Computer-aided method for identification of components in essential oils by 13 C NMR spectroscopy

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1 Analytica Chimica Acta 447 (2001) Computer-aided method for identification of components in essential oils by 13 C NMR spectroscopy Marcelo J.P. Ferreira a, Mara B. Costantin a, Patrícia Sartorelli a, Gilberto V. Rodrigues b, Renata Limberger c, Amélia T. Henriques c, Massuo J. Kato a, Vicente P. Emerenciano a, a Instituto de Química, Universidade de São Paulo, Caixa Postal 26077, São Paulo, Brazil b Departamento de Química, ICEx, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil c Faculdade de Farmácia, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil Received 20 December 2000; received in revised form 6 June 2001; accepted 13 June 2001 Abstract The aim of this paper is to present a procedure based on analyses of 13 C NMR data for identification of known and new chemical constituents in essential oils. A novel program developed to analyze complex mixtures of terpenoid compound was evaluated for the identification of components in the essential oils from leaves of Piper cernuum and Piper regnellii Elsevier Science B.V. All rights reserved. Keywords: 13 C NMR; Essential oils; Computer-aided method; Expert system; Piper sp.; Terpenoid identification 1. Introduction Countless specialist systems have been developed in the last decades seeking the structural determination of novel organic substances which are not recorded in databases yet [1 10]. Among these systems, the SISTEMAT system [11 22] was developed by our research group to assist the processes of structural determination of natural products. In this system and in others, the developed programs were proved to be very efficient to accomplish the analysis and identification for new isolated compounds. However, such systems do not allow the analysis of mixtures of substances, such as in the essential oils, without previous purification, due to the large number of signals. Fig. 1 shows Corresponding author. Fax: address: vdpemere@quim.iq.usp.br (V.P. Emerenciano). an example of 13 C NMR spectra of the essential oil where it is easy to note the difficulties faced by the method due to the great number of signals and overlapping of these. For such cases, we have developed a methodology specifically addressed to analyze 13 C NMR data of constituents present in mixtures. This methodology was applied in the identification of a mixture of triterpenes obtained from the roots of Vernonia cognata (Asteraceae) [23]. From this mixture six triterpenes were recognized by the SISTEMAT system. However, the analysis of volatile oils was not possible due to large number of compounds found in this type of mixture and also because, at that time, SISTEMAT did not have databases of 13 C NMR data of monoterpenes and sesquiterpenes as well. The 13 C NMR spectroscopy has been recently introduced to analyze several classes of compounds /01/$ see front matter 2001 Elsevier Science B.V. All rights reserved. PII: S (01)

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3 M.J.P. Ferreira et al. / Analytica Chimica Acta 447 (2001) in mixtures by the use of various specialist systems [24 32]. The aim of this paper is to introduce a new methodology to the identification of chemical constituents present in essential oils through analyses of 13 C NMR data. The developed method differs from others [31 33] based on signal intensity. 2. Methodology The identification of constituents present in essential oils was based on the analysis of their 13 C NMR spectra and comparison with these of monoterpenes and sesquiterpenes available in the literature. These data were encoded and stored in the SISTEMAT system [19,21]. After we have prepared the 13 C NMR databases that contain around 1300 mono- and 2500 sesquiterpenes, the SISCONST program [14] was developed, in order to analyze mixtures of these compounds. The sequence of information to be supplied to the SISCONST program is the following: 13 C chemical shifts and their multiplicities obtained experimentally from the DEPT spectra; the minimum number of carbon atoms for the substructure search, i.e. this number is demanded by the user, and generally it is the half of the total carbon number of the chemical class studied; the error range admitted by the program, being generally 1.0δ; the gradient, that allows the automatic increase of the error range; specify the possible classes of compounds to be searched. The search process for a probable skeleton is limited to compounds having a substructure containing at least half of the total carbons number. So, from each chosen compound and for its skeleton, the program associates a statistical weight (W s ) calculated by: W s = (N C TNC) 2 (ER/ERM), where N C is 2.1. The SISCONST program The SISCONST program [14] was developed to aid the process of structural determination of natural products by means of analysis of chemical shifts and multiplicities obtained from the 13 C NMR DEPT spectra. The program may predict the most probable skeleton type for a compound under analysis and suggest several substructures with the assigned signals of 13 C NMR. The program matchs the signals of the spectrum obtained with all spectra stored in the database. If a spectrum signal and the its respective multiplicity are present in a determined carbon atom, the signals of the interlinked carbons are matched with the spectrum in question. This searching process is repeated so that the largest fragments of the substructure bearing compatible chemical shifts with the 13 C NMR data from the spectrum are obtained. Except for the reduction process of associative groups [13], the system basically follows the Munk s algorithm [2]. Earlier works using a similarity searching method are reported in the literature concerning the identification of known and new substances through infrared spectra, mass spectrometry, 1 H NMR and 13 C NMR spectroscopic databases [34 46]. Fig. 2. Performance flow chart of the SISCONST program.

4 128 M.J.P. Ferreira et al. / Analytica Chimica Acta 447 (2001) the number of carbons found in the substructure, TNC the total number of carbons, ERM the absolute value of the largest difference between the substructure signals and the corresponding signal of the spectrum in question, and ER is the error range. Thus, the skeleton probability of the substance in question is computed. Table 1 Chemical constituents of the P. cernuum volatile oil with their respective Kovats indexes and relative concentrations analyzed by GC MS KI-DB5 a Chemical constituents Concentration (%) 924 -Pinene Sabinene Pinene Myrcene Terpinene p-cymene Limonene (Z)- -ocimene (E)- -ocimene Terpinolene Copaene Bourbonene Cubebene Elemene Caryophyllene Aromadendrene Humulene Alo-aromadendrene Germacrene-D Viridiflorene Bicyclogermacrene Muurolene Germacrene-A Cadinene (E)-nerolidol Ledol Spathulenol Caryophyllene oxide Globulol Epi-globulol Eudesmol (isomer not identified) Di-epi-cubenol Iso-spathulenol Cadinol + -muurolol Cubenol Not identified Cadinol 2.84 Total Non-identified 0.49 Identified a Kovats index on DB-5 column [54]. For this search process an error range of the 13 C NMR chemical shift is fixed at 1.0δ for the substructure and 5.0δ for skeleton. If this error range is too narrow and does not allow a successful matching, the ranges are automatically increased with a gradient of 0.5δ and 1.0δ for substructures and skeletons, respectively. If the error range of the search reaches the upper limit of 3.0δ for substructures and 10.0δ for skeletons, the program automatically will stop the search, and no substructures and/or skeletons will be presented. Since the system is supposed to create constraints in structural determination, the assignments of the carbon belonging to the substructures are important data as well as their respective chemical shifts. Due to this fact, we introduced the Kalchauser s algorithm [47] which we modified slightly, because the only data Table 2 Chemical constituents of the P. regnellii essential oil analyzed by GC MS KI-DB5 a Chemical constituents Concentration (%) 917 Tricyclene Pinene Sabinene Pinene Myrcene Phellandrene D3-carene p-cymene Limonene+ -phellandrene Terpinene Linalool Terpinen-4 -ol Terpineol Copaene Caryophyllene Aromadendrene Humulene Germacrene-D Bicyclogermacrene Cadinene Germacrene-B (E)-nerolidol Spathulenol Muurolol Muurolol 0.70 Total Non-identified 1.30 Identified a Kovats index on DB-5 column [55].

5 M.J.P. Ferreira et al. / Analytica Chimica Acta 447 (2001) Table 3 13 C NMR DEPT spectral data obtained for crude essential oil from leaves of P. cernuum s d t q which are necessary are the signal values and their multiplicities. Therefore, from this structure search process, the chemical shift values are attributed to the respective carbons during the similarity searching process. Therefore, the reliability of each assignment of the experimental spectrum is achieved by the program with the stored data obtained from the literature. However, so far, this program had only been previously tested for the individual identification, i.e. previously separated and purified compounds. In order to allow the analysis of mixtures, we have increased the data storage capacity of the program to a largest number of chemical shifts and multiplicities into the system during the analysis. The analyses with a minimum of nine coincident signals could be successfully carried out in the analysis of mono- and sesquiterpenes, once this value embodies structures of both compounds. The SISCONST program is able to recognize structures for which the 13 C NMR data are present in the database and structures whose 13 C NMR data are absent in the database. In the last case, the program searches for substructures, parts of a structure compatible with the experimental data that, in many cases, when overlapped, they furnish the complete structure. Various examples of new substances which were tested by the program are shown with excellent results [11,14,16,19,22]. The self-training is the main charactheristic of this program, since it identifies known and new substances present in the sample. Fig. 2 shows the performance flow chart of the SISCONST program. 3. Results The identification capacity of the SISCONST program was evaluated in the analysis of terpene mixtures Table 4 13 C NMR DEPT spectral data obtained for crude essential oil from leaves of P. regnellii s d t q

6 130 M.J.P. Ferreira et al. / Analytica Chimica Acta 447 (2001) Fig. 3. Structures of compounds identified by the SISCONST program. in volatile oil samples obtained from leaves of Piper cernuum and Piper regnellii (Piperaceae family). The system efficiency was based on the comparison of results obtained in the identification of compounds by analysis of 13 C NMR spectra with those obtained by GC MS (gas cromatography mass spectrometry) and by GC KI (gas chromatography Kovats indexes). The data obtained through GC MS and GC KI for P. cernuum and P. regnellii are listed in Tables 1 and 2, respectively. Both samples were also submitted to the aquisition of 13 C NMR data in which 167 chemical shifts and their respective multiplicities were obtained by 45, 90 and 135 DEPT for P. cernuum (Table 3) and 49 chemical shifts for P. regnellii (Table 4). All chemical shifts obtained from the list of the peaks in the 13 C NMR

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8 132 M.J.P. Ferreira et al. / Analytica Chimica Acta 447 (2001) Table 6 Experimental 13 C NMR data from crude essential oil of P. regnellii assigned by SISCONST program Linalool Myrcene -Caryophyllene Bicyclogermacrene Nerolidol Exp. Lit. [19] Exp. Lit. [19] Exp. Lit. [50] Exp. Lit. [50] Exp. Lit. [57] spectra were entered in the program for analysis. The additional parameters of the obtained spectra are described in the Section 4.1. The data of P. cernuum were loaded into the SIS- CONST program which, at the first stage of run, identified the sesquiterpene germacrene-d. The assignment of its 13 C NMR signal was in full agreement with the data previously published in [48]. Then, in the second step, the program continued the analysis and identified additionally three sesquiterpenes: -caryophyllene, germacrene-a and bicyclogermacrene. Thus, successively, the program identified the following compounds: -pinene, spathulenol, -elemene, -pinene and globulol (Fig. 3). The experimental 13 C NMR data for identified compounds was compared to those available in the literature, and they are listed in Table 5. The analysis of the components in the essential oil of P. regnellii was also carried out (Table 6) and the SISCONST program was able to identify sequentially linalool, myrcene, nerolidol, -caryophyllene and bicyclogermacrene. The reliability in each assignment of the compound was obtained in both samples by GC MS, GC KI and 13 C NMR data previously published in [48 53]. In summary, the SISCONST program was able to identify two monoterpenes ( - and -pinene) and eight sesquiterpenes (germacrene-d, germacrene-a, -elemene, -caryophyllene, spathulenol, globulol, bicyclogermacrene and -cadinol) in the essential oil from P. cernuum. In P. regnellii s essential oil, two monoterpenes (myrcene and linalool) and three sesquiterpenes ( -caryophyllene, nerolidol and bicyclogermacrene) were identified. 4. Discussion and conclusions The SISCONST program was capable to identifying efficiently all the constituents present in the volatile oils at the same or superior concentration of 2.26%. The non-identification of minor compounds (<2.26%) is caused by interrelated factors: the small concentration of the constituent in our sample of the volatile oil which did not allow the achievement of the chemical shifts of the compound. This limitation is inherent to the own analysis type. However, if a larger amount of oil were obtained, starting, for example, from a larger biomass, and we had haven more time of accumulation to run the spectrum, we probably could have identified, through 13 C NMR, the other constituents present in smaller amounts. In summary, the method here described allows the correct identification of major compounds present in volatile oils. The method can be suggested as a complementary tool for analysis of essential oils based on

9 M.J.P. Ferreira et al. / Analytica Chimica Acta 447 (2001) GC MS data. One of the advantage of this method is the correct identification of isomers of a certain compound present in the sample or to detect the presence of new compounds in essential oils Experimental Plant material and extraction: leaves of Piper cernuum Vell. and Piper regnellii (Miq.) C.DC. were collected at the Campus of São Paulo University in São Paulo city, Brazil, in June Voucher specimens (Kato-0137 and E. Guimarães-1961, respectively) were deposited at Herbarium of Instituto de Botânica, São Paulo, Brazil. The essential oils were obtained from steam distillation of 570 and 400 g of fresh leaves of P. cernuum and P. regnellii, respectively, in agreement with the literature [54], yielding 102 mg of the crude oil for P. cernuum and 400 mg for P. regnellii C NMR analysis The 13 C NMR spectra were obtained through a Varian 300 MHz, using CDCl 3 as solvent and TMS as internal standard. The 13 C NMR spectra were measured using 45 pulse (13.4 ms) and 3600 pulses; repetition time, 2 s; threshold, 15; signal to noise, 80:1; spectral width, 18.1 khz; data set, 65.5 kilo-words zero-filled; temperature, 25 C GC and GC MS analysis Gas chromatography analysis was performed in a chromatograph (Shimadzu GC-17A) equipped with a Shimadzu GC 10 software, using a fused silica capillary column, DB-5 (30 m 0.25 mm 0.25 m), and a flame ionization detector. Injector and detector temperatures were set at 220 C. The oven temperature was programmed from 60 to 300 Cat3 C/min and helium was employed as carrier gas (1 ml/min) for both analyses. The percentage compositions were obtained from electronic integration measurements, using flame ionization detection without taking into account relative response factors. Gas chromatography mass spectrometry: the sample was analyzed by GC MS, using a Shimadzu capillary GC-quadrupole MS system (QP 5000) operating at 70 ev at the same conditions as described above. The identification of the compounds was performed by comparison of retention indexes (determined relatively to the retention times of a series of n-alkanes) and mass spectra with that available in the system [55,56]. Acknowledgements This work was supported by grants from the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP), the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and by the Fundação Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES). The authors thank Antônio J.C. Brant for helpful discussion during the preparation of the manuscript. References [1] R.K. Lindsay, B.G. Buchanan, E.A. Fergenbaum, J. Lederberg, Applications of Artificial Intelligence for Organic Chemistry: the DENDRAL Project, McGraw-Hill, New York, [2] C. Shelley, M.E. Munk, Anal. Chem. 54 (1982) 516. [3] N.A.B. Gray, in: Computer-Assisted Structure Elucidation, Willey, New York, [4] B.D. Christie, M.E. Munk, Anal. Chim. Acta 200 (1987) 347. [5] K. Funatsu, N. Miyabashi, S. Sasaki, J. Chem. Inform. Comput. Sci. 28 (1988) 18. [6] M. Carabedian, I. Dagane, J.-E. Dubois, Anal. Chem. 60 (1988) [7] M. Will, W. Fachinger, J.R. Richert, J. Chem. Inform. Comput. Sci. 36 (1996) 221. [8] M.E. Munk, J. Chem. Inform. Comput. Sci. 38 (1998) 997. [9] M. Jaspars, Nat. Prod. Rep. 16 (1999) 241. [10] M. Badertscher, A. Korytko, K.P. Schulz, M. Madison, M.E. Munk, P. Portmann, M. Junghans, P. Fontana, E. Pretsch, Chemometrics Intell. Lab. Syst. 51 (2000) 73. [11] J.P. Gastmans, M. Furlan, M.N. Lopes, J.H.G. Borges, V.P. Emerenciano, Química Nova 13 (1990) 10. [12] V.P. Emerenciano, G.V. Rodrigues, P.A.T. Macari, S.A. Vestri, J.H.G. Borges, J.P. Gastmans, D.L.G. Fromanteau, Spectroscopy 12 (1994) 91. [13] J.P. Gastmans, M. Furlan, M.N. Lopes, J.H.G. Borges, V.P. Emerenciano, Química Nova 13 (1990) 75. [14] D.L.G. Fromanteau, J.P. Gastmans, S.A. Vestri, V.P. Emerenciano, J.H.G. Borges, Comput. Chem. 17 (1993) 369. [15] P.A.T. Macari, J.P. Gastmans, G.V. Rodrigues, V.P. Emerenciano, Spectroscopy 12 (1994/1995) 139. [16] G.V. Rodrigues, I.P.A. Campos, V.P. Emerenciano, Spectroscopy 13 (1997) 191. [17] M.J.P. Ferreira, Llm sistema especialista em determinacao estructural de monoterpenos e indòides; M. Sc. Dissertation, IQ-USP (1999). [18] M.J.P. Ferreira, A.J.C. Brant, G.V. Rodrigues, V.P. Emerenciano, Anal. Chim. Acta 429 (2001) 151.

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