Inorganic elements are important components of medicinal herbs, and provide valuable experimental evidence for the quality evaluation and control of traditional Chinese medicine (TCM). In this study, to investigate the relationship between the inorganic elemental fingerprint and geographical origin identification of cultivated Polygala tenuifolia, 41 elemental fingerprints of P. tenuifolia from four major polygala-producing regions (Shanxi, Hebei, Henan, and Shaanxi) were evaluated to determine the importance of inorganic elements to cultivated P. tenuifolia. A total of 15 elemental (B, Ca, Cl, Cu, Fe, K, Mg, Mn, Na, N, Mo, S, Sr, P, and Zn) concentrations of cultivated P. tenuifolia were measured using inductively coupled plasma mass spectroscopy (ICP-MS). The element composition samples were classified by radar plot, elemental fingerprint, and multivariate data analyses, such as hierarchical cluster analysis (HCA), principle component analysis (PCA), and discriminant analysis (DA). This study shows that radar plots and multivariate data analysis can satisfactorily distinguish the geographical origin of cultivated P. tenuifolia. Furthermore, PCA results revealed that N, Cu, K, Mo, Sr, Ca, and Zn are the characteristic elements of cultivated P. tenuifolia. Therefore, multi-element fingerprinting coupled with multivariate statistical techniques can be considered an effective tool to discriminate geographical origin of cultivated P. tenuifolia.
Polygala tenuifolia (family: Polygalaceae) is a perennial herb that has been cultivated throughout East Asia (P. tenuifolia in Korea, Wonji in Korean, Onji in Japanese, and Yuanzhi in Chinese)1. This herb is also distributed in the different regions in China, such as Shanxi, Shaanxi, Hebei, Henan, Gansu, Qinghai, Heilongjiang, Liaoning, Inner Mongolia, Jiangxi, Jiangsu, and Sichuan2. P. tenuifolia is a well-known TCM prescribed for amnesia, neurasthenia, palpitation, asthma, rhinitis, and insomnia3. Modern pharmacological studies have demonstrated that Polygala has a wide range of biological activities, such as improved learning and memory, anti-aging, antimicrobial, expectorant, and increased uterine contractions and muscle tension. P. tenuifolia primarily contains oligosaccharides4,5, xanthones6, and saponins7. Moreover, the saponins from P. tenuifolia have a complex chemical structure8. Xanthones have displayed antitumor9, antimicrobial10, anti-thrombotic11, and anti-inflammation activities. As a widely used medicinal plant, P. tenuifolia has attracted attention due to its superior pharmaceutical properties12.
With growing consumption and sales of P. tenuifolia in recent years, the wild resource has been worsened by overexploitation13, leading to the decline of wild resources for TCM. Alternatively, the cultivated P. tenuifolia is used as a medicine, but it has a low yield and is time consuming because it needs to be harvested by dredging after being planted for a few years. Poor quality cultivated P. tenuifolia is increasingly becoming available in the market, because the cultivated P. tenuifolia comes from non-authentic producing regions. Therefore, geographical origin, which affects the quality and efficacy, and authenticity of cultivated P. tenuifolia are highly important. Identifying the geographical origin of TCM is crucial because it determines quality14. The differences between authentic and non-authentic TCMs are invisible to the naked eye; therefore, systematic control of the authenticity TCM relies on chemical analysis15. The current elemental fingerprint study differentiated the crops and focused on the organic composition15; however, only a few fingerprints were reported on the inorganic elements or differentiation of TCMs. Many investigations revealed that the contents of inorganic elements in TCM play a significant role in the biological activity and greatly affects quality16. Therefore, constructing inorganic elemental fingerprints is valuable in identifying the geographical origin of TCM17. Elemental fingerprint techniques have been used in many plant studies based on element composition and multivariate statistical analysis. ICP-MS, as an element-specific detector, has several advantages, such as wide linear range, high sensitivity, multi-elements, and multi-isotopes detection ability18. These factors help determine various elements for qualitative, quantitative, and semi-quantitative analyses. The analytical approach used in this study is based on ICP-MS due to its aforementioned advantages.
This study did not focus on the identification of cultivated P. tenuifolia using the elemental composition profiles. Instead, it determined the 15 inorganic elements in cultivated P. tenuifolia using ICP-MS and investigated the elemental compositions. This study discriminated cultivated P. tenuifolia from different regions combined with multivariate analysis, and further established a reliable method for differentiating cultivated P. tenuifolia. The corresponding results demonstrated that the combination of inorganic elemental fingerprint with multivariate statistical analysis is a promising approach to discriminate the geographical origin of cultivated P. tenuifolia.
Element concentrations of cultivated P. tenuifolia samples
A total of 41 cultivated P. tenuifolia samples were collected from four major polygala-producing four provinces (Shaanxi, Hebei, Henan, and Shanxi) in China (Supplementary Table S1 and Fig. S1). To investigate the relationship between the inorganic elemental fingerprints and the geographical origin identification of cultivated P. tenuifolia, 15 inorganic elements were determined in cultivated P. tenuifolia samples by ICP-MS15. Moreover, the elemental contents of cultivated P. tenuifolia are listed in Supplementary Table S2.
The mean concentration of elements in cultivated P. tenuifolia is classified as follows: Mo < Cu < B < Zn < Mn < Sr < Na < Cl < Fe < S < Mg < P < K < N < Ca. Furthermore, the general order of the concentrations is Cu, Zn, Mn, Fe, Mg, and Ca, which corresponds to those of a previous study19. This sequence is associated with the pharmacodynamic material basis of cultivated P. tenuifolia.
Establishment and evaluation of the elemental fingerprint of P. tenuifolia grown in different locations
To visually demonstrate the distribution rules of the elemental contents, according to the results of the ICP-MS, the content distribution curves of 15 elements from 41 samples were drawn together in one chart (Fig. 1), as ephedra sinica sample20. For drawing convenience, some elements were expanded or narrowed to the same order of magnitude (B, Zn, Mn, and Sr expanded tenfold; Mo and Cu expanded 100-fold; K, P, Mg, S, and Fe reduced tenfold; Ca and N reduced 100-fold). The contents of individual elements, such as Fe, Cl, Zn, and Mo, from cultivated P. tenuifolia samples were maintained at a certain range, but different from that from Ephedra sinica sample. Other elemental content, such as Ca, K, Mg, Na, Sr, Mn, B, and Cu, displayed a similar peak shape in the chart. Obviously, the content of the individual elements, such as Ca, Fe, Cl, Sr, Zn, and Mo, from the cultivated P. tenuifolia samples differed greatly from that of the ephedra sinica sample, in which Ca content had expanded more than 3.6 fold from the average 250 mg/kg of cultivated P. tenuifolia samples to 900 mg/kg of ephedra sinica sample.
Studies showed that the differences of elemental contents are associated with diverse origins, the elements in cultivated P. tenuifolia samples maintained a certain balance compared with the peak of Ephedra sinica, which presented a completely different shape. Therefore, a commonality of elemental fingerprint exists, which determines the difference between P. tenuifolia and other medicinal plants. This discovery promotes the application of inorganic elemental fingerprints for determining the geographical origin of cultivated P. tenuifolia. This study suggested that the elemental fingerprint could be used to identify the authenticity of cultivated P. tenuifolia.
Distribution of the cultivated P. tenuifolia samples according to HCA
To improve the visualization of the relative distribution of the cultivated P. tenuifolia samples according to their geographical origins, HCA was performed using the first three discriminant normalization scores21. Cultivated P. tenuifolia samples from different regions were separated into four clusters based on the dendrograms cut at a distance of 3 (Fig. 2). The first cluster was composed of samples from the Hebei province, including Anguo, Neiqiu, and Xinglong, and the second cluster mainly was comprised of cultivated P. tenuifolia samples (Xingyang and Mengjin) from Henan, and two cultivated P. tenuifolia samples (Wanan and Pinglu) from Shanxi. The third cluster included Suide, Pucheng, and Zizhou from Shaanxi province, and Yicheng from Shanxi province. The fourth cluster was composed of 30 cultivated P. tenuifolia samples. A total of 29 cultivated P. tenuifolia samples were collected from the Shanxi province and only one cultivated P. tenuifolia sample from Shaanxi. The cluster analysis revealed that Hebei-cultivated P. tenuifolia samples are found in other regions. Some of the cultivated P. tenuifolia samples from different regions were clustered together22; notably, samples from Henan and Shanxi have similar elemental contents. For example, both of Shanxi- and Hebei-cultivated P. tenuifolia samples have significantly high K content. Overall, the cluster results are generally in agreement with the actual origin of cultivated P. tenuifolia samples. Results implied that elemental information is suitable for classifying the cultivated P. tenuifolia samples from different regions. However, due to the geographical origin of the cultivated P. tenuifolia samples, an overlap between Shaanxi and Shanxi occurs. Therefore, the radar plot analysis was used to further study the identification of the cultivated P. tenuifolia from different regions.
Distinguishing geographical origin of cultivated P. tenuifolia samples by radar plot
A radar plot based on the elemental composition has been used for distinguishing the geographical origin of cultivated P. tenuifolia samples. This method allows the use of a simple routine and rapid discrimination. For ease of comparison, a radar plot was performed to preliminarily classify cultivated P. tenuifolia origins based on the mean concentrations of the eight elements (K, P, Mg, Fe, Na, Sr, B, and Mo). These concentrations showed high relative standard deviation (RSD) in each cultivated P. tenuifolia sample. The distributions of the elemental patterns of cultivated P. tenuifolia samples from various provinces showed different characteristic patterns, as shown in Fig. 3. This study showed that some elements in cultivated P. tenuifolia vary in different regions23. Therefore, this method distinguishes the geographical origin of different cultivated P. tenuifolia samples. The cultivated P. tenuifolia samples are found in the two nearby provinces, namely, Shanxi and Hebei. This species has a significantly higher K content and is easily distinguished compared with the cultivated P. tenuifolia from the other two regions. This finding corresponded to the result of HCA (Shanxi and Hebei gather in clusters). Radar plots analysis was performed for 32 cultivated P. tenuifolia samples collected from different regions of the Shanxi province. Radar plots of 32 cultivated P. tenuifolia samples from different regions of Shanxi province illustrated that the distribution of elemental patterns shows the same characteristics. Moreover, the pattern is the same for all the cultivated P. tenuifolia samples from the same province (Fig. 3E). This finding indicated that the cultivated P. tenuifolia samples from the same province have a similar growth environment, including light, temperature, and humidity24,25,26. However, visual determination was the only result obtained by the radar plot analysis and the lack of definite index describing the exact differences decreased the credibility of the results. Therefore, the principal component analysis (PCA) was utilized in the following studies for actual discrimination.
Analysis of elements influencing the discrimination of P. tenuifolia by Principle component analysis
Principle component analysis (PCA) was accomplished by factor analysis in SPSS software27. PCA was performed on elemental concentrations according to 15 variables to classify all cultivated P. tenuifolia samples from different geographical origins. The results demonstrated that the first four principal components (PC1-4), with eigenvalues >1 had 74.024% of the total variability among the 15 variables in the original data, where in PC1, PC2, PC3, and PC4 contributed 38.78%, 18.33%, 9.45%, and 7.47% of the total variance, respectively (Table 1). This finding showed that a four-factor model could explain 74.024% of the test data. The first three principal component loading plot (Fig. 4) displayed that the N, Cu, K, Mo, Mn, and Sr contents have the highest weights in PC1; Ca content loaded highly in PC2; all element contents have a low PC3; Fe was the dominating feature content in PC4. The contribution of more than 74.024% varied from PC1 to PC4; therefore, the elements (N, Cu, K, Mo, Mn, Sr, Ca, and Fe) were regarded as the characteristics of inorganic elements in the cultivated P. tenuifolia. These elements may be identified as the most powerful indicators of cultivated P. tenuifolia. Furthermore, the trace elements Fe, Mn, and Cu are indicators of the geographical origin of plant samples due to their different concentrations in soils and effective uptake by plants28.
The PCA score plot (Supplementary Fig. S2A) illustrates a separation pattern of cultivated P. tenuifolia samples, whereas the corresponding loading plot (Supplementary Fig. S2B) describes the variables related to the separation. Elements, such as Sr, Mo, K, N, Cu, Mn, and Zn, controlled the discrimination of cultivated P. tenuifolia samples. Only 38.78% and 18.33% of the dataset were explained by the principle components 1 and 2, respectively.
To improve the discrimination, PCA was applied on the concentration values of 15 elements to classify the cultivated P. tenuifolia samples from Shanxi province. As shown in the results of the score and the loading plots in Supplementary Fig. S2C,D, the cultivated P. tenuifolia samples from the southern regions are easily distinguished. Elements, such as N, K, S, Sr, and Mo, influenced the separation of cultivated P. tenuifolia samples. The plots were defined by the principal components 1 and 2 and explained by the 45.01% and 15.12% variance. The results obtained by PCA did not allow for good discrimination of the geographical origin of cultivated P. tenuifolia. Therefore, DA could be applied for improved separation.
Discrimination of geographical origin of cultivated P. tenuifolia based on Discriminant analysis (DA)
DA of elemental concentrations measured by ICP-MS in the cultivated P. tenuifolia samples from different geographical origins were applied to classify groups; then, the discriminant functions correlated with the variables were obtained. The coefficients and cumulative contribution of the different variables in the discriminant functions for cultivated P. tenuifolia classification are shown in Table 2. In this study, the calculation was performed using seven variables (Ca, P, Mg, Fe, Na, Mn, and B) to classify all cultivated P. tenuifolia samples.
Distribution patterns of all cultivated P. tenuifolia samples were performed according to their origins in the plot defined by the discriminant functions (Fig. 5A). The variations between groups were explained by the discriminant functions 1 (78.8%) and 2 (15.5%). The cultivated P. tenuifolia from Shanxi, Henan, Shaanxi, and Hebei were completely separated.
DA was further performed on the elemental concentrations based on the six variables (Mg, Fe, Mn, Zn, B, and Mo) of the cultivated P. tenuifolia samples (Table 2) found in the different regions of Shanxi, as shown in Fig. 5B. The distribution pattern of Shanxi-cultivated P. tenuifolia defined by the discriminant functions 1 and 2 was plotted and was explained by the 68.2% and 38.1%, respectively. The plot shows a clear classification of cultivated P. tenuifolia origins from the three regions of Shanxi (southern, northern, and central).
Figure 5C represents the correlation chart of loadings for the selected elements, in the plane designed by the first two discriminant functions (F1 and F2). F1 expressed 78.8% of the variance, which provided the main separation between all cultivated P. tenuifolia regions and has a strong positive correlation with Ca, Na, and B; whereas F2 (15.5% of the variance) has a positive correlation with Mn and P concentrations. The correlation between Fig. 5A,C (Ca, B, P, and Na) was observed as the most useful variable for discriminating all cultivated P. tenuifolia samples from four provinces. This study showed that the cultivated P. tenuifolia samples from four different provinces were plotted in various spaces.
By applying DA to cultivated P. tenuifolia samples, the researchers were able to distinguish the southern, northern, and central regions of Shanxi province and notice that their projections fall in different dimensions of the chart. The main separators between these three regions were the elements Ca, Fe, and Mn (Fig. 5D). F1 expressed 68.2% of the variance, which provide the main separation between Shanxi regions and has a strong positive correlation with Fe and Mn, whereas F2 (31.8% of the variance) has a strong correlation with Ca, Fe, and Zn concentrations.
In this study, the multivariate statistic approach was used to verify the correlation between the compositions of cultivated P. tenuifolia inorganic elements and the authentic regions. The P, Mn, Ca, Na, Mg, Fe, and B were used to classify all cultivated P. tenuifolia samples according to their geographical origins. Moreover, Mn, Ca, Mg, Fe, Zn, and B were applied to recognize the different regions of the Shanxi province. Elements were classified using the multivariate statistic approach, thereby confirming the relationship between cultivated P. tenuifolia authentic regions and inorganic elements.
To check the reliability of the developed classification model, cross-validation method was operated to compute the classification and probability of cultivated P. tenuifolia samples29. Table 3 summarizes the observation of the cross-validation results together with the classification of cultivated P. tenuifolia samples using the DA model. The results showed that 80.5% of the cultivated P. tenuifolia samples (Shanxi, Henan, Shaanxi, and Hebei) and 100% of the 32 Shanxi cultivated P. tenuifolia samples (southern, northern and central regions) were correctly classified. These results were similar to findings of the previous studies; therefore, the multi-element analysis is a reliable fingerprinting analytical strategy for authenticating cultivated P. tenuifolia.
Variation in element contents and the bioactivity of P. tenuifolia
The 15 elemental fingerprints of P. tenuifolia are consisted of macro elements (N, K, and P), secondary elements (Ca, Mg, and S), and trace elements (Fe, Mn, B, Zn, Cu, Mo, Cl, and Sr). A recent study reported that Chinese herbal medicines contain trace elements that are essential to the human body. Moreover, trace elements in other TCM and their active ingredients have different degrees of relationship30,31. Based on the results of the multi-element analysis, the absorption of trace elements in TCM is affected by their chemical compositions32,33. Moreover, these elements exhibit complex interactions34. This study showed that the contents of Ca and N are high in 41 cultivated P. tenuifolia samples, whereas the RSD of the two elements is low. This result indicated that the contents of these two elements have a narrow range of variation and thus are relatively stable. Ca is required for plant cell division and enlargement, and its insufficiency can generate leaves deformity, terminal buds death, and reduce root growth. Ca is also important for human healthy bones and teeth, muscle contraction, nerve impulse stimulation, and ion transport. N is the component of amino acids, nucleic acids, and vitamins in the plants or animals. The deficiency of N can generate human malnutrition, inactivity and failure to grow35. Quantitative Ca and N may be significant for the yield and biological activity of cultivated P. tenuifolia.
Elements and geographical origins of TCM
Genuineness of TCM is related to the climate and environment and the geological environment, especially the soil geochemical characteristics36. Environmental biogeochemical and trace elements provide scientific guidance for the correct introduction and transplantation of TCM36. The elemental fingerprints of P. tenuifolia from different provinces (Fig. 1) displayed a similar peak shape compared with E. sinica, thereby indicating the element in cultivated P. tenuifolia maintained a commonality, which could identify the difference between P. tenuifolia and other medicinal plants. At the same time, The element fingerprint peak height had a wide range, indicating the cultivated P. tenuifolia in different production regions have a great difference. In this study, the combination of multi-element fingerprinting and multivariate statistical techniques can be used as an effective tool for distinguishing the geographical origins of cultivated P. tenuifolia. So the elemental fingerprint could be used to identify the authenticity of cultivated P. tenuifolia.
Materials and Methods
In this study, the roots of cultivated P. tenuifolia were collected in August and October2013 (Supplementary Table S1). A total of 41 cultivated P. tenuifolia samples were gathered from the four provinces (Shaanxi, Hebei, Henan, and Shanxi) in China, as shown in Supplementary Fig. S1. Professor Yunsheng Zhao and assistant research fellow Hongling Tian identified the plants as the roots of authentic P. tenuifolia.
The ICP-MS element standard stock solutions for mineral elements were provided by Beijing General Research Institute for Nonferrous Metals in China. All glassware and plastic ware were cleaned with nitric acid and rinsed with deionized water prior to the procedure.
Methods: ICP-MS Measurements
The concentrations of essential elements in plant samples were determined by inductively coupled plasma mass spectrometry (NexION 300D, PerkinElmer Instrument Co., U.S.) following the modified JIS K0133-2007 Method (Japanese Industrial Standards Committee 2007). The instrument parameters were optimized as follows: plasma gas flow rate of 18.0 L/min, carrier gas flow rate of 1 L/min, auxiliary flow rate of 1.20 L/min, dwell time of 50.0 ms, radio frequency power of 1600 W, sample uptake of 1.0 ml/min, scan time of 20 s, and integral time of 1 s.
In this study, the analytical data were manipulated using the Excel 2010 spreadsheet. The SPSS 21.0 software was used for pattern recognition computations. Pattern recognition methods were applied involving the multivariate data analysis. The discrimination and authenticity of the cultivated P. tenuifolia samples were carried out by the following multivariate data analysis (chemometric) techniques: hierarchical cluster analysis (HCA), principle component analysis (PCA), and discriminant analysis (DA). The statistical analysis used in this study is as follows:
The establishment of element fingerprint and radar plot analysis
The elemental fingerprints and radar plots were analyzed. The statistical analysis was performed using Microsoft Office Excel 2010.
Hierarchical cluster analysis (HCA)
HCA is an unsupervised classification procedure that involves measuring the similarity between samples to be clustered. Hierarchical cluster analysis (HCA) of samples was performed using the selected chemical descriptors as variables, the Ward’s method as the amalgamation rule, and the squared Euclidean distance as the similarity measurement. Samples were grouped in clusters based on their nearness and similarities37. The groups were represented by the branches of the dendrogram. The dendrogram showed the different groups at a normalized or rescaled distance. The between-group linkage method was the clustering method used in this study.
Principal components analysis (PCA)
PCA is a projection method that allows for easy visualization of all information contained in the data set. PCA reduces the dimensionality of the data matrix and transforms original variables into principal components (PCs)38. By plotting the PCs, one can view the interrelationships between different samples and examine the grouping of samples. Finally, PCAs quantify the amount of useful information, as opposed to meaningless variations, contained in the data39.
Discriminant analysis (DA)
DA is a widely known supervised pattern recognition method for classifying the maximum variance between groups and minimum variance within the groups by creating new variables, which are linear combinations of the original variables40. This classification procedure maximizes the variance between categories and minimizes the variance within categories.
All data generated or analysed during this study are included in this published article (and its Supplementary Information files).
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This work was supported by the National Natural Science Foundation of China (31270381). We are grateful to Jianzhao Qi for his assistance and helpful comments.
The authors declare that they have no competing interests.
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Zhao, Y., Ma, X., Fan, L. et al. Discrimination of geographical origin of cultivated Polygala tenuifolia based on multi-element fingerprinting by inductively coupled plasma mass spectrometry. Sci Rep 7, 12577 (2017). https://doi.org/10.1038/s41598-017-12933-z
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