|Year : 2020 | Volume
| Issue : 71 | Page : 654-661
Distinguishing the rhizomes of Atractylodes japonica, Atractylodes chinensis, and Atractylodes lancea by comprehensive two-dimensional gas chromatography coupled with mass spectrometry combined with multivariate data analysis
Jie Lu1, WenTing Chen1, BoWen Zhou1, Yang Chen1, XinHong Wang1, Rui An1, Ming Yang2
1 Department of Chemistry, School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
2 Department of Pharmacy, Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
|Date of Submission||06-Feb-2020|
|Date of Decision||27-Feb-2020|
|Date of Acceptance||20-Apr-2020|
|Date of Web Publication||20-Oct-2020|
School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 201203
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Background: In clinical practice, the species of Atractylodes are difficult to identify based on their morphological and chemical features which often leads to confusion. In addition, the composition of volatile components may influence the clinical efficacy of rhizomes of Atractylodes. Materials and Methods: In this study, a comprehensive two-dimensional gas chromatography with mass spectrometry coupled with multivariate data analysis was employed to investigate the differences in the volatile components of the rhizomes of three species of Atractylodes, namely Atractylodes lancea (Thunb.) DC, Atractylodes japonica Koidz. et Kitam, and Atractylodes chinensis (DC.) Koidz. Results: A total of 119 compounds were tentatively identified and confirmed based on the NIST database. Thirty-three samples were well distinguished and the results of two different analytical methods using principal component analysis and partial least-squares discriminant analysis were in satisfactory agreement with one-way analysis of variance. Atractylodin and β-eudesmol can be used to reveal the chemical differentiation and distinguish different species of Atractylodes. Conclusion: The results may provide a reliable reference to quality control and product grade of rhizomes of Atractylodes.
Keywords: Atractylodes rhizome, comprehensive two-dimensional gas chromatography, multivariate-data analysis, partial least-squares discriminant analysis, principal component analysis
|How to cite this article:|
Lu J, Chen W, Zhou B, Chen Y, Wang X, An R, Yang M. Distinguishing the rhizomes of Atractylodes japonica, Atractylodes chinensis, and Atractylodes lancea by comprehensive two-dimensional gas chromatography coupled with mass spectrometry combined with multivariate data analysis. Phcog Mag 2020;16:654-61
|How to cite this URL:|
Lu J, Chen W, Zhou B, Chen Y, Wang X, An R, Yang M. Distinguishing the rhizomes of Atractylodes japonica, Atractylodes chinensis, and Atractylodes lancea by comprehensive two-dimensional gas chromatography coupled with mass spectrometry combined with multivariate data analysis. Phcog Mag [serial online] 2020 [cited 2020 Nov 30];16:654-61. Available from: http://www.phcog.com/text.asp?2020/16/71/654/298649
- 119 compounds were identified based on comprehensive two-dimensional gas chromatography coupled with mass spectrometry between the 33 samples of Atractylodes rhizome
- According to the multivariate data analysis, Atractylodin and β-eudesmol could be used to distinguish different kinds of Atractylodes rhizome.
Abbreviations used: GC × GC-MS: Comprehensive two-dimensional gas chromatography coupled with mass spectrometry; GC-MS: Gas chromatography coupled with mass spectrometry; PCA: Principal Component Analysis; PLS-DA: Partial least-squares discriminant analysis; one-way ANOVA: One-way analysis of variance.
| Introduction|| |
Rhizomes of Atractylodes species have long been used in the preparation of traditional Chinese medicine to treat cold and diarrhea. The history of use of Atractylodes rhizome in patients can be dated back to the Han dynasty, when it was first recorded in the first Chinese pharmacopeia (Shennong's Materia Medica).
According to the literature, the primary pharmacological components in the volatile oils of Atractylodes rhizomes include terpenoids, sesquiterpenes, lactones, and flavonoids involving β-eudesmol, hinesol, atractylon, atractydin, and atractylenolide.,,, Moreover, several new components have been recently reported, such as two thiophene polyacetylene glycosides, one eudesmane-type sesquiterpenoid, one guaiane-type sesquiterpenoid, two C14-polyacetylenes, and four C10-type polyacetylene glycosides.,, Some of them show hepatoprotective and anti-inflammatory activities. The volatile oils of Atractylodes rhizomes demonstrate numerous pharmacological activities such as anticancer, anti-inflammatory, antimicrobial, intestinal immune system modulating activity, and antipyretic activities.,,,,, The anti-gastritis effect was found to be associated with Akt/IκBα/nuclear factor-κB signaling pathway. The bran-processed Atractylodes rhizome has been reported to have a greater effect than that of the crude one.,, In recent years, scholars are more interested in understanding the effect of Atractylodes rhizome in preventing diarrhea.
Comprehensive two-dimensional gas chromatography (GC × GC) is a popular choice for the separation of complex biomolecules. It yields superior separation efficiency by enhancing resolution and increasing peak capacity, in addition to improving the limit of detection., This technique is usually combined with mass spectrometry (MS), which provides effective separation chromatogram and comprehensive mass spectrum for the analyses of complex sample matrixes. The GC × GC-MS is a robust separation method, with a superior resolution and separation efficiency compared with GC × GC or MS alone. Contended with GC-MS, the chemical profiling information revealed by a GC × GC-MS chromatogram after secondary separation is markedly more dispersed and rich.,, Moreover, GC × GC-MS can furnish with the lower detection limit compared to other methods. With the development of MS, GC × GC-MS has developed into a significant method for the rapid identification of constituents in Chinese herbs.
At present, there are three kinds of Atractylodes rhizomes available in the market: Atractylodes lancea (Thunb.) DC, Atractylodes japonica Koidz. et Kitam, and Atractylodes chinensis (DC.) Koidz. However, due to the low content of volatile components, A. japonica Koidz. et Kitam is no more considered as a medicine in recent times, and has not been adopted in the Chinese Pharmacopoeia. This has led to a serious confusion about the clinical efficiency of Atractylodes rhizomes. At present, the standard identification and quantification of Atractylodes rhizome in the Chinese Pharmacopoeia is confined to atractydin. Moreover, this single compound cannot fundamentally distinguish the three species of Atractylodes that we intend to research in this study. A previous study showed that fructooligosaccharides can be applied for the authentication of Atractylodes rhizome and that it can distinguish A. chinensis from A. lancea. In addition, four sesquiterpenoids were determined by GC in the rhizome of Atractylodes. Another study analyzed the chemical composition of A. japonica, A. chinensis, and A. lancea through high-performance liquid chromatography (HPLC)/GC and multivariate data analysis and showed that different species of Atractylodes rhizome significantly differed in the chemical composition., This shows that there are still some deficiencies in the quality control of Atractylodes rhizome, which needs to be further elaborated.
Therefore, in this study, we employed GC × GC-MS approach to investigate and compare different compounds in the rhizomes of A. japonica, A. chinensis, and A. lancea. To this end, 33 samples were tested, and the components in each sample were analyzed. Multivariate data analysis was used to classify three species of Atractylodes rhizome. The results of this study may be beneficial to perform quality control analysis of Atractylodes rhizome. The developed method can be reliably used in the analysis of compounds in Atractylodes rhizome in addition to distinguishing different species of Atractylodes. Furthermore, the developed method may be valuable as a reference method for analyzing other Chinese herbal medicines.
| Materials and Methods|| |
Reagents and chemicals
In this study, 33 Atractylodes rhizome samples obtained from different geographical locations were purchased or collected from vendors. The samples are classified and numbered as C1–C10, K1–K7, and L1–L16. All the samples were authenticated by Professor Zhili Zhao (Shanghai University of Traditional Chinese Medicine, Shanghai, China). HPLC-grade n-Hexane and methanol (SCRC, Shanghai, CN) were used for the sample preparation. [Table 1] presents the information on the samples.
Preparation for essential oil and samples
Each sample of Atractylodes rhizome was powdered and passed through 50 mesh sieves to obtain a fine powder. Next, 1 g of each powder was precisely weighed and soaked in 10 mL of n-hexane in a flask, which was weighed and recorded. Then, the material was extracted in the ultrasonic bath at 40 kHz for 30 min under room temperature. Adding n-hexane to make up for the weight loss during the extraction is a necessary step. Then, the solvent was collected after centrifugation (12,000 rpm, 10 min, 4°C). The supernatant was filtered through a 0.45 μm microporous film before the analysis.
Comprehensive two-dimensional gas chromatography coupled with mass spectrometry analysis
In this study, the GC × GC-MS analysis was conducted using GC/MS-QP2010 Ultra (SHIMADZU, Tokyo, Japan) equipped with a rail autosampler (AOC-20i, SHIMADZU, Japan) and fitted with a two-dimensional column set consisting of an Inter Cap Pure Wax (30 × 0.25 × 0.25) as the first column, followed by a BPX-5 (2.5 × 0.1 × 0.1) as the second column. The volume of the sample injection was 1 μL. The split ratio of the sample was 20:1 and the injector temperature was 300°C. The oven temperature was held at 40°C for 4 min and then changed to 256°C by an increase with 3°C/min. The oven temperature was held at 256°C for 35 min. Hydrogen was used as the carrier gas at a constant flow rate of 0.93 mL/min. The modulation period was 5 s. The mass transfer line temperature was 250°C, ion source temperature was 200°C, and the detector was operated in a scan mode with a mass range of 45–339 m/z.
Multivariate data analysis
In this study, GC image software was employed to acquire total ion chromatograms. For peak identification, there is a forward searching in the NIST Mass Spectral Database (NIST 11) for the resulted peaks. A forward match score of at least 800 was achieved for putative compound identification. The data were then exported to excel files, which included compound identification and peak volume.
Principal component analysis
Principal component analysis (PCA) is a multivariate statistical method that examines correlations among variables. Instead of dealing with a considerable number of variables, PCA identifies fewer principal components to describe both correlations and differences between samples, without losing any significant information. The similarities among samples can be assessed by the score plot. To carry out the PCA analysis, it is necessary to normalize peak volumes among different chromatograms. The chromatograms of 33 samples were handled and 119 peaks were generated, in which a 33 × 119 data matrix, including the peak volumes from GC × GC-MS, was used to discriminate 33 samples and find out the compounds with significant differences. We used SIMCA 14.1 software (Umetrics, Umea, Sweden) for performing PCA.
Partial least-squares discriminant analysis
Partial least-squares discriminant analysis (PLS-DA) is generally used for the supervised classification which is a variant of the multivariate calibration method PLS. The PLS-DA model can be utilized to reveal the inner connection and key makers. In this study, PLS-DA was adopted to enhance the authenticity of discriminating the samples according to their geographical origins. The discriminative compounds were identified by the analysis of variable importance in projection (VIP). In this study, PLS-DA was used to differentiate the geographical origins and chemical compositions of Atractylodes rhizome samples and found the key makers. PLS-DA was analyzed using SIMCA 14.1 software (Umetrics, Umea, Sweden).
One-way analysis of variance and boxplots
Based on the PLS-DA analysis, the components with VIP value, which were >1, were selected by PLS-DA analysis and the sample category was used as the independent variable. The peak volumes of these components in the samples were the dependent variable for one-way analysis of variance (ANOVA) (P < 0.05 was considered statistically significant). All raw data for the probable maker were used for boxplots. SPSS 25.0 software (IBM, New York, NY, USA) was utilized to conduct one-way ANOVA and boxplots.
| Results and Discussion|| |
Tentative identification of volatile components by using GC×GC-MS
[Figure 1] shows the GC × GC-MS contour plots of the volatile oils in Atractylodes rhizome samples. Based on GC × GC-MS with NIST 11, a total of 119 compounds with reverse match factors were found to be >800, mainly including terpenoids and benzene derivatives. [Table 2] lists 119 compounds that match well. These 119 compounds were retrieved by MS library and were verified by reference reports.,,,, In this study, We identified 52 compounds in A. japonica, A. chinensis, and A. lancea. Due to the fact that the species and disparate habitats might cause significant changes in the volatile compounds in Atractylodes rhizome samples, we identified 67 compounds in A. japonica, A. chinensis, and A. lancea samples. By optimizing the chromatographic conditions, we identified 119 compounds with a reverse match factor >800. The results revealed that the number of peaks identified by GC × GC was remarkably higher than that of GC-MS, further indicating that the full two-dimensional GC has higher resolution and sensitivity.
|Figure 1: The comprehensive two-dimensional gas chromatography coupled with mass spectrometry contour plots of volatile oils in Atractylodes rhizome; the picture a is the sample form Atractylodes chinensis; the picture b is from Atractylodes japonica; the picture c is from Atractylodes lancea. Where dark blue means there is a component eluted, and where the darker color means a higher component content|
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|Table 2: Tentative identifications of components in Atractylodes rhizome by gas chromatography × gas chromatography-mass spectrometry|
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Multivariate data analysis
Principal component analysis
To further evaluate Atractylodes rhizome samples collected from China and North Korea, PCA was undertaken to explore the diversities among the chemical nature of Atractylodes rhizome samples and make further efforts to find out the key components. The total variance explained by the two principal components was 43.19% and the PCA score plot showed a separation of Atractylodes rhizome without any specific order [Figure 2]. In addition, in the A. japonica group, two batches of samples from North Korea were separated from other batches of samples, which might be attributed to the differences in their origin. The three-dimensional PCA score plot showed a distance separation of Atractylodes rhizome from diverse species [Figure 3]. Thus, three principal components were found to be appropriate. The primary confusion emerged from the samples of A. chinensis and A. lancea. Sample C10 originated from A. chinensis, whereas it was closer to the group of A. lancea. The results indicated that A. chinensis and A. lancea resembled in their chemical composition. Although the volatile oil contents in A. chinensis had no special features, it is possible that similar contents existed in higher quantities than that of other volatile compounds. The aforementioned results indicated that inherent causes such as place of origin and species could affect the volatile components of Atractylodes rhizome and the classification of species. The results of PCA provided a preliminary overview of the gathering and separation among the different species of Atractylodes. To further understand these differences, we employed a PLS-DA model.
|Figure 2: The principal component analysis result of essential oil of Atractylodes rhizome. The green point means the samples from Atractylodes chinensis; the blue point means the samples from Atractylodes japonica; the red point means the samples from Atractylodes lancea. The closer the points on the graph are, the more similar their chemical composition is|
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|Figure 3: The three-dimensional principal component analysis result of the essential oil of Atractylodes rhizome. The point is the same as Figure 2. The three-dimensional principal component analysis result is the two-dimensional principal component analysis result in which a new principal component is added. The third principal component accounts for 13.2% of all the component data|
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Partial least-squares discriminant analysis
The PLS-DA score plot indicated an obvious distinction among the three species based on the 119 peaks obtained [Figure 4]. It indicated that the chemical components of samples had remarkable differences among the 119 selected peaks, characterizing these differences. In particular, the samples of A. japonica showed distinct components from that of the others. The scores t1 which is fitted to be the first principal component and t2 which is fitted to be the second principal component are new variables summarizing the X-variable, which are the values of the components. According to the cross-validation, the scores t1 and t2 depicted 39.4% of the variation in X (R2X = 0.394) and 81.5% of the variation in Y (R2Y = 0.815) and foresaw 67.8% (Q2 [cum] = 0.678). In this study, the PLS-DA model effectively distinguished the three kinds of Atractylodes rhizome.
|Figure 4: The partial least-squares discriminant analysis result of essential oil of Atractylodes rhizome. The green point means the samples from Atractylodes chinensis; The blue point means the samples from Atractylodes japonica; The red point means the samples from Atractylodes lancea. The abscissa means the difference between the groups, and the difference within the group is seen on the ordinate|
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The VIP values of the primary compounds are ranked from high to low, revealing the differences in chemical components in sample identification. The VIP plot of PLS-DA [Table 3] showed that β-eudesmol and atractylodin may have greater effects than the others on the distinction of different kinds of Atractylodes rhizome. The PCA loading graph showed the degree of original variables in the different components. As shown in [Figure 5], β-eudesmol had a negative contribution to P1, whereas atractylodin had a positive contribution to P1. Moreover, it implies that these two compounds lead to most of these variables. According to the VIP value [Table 3], the contribution of each variable from each compound was quantified for the classification, and we found that the greater the VIP value is, the more significant the variance is in the difference between the various species of Atractylodes. The VIP value of 53 physicochemical components was found to be higher than 1. [Table 3] lists the top 10 components of the VIP value. Especially, the VIP values of β-eudesmol and Atractylodin were both >2. It indicated that these two components may have different contents in the samples. Among them, β-eudesmol and atractylodin were noted as the most important variables for the classification.
|Table 3: The top ten components of variable importance in projection value|
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|Figure 5: Loadings plot of principal component analysis for the key compounds. The abscissa indicates the correlation coefficient between the principal component and the compound, and the ordinate indicates the correlation coefficient between the principal component and the compound. The compound β-eudesmol is in the third quadrant. The compound Atractylodin is in the first quadrant. These are the components furthest from the origin|
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One-way analysis of variance and boxplots
One-way ANOVA was performed for comparison between the peak volumes of β-eudesmol and atractylodin among the three species of Atractylodes. The results showed that there were significant differences among the three species (P < 0.01).
Thus, β-eudesmol and atractylodin can be used to distinguish different species of Atractylodes. To further confirm the accuracy of the results, the boxplots were drawn for the first two components [Figure 6]. The remarkable differences in the contents of these two components were found among three kinds of Atractylodes rhizome. The two components showed different dispersions. There was a high level of the atractylodin in the samples of A. chinensis, a medium level in A. lancea, and a low level in A. japonica, whereas there was a high level of the β-eudesmol in A. lancea, a medium level in A. chinensis, and a low level in A. japonica. In brief, these two components have a significant influence on the classification of the samples.
|Figure 6: Boxplots of.Atractylodin (a) and β eudesmol (b). The relative content of the two components was calculated with the highest peak area in the sample as references. The relative content of Atractylodin in samples C1-10, K1-7 and L1-16 were 0.69, 0.00 and 0.11, respectively. And The relative content of β eudesmol in samples C1-10, K1-7 and L1-16 were 0.03, 0.00 and 0.71, respectively|
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| Conclusion|| |
In this study, GC × GC-MS was developed by integrating PCA and PLS-DA to investigate the volatile components of Atractylodes rhizome comprehensively. The superior separation efficiency of this method allowed us to identify some of the new components from the complex matrix. This method helped us to distinguish various Atractylodes rhizome samples according to their raw profiles. It can be used as a rapid and effective method to distinguish herbal medicines particularly those containing essential/volatile oils.
Jie Lu and WenTing Chen contributed equally to this work.
Financial support and sponsorship
This study was supported by the National Traditional Chinese Medicine Standardization Project (Grant No. ZYBZH-Y-HEB-15).
Conflicts of interest
There are no conflicts of interest.
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[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6]
[Table 1], [Table 2], [Table 3]