Original Articles

By Ms. Shuai-Hua Chen , Prof. Xiao-Ru Li
Corresponding Author Prof. Xiao-Ru Li
Research Center of Modernization of Chinese Medicine,Central South University, - China 410083
Submitting Author Ms. Shuai-Hua Chen
Other Authors Ms. Shuai-Hua Chen
College of Chemistry and Chemical Engineering,Jiangxi Normal University of Science and Technology, - China 330013


Herbal pair Ephedra sinica Stapf-Zingiber officinal Rosc, GC-MS, alternative moving window factor analysis, Temperature-programmed retention indices (PTRIs), volatile oil, common constituents

Chen S, Li X. Analysis Of Common Volatile Constituents Between Herbal Pair Ephedra Sinica Stapf-zingiber Officinale Rosc And Its Single Herb By Gc-ms Combined With Amwfa Method. WebmedCentral CHINESE MEDICINE 2010;1(10):WMC001071
doi: 10.9754/journal.wmc.2010.001071
Submitted on: 25 Oct 2010 08:20:50 AM GMT
Published on: 25 Oct 2010 09:16:29 AM GMT


Analysis of common volatile constituents between herbal pair (HP) Ephedra sinica Stapf (ESS)-Zingiber officinal Rosc (ZOR)and its single herb was performed by the method of GC-MS and alternative moving window factor analysis (AMWFA). Identification of the compounds was also assisted by comparison of temperature-programmed retention indices (PTRIs) on the OV-1 column with authentic samples. AMWFA analysis indicates that there are 13 common volatile constituents in HP ESS-ZOR and single herb ESS, and 36 common volatile constituents in HP ESS-ZOR and single herb ZOR, and 5 common volatile constituents among these three systems, and volatile chemical components in HP ESS-ZOR are mainly from single herb ZOR.


The compatibility of Ephedra sinica Stapf (ESS) with Zingiber officinal Rosc (ZOR) either enhances efficacy of the medicine or reduces noxious side effect of the medicine, which could improves clinical curative effects [1]. ESS has the effects of diaphoretics pungent in flavor and warm in property, gently clear floating upward, which is the first medicine of relieving exterior syndromes by dispelling pathogenic wind, and ZOR has the effects of diaphoretics pungent in flavor and warm in property, and powerful force of the diaphoresis. The compatibility of ESS-ZOR has the effects of relieving exterior syndromes by dispelling pathogenic wind, promoting skin eruption by dispelling pathogens, and arresting bleeding. These two herbal medicines are often used together for affection by exopathogenic wind-cold shown as aversion to cold, fever, headache and anhidrosis to expel exogenous pathogenic factors and relieving exterior syndrome. Essential oil of the recipe has the anti-inflammatory effect[2],but its chemical components have not been reported yet.
The aim of this research work is at systematically investigating of the volatile chemical components of ESS, ZOR, and the HP by the method of gas chromatography—mass spectrometry (GC-MS) and alternative moving window factor analysis (AMWFA) [3,4] , and then carrying out qualitative and quantitative analysis between HP and its single herbal medicines, i.e. ESS, ZOR, by means of chemometric techniques and temperature-programmed retention indices (PTRIs). By comparison analysis, the differences and the sameness of volatile components between the recipe and its single herbal medicines can be gotten, and a further knowledge about the mechanism of changes of the chemical components in herbal pairs (two herbal medicines used together) can be obtained. To our best knowledge, it is the first report on the volatile components of the recipe. The application of PTRIs has greatly improved the accuracy of qualitative analysis.
Generally speaking, the analysis of volatile oil is usually conducted with GC–MS, which contributed greatly to the characterization and identification of volatile chemical components in complex systems. However, it is difficult to assess the purity of chromatographic peaks by general GC and the peak inspected as one component may be mixture of several components, because the boiling points of compounds are close to each other, then co-elution of two or more different compounds is very possible. For these overlapping peaks, it is hard to identify the component and even possible to lead to wrong conclusion by direct searches in MS database alone, and it would be also difficult to determine the area of each component in them.
Furthermore, temperature-programmed retention index (PTRI) [5-6] is also employed to the identification of compound. Richmond [7-9] thought that IuT can be highly reproducible on a given capillary column, whose stationary phase is fixed, if working variables, such as carrier flow-rate, film thickness and linear temperature programming rate are standardized. For those substances whose mass spectra are similar to each other, the identification is difficult by directly mass spectral searching alone. However, they have different retention times, from which the qualitative results can be confirmed and determined. Therefore, by using PTRIs, the identification of the compound will be more accurate to some extent. The combination of PTRIs and chemometric techniques can greatly improve the identification of chemical components in volatile oils of herbal medicines.


2.1 Extraction of essential oil
Single herbal medicines ESS and ZOR were purchased from Jiuzhitang medical materials corporation (Changsha,Hunan,China), and they were both identified by Institute of Materia Medica, Hunan Academy of Traditional Chinese Medicine and Materia Medica(Changsha,Hunan,China). n-Alkane standard solutions of C8–C20 (mixture no. 04070) and C21–C40 (mixture no. 04071) were purchased from Fluka Chemika.
Extraction of essential oil of the recipe: 100g of dried powder of each single herbal medicine, HS and RS, were weighted exactly and mixed and then processed according to the standard method described in Chinese Pharmacopoeia (2000 version)[10].
Extraction of essential oil of single herbal medicine was carried out by the same method as mentioned above.
2.2 Instruments and experimental conditions
The GC-MS instrument was QP2010 with OV-1 capillary column (30mm ×0.25mm I.D.). In analysis, the column temperature was set at 40? initially, which was then increased to 120? at a rate of 2?/min and finally to 230? at a rate of 10?/min (maintained for 15 min).The inlet temperature was kept at 250? and interface temperature at 280?. The carrier gas was Helium with a constant flow-rate of 1.0ml/min.To the experimental conditions of the mass spectrometer, electron impact (EI+) mass spectra were recorded at 70 eV. Ionization energy in full scan model was in the ranges of 20−400 amu with 3.8 scan/s. The ionization source temperature was set at 230?.
2.3 Retention indices
Van Den Dool and Dec Kratz[11] proposed a quasi-linear equation for temperature-programmed retention indices as follows (Eq. 1):

IuT =100×(n+(tu-tn)/(tn+1-tn)) ( 1 )

where IuT is the temperature-programmed retention index of the interest,tn ,tn+1 , tu are the retention time in minutes of the two standard n-alkanes containing n and n+1 carbons of the interest, respectively. This equation was used to calculate retention indices in the research work.
2.4 Data analysis
Data analysis were performed on a Pentium based IBM compatible personal computer, and all programs of chemo metric resolution methods were coded in MATLAB 6.5 for windows, and all the calculations were performed on a Pentium IV processor based IBM compatible personal computer. Resolved spectra were identified by matching with the standard mass spectral database of the National Institute of Standards and Technology (NIST).

Results and discussion

3.1 Resolution of overlapped peaks by AMWFA
For the sake of resolving the overlapping peaks, many chemometric methods, such as evolving factor analysis (EFA) [12,13], windows factor analysis (WFA) [14-16], heuristic evolving latent projections (HELP) [17-19], sub window factor analysis (SFA) [20-22], evolving window orthogonal projection (EWOP) [23-24], and evolving sub window comparison (ESC) [24] , have been developed to provide more information for chemical analysis both in chromatographic separation and in spectral identification, which make it possible to extract “pure” spectra from complex chromatograms. These methods can achieve the true solution successfully but can’t extract “pure” spectra for the embedded peaks and therefore determine the number of common components in different samples rapidly. In order to solve this problem, a new proposed chemo metric method, named alternative moving window factor analysis (AMWFA) [25], was introduced. AMWFA is the extension and combination of multi-component spectral correlative chromatography (MSCC)[26] and sub window factor analysis (SFA) [20-22].This method could use the cross-information hidden in two systems to determine the number of common components in different samples and then to identify their corresponding spectra of common components automatically.
The total ion chromatograms (TIC) of herbal medicines ESS (A), ZOR (B) and the HP (C) are shown in Fig.1, respectively. To illustrate how to extract the pure mass spectra efficiently, the peak clusters marked X (30.365~31.446min) and Y (30.364~30.964min) are picked out as examples and then processed by AMWFA.
Here, peak cluster X is taken as the base matrix, and peak cluster Y as the target matrix. First, MSCC and inverse projection MSCC (IP-MSCC) are employed to see whether the two peak clusters contain common components. The results of MSCC and IP-MSCC are shown in Fig.2, which indicates that the mass spectra features of the compounds in peak cluster X are highly correlated with that in peak cluster Y and then the two peak clusters contain common components. The result obtained by common rank analysis (Fig.3) shows clearly that the number of common components in the two peak clusters is 3, because the first three values (No.1 , No.2 and No.3) on the Y axis are almost zero, and the values after No.4 on the Y axis become on the increase gradually. At the same time, we can also obtain the common rank map and spectral auto-correlative curves by AMWFA, as shown in Fig.4 (A) and (B).
Resolution of pure mass spectra for components are followed to conduct after the number of common components in the two systems, i.e. perk cluster X and perk cluster Y, has been obtained. According to AMWFA, on the region in which the number of common components is equal to one, the corresponding pure mass spectrum could be acquired from the eigenvector. We take the range 5-40 of X as the base matrix, and then the moving window searching was conducted on the range 18-165 of Y with a fixed window size 2, then we can obtain the common rank map as shown in Fig.4 (A). During the process of moving window scanning, the mass spectra and the similarity between the two close mass spectra were obtained by calculation with formula. The spectral auto-correlative curve was also acquired, as shown Fig.4 (B), by plotting of similarity vs retention time. In Fig.4 (A), we can see clearly that there are three regions (48-150,152-179,190-228), in which the number of common components is close to 1, in the common rank map, and three flat regions (indicated by R1 ,R2 and R3 in Fig.4 (B)) with similarity being close to 1 appear in the corresponding spectral auto-correlative curve. If the number of common components is equal to 1, a pure spectrum can be acquired from the corresponding region with correlation coefficient close to 1 in the spectral auto-correlative curve. In the Fig.4 (B), three flat parts indicated by R1, R2 and R3 show the regions, in which the three identified mass spectra were picked out. By matching search from NIST107 standard mass spectral database, the resolution result by AMWFA shows the three
common components in peak clusters X and Y are [E]-3(10)-caren-2-ol(R1) 3,7-dimethyl-1,6-octadien-3-ol (R2), and 3,7-dimethyl-1,6-octadienal (R3) respectively, with the match similarity of 0.9160, 0.9850and 0.9705. The pure chromatograms of peak clusters X and Y are showed in Fig.8 and Fig.9 which indicates that both peak clusters X and Y are overlapping ones of three components Fig.5, Fig.6 and Fig.7 shows their resolved mass spectra. In Fig.5, Fig.6 and Fig.7, a , b and c were standard mass spectra of R1 , R2 and R3, respectively.
3.2 Quantitative analysis of volatile components
The overall volume integration method [22-24] was used for all the chromatogram peaks in order to obtain the quantitative results of each component. In total, 43, 59, and 67 volatile active components in volatile oil of ESS, ZOR, and HP were respectively determined qualitatively and quantitatively, accounting for 77.85%, 66.51% and 87.54% total contents of volatile oil of ESS, ZOR and HP respectively. Their relative content and retention indices (RI) are given in Table 1, where the components are listed in order of elution on the OV-1 column.
3.3 Comparison of volatile components between the HP and single herbs
From Table 1,the main volatile chemical components are α, α,4-trimethyl-3-cyclohexene-1-methanol/C10H18O (31.9%), n-hexadecanoic acid (6.36%), D-limonene(3.95%),4-(1-methylethyl)-1-cyclohexene-1-carboxaldehyde (3.08%), 1-methyl-3-(1-methylethyl)- benzene (2.27%) for ESS,and camphene(6.4%),(E)-3,7-dimethyl-2,6-Octadien-1-ol( 4.77%),α-farnesene (4.13%), [s-(R@,s@)]-3-(1,5-dimethyl-4-hexenyl)-6-methylene-cyclohexene(4.09%),α-pinene (3.22%),α, α,4-trimethyl-3-cyclohexene-1-methanol(3.15%) for ZOR, and β-phellandrene (16.33%) ,camphene(11%),α, α,4-trimethyl-3-cyclohexene-1- methanol (6.06%), [s-(R@,s@)]-3-(1,5-dimethyl-4-hexenyl)-6-methylene-cyclohexene (4.33%),α- farnesene(4.17%), borneol(4.1%),.α-pinene(3.4%) for the HP. Compared with HP , 28 and 23 chemical components disappeared in the essential oil of the ESS and ZOR respectively, such as D-Limonene and dodecanoic acid from ESS, 2-Heptanone and 3,7-dimethyl -2-octen-1-ol from ZOR. Meanwhile, 23 new chemical components, such as 2-decanol and bornyl acetate, appeared in the essential oil of HP. Especially, the content of main component, α, α, 4-trimethyl-3-cyclohexene-1- methanol, in ESS was only 6.06% in the HP.
This is based on some chemical reactions taking place during the process of decocting two single herbs, which usually include oxidation, reduction, condensation, and especial hydrolysis. In addition to chemical reactions, much attention should be paid to physical effects like solubilization effect and co-dissolving effect in decoction of two single herbs, which is very popular in traditional Chinese medicinal prescriptions [30-32].


In this paper, the essential oils of the herb pair and two single herbs ESS and ZOR have been investigated for the first time. By applying chemometric method upon two-dimensional data together with PTRIs, three essential oil samples from the recipe, and two single herbs ESS and ZOR were analyzed and compared regarding their qualitative and relatively quantitative characteristics. The information about the chemical constituents of essential oils is useful and necessary to the further pharmacological study of the recipe ESS-ZOR, which will certainly help us to utilize the recipe ESS-ZOR better. It is worthy noting that the application of AMWFA to the characterization of the components of essential oils is more convenient for the comprehensive comparison between the complex systems. In addition, the results obtained showed that the accuracy of qualitative analysis could be greatly enhanced by the combination of PTRIs. The strength of the chemometric techniques assisted with PTRIs in obtaining more information from the GC–MS data is demonstrated in this investigation.


[1].Gemperline P J. J Chem Inform Comp Sci, 1984, 24: 206-212.
[2]. Shen YJ. Study on Exterior-Releasing Drugs and Prescriptions in Traditional Chinese Medicine. Traditional Chinese Medicine Press, Beijing, China, 2005, pp.198-199.
[3].Zeng ZD, Liang YZ, Wang YL. J Chromatogr A , 2006,1107: 273 – 285.
[4].Yi LZ, Liang YZ, Zeng ZD. Chem J Chin Univ, 2006, 27: 2052 – 2055.
[5].Cavaleiro, C, Salgueiro, L.R.  J Chromatogr A, 2004, 1033: 187-190.
[6]. Hudaib, M., Speroni, E. J Pharm Biomed Anal, 2002, 29: 691-700.
[7]. Richmond, R. J Chromatogr A, 1996, 742: 131-134.
[8]. Richmond, R, Pombo-Villar, E. J Chromatogr A, 1998, 811: 241-245.
[9]. Richmond, R .J Chromatogr A, 1996, 724:229-234.
[10].Chinese Pharmacopoeia Committee, Chinese Pharmacopoeia, People's Health Press, Beijing, China. 2000, Appendix 62.
[11].Vanden Dool H, Kratz P D.  J Chromatogr , 1963,11: 463 – 471.
[12]. Maeder M. Anal Chem, 1987, 59:527-530.
[13]. Maeder M, Zilian A .Chemom Intell Lab Synt, 1988, 3: 205-213.
[14].Malinowsky ER.J Chemom, 1992, 6:29-40.
[15].Malinowsky ER .J Chemom, 1996, 10, 273-279.
[16].Shao XG, Li MQ. Chin J Chromatogr, 2001, 19:184-187.
[17]. Kvalheim OM, Liang YZ. Anal Chem, 1992, 64: 936-946.
[18]. Liang YZ, Kvalheim OM, Keller HR. Anal Chem, 1992, 64: 946-953.
[19]. Lu F, Li S. Anal Chimi Acta, 2007, 589:200-207.
[20]. Manne R, Shen HL, Liang YZ. Chemom Intell Lab Syst, 1999, 45:171-176.
[21]. Fan G, Liang YZ, Chau FT. J Sep Sci, 2003, 26: 112-122.
[22].Huang LF, Zhong KJ. Anal Chimi Acta, 2007, 588: 216-223.
[23].Guo FQ, Liang YZ. J Chromatogr A, 2003, 17:99-110.
[24].Xu CJ, Liang YZ .Anal Chimi Acta, 2001, 428:235-244.
[25].Zeng ZD, Liang YZ, Wang YL. J Chromatogr A, 2006, 1107: 273-285.
[26]. Hu Y, Liang YZ, Li BY. J Agric Food Chem,2004, 52: 7771-7776.
[27]. Gong F, Peng YG. Acta Pharm Sin,1999, 3:214-217.
[28].Gong F, Peng YG .Chem J Chin Univ,1999, 2: 199-203.
[29].Shen HL, Liang YZ. Sci China B,1998, 1: 21-29.
[30].Huang X, Ren P. Chin Traditional and Herbal Drugs,2001, 32: 29-31.
[31].Cao PX, Liang GY. Chin Traditional and Herbal Drugs,2001, 32: 24-26.
[32]. Rong R, Yuan J R. Chin Traditional and Herbal Drugs,2001, 32: 20-21.


This work is financially supported by the National Nature Foundation Committee of PR China

(Grant Nos.20235020 and 20475066) and the Cultivation Fund of Key Scientific and Technical

Innovation Project, Ministry of Education of China (No. 704036).

Source(s) of Funding

This work is financially supported by the National Nature Foundation Committee of PR China

(Grant Nos.20235020 and 20475066) and the Cultivation Fund of Key Scientific and Technical

Innovation Project, Ministry of Education of China (No. 704036).

Competing Interests



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