L 6–8, which contained Brugmansia samples. The part of the data was in agreement with CA-LS: clusters CA-L 1, CA-L 3 corresponded to CA-LS 8, CA-LS 9, respectively. Cluster analysis performed separately for seeds showed that similarly to data obtained for both leaves and seeds, S. lurida formed a separated cluster CA-S 2. Cluster CA-S 1 contained samples grouped in cluster CA-LS 1 except for SDMN1 (CA-S 2). Moreover, sample SDSN1 which was according to CA-LS classified together with leaf samples was located also in cluster CA-S 1. One can notice that among that group there is a possibility to distinguish between D. stramonium and D. metel seed samples, nevertheless according to the Mojena stopping rule, they were treated as a single cluster. Similarly to the results obtained during PCA and CA-LS, D. metel samples differed according to the place of growth (samples from southern Poland were grouped in cluster CA-S 1, from the northern region in cluster CA-S 5 and CA-S 6). Furthermore, differences between some samples of the various age or specimen were more significant than between species, e.g. as well as SDMN1 (CA-S 3) and SDMN2 (CA-S 6).

3.4.2. Cluster analysis of leaves and seeds analyzed separately Analogically to PCA, CA was also separately repeated for leaves (CA-L) and seeds (CA-S) and was presented on Figs. 5 and 6,

This research, using MAE/QuEChERS/GC–MS as a tool, allowed 172 compounds to be detected and Atr and Sco to be determined in 34 Solanaceae leaf and seed samples of a different genus, species, place of

4. Conclusion

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Fig. 4. Hierarchical clustering diagram obtained for all Solanaceae samples calculated from Atr and Sco concentration as well as other matrix constituents GC–MS signal. Separation of clusters done by the Mojena stopping rule at 37.52.

Fig. 5. Hierarchical clustering diagram obtained for Solanaceae leaves samples calculated from Atr and Sco concentration as well as other matrix constituents GC–MS signal. Separation of clusters done by the Mojena stopping rule at 31.46.

growth or specimen. Using PCA and CA enabled to distinguish some groups of samples. It was found out that Atr and Sco concentrations were not the main variables that implicated differences between the

tested samples. Leaf samples of A. belladonna, S. lurida and S. carniolica, B. pittieri as well as seed samples of A. belladonna, S. lurida and H. niger were distinguished from others according to PCA and CA. In the case of 7

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Fig. 6. Hierarchical clustering diagram obtained for Solanaceae seeds samples calculated from Atr and Sco concentration as well as other matrix constituents GC–MS signal. Separation of clusters done by the Mojena stopping rule at 25.40.

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leaf samples of D. inoxia, and D. stramonium or seed samples of D. metel, D. stramonium, B. arborea factors other than the species (e.g. the place of growth) had sometimes a bigger impact on the differences between tested samples. In the cases of leaf and seed samples, PCA and CA were also helpful in distinguishing between samples of the same species but different age (D. metel leaves, D. metel, and D. stramonium leaves). Thus, it was proved that not only does the genus and species influence on the chemical composition in plant tissues but other factors are often more significant. However, because the conditions of tested plants growth were not controlled before sample collecting, choosing the factor that had the biggest impact on the differentiation is not unequivocal. It is worth mentioning that in some cases (some samples of D. stramonium and Brugmansia pittieri leaves) the differences between various specimen within the same species were more significant than between different genus or species so it can be concluded that the genus and species are not always the major factor that implicit the content of the plant samples.

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Acknowledgments This study was funded by the Ministry of Science and Higher Education in the frame of Iuventus Plus 2015-2017 program (M. Woźniakiewicz, grant no. IP2014 052273). The research was carried out with equipment purchased thanks to the financial support of the European Regional Development Fund within the framework of the Polish Innovation Economy Operational Program, Poland (contract no. POIG.02.01.00-12-023/08). Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.microc.2019.104098. 8

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