| Summary: | 博士 === 國立臺灣師範大學 === 化學系 === 95 === ABSTRACT
High-performance liquid chromatography (HPLC) and inductively coupled plasma - mass spectrometry (ICP-MS) are analysis tools frequently used to determine the marker substances and trace elements of Chinese herbal medicine. This study has developed methods to carry out the ingredient analysis and sample recognition of medicinal materials.
This research consists of three parts: the first part, through the HPLC quantitative results (30 peaks) of organic ingredients and the ICP-MS assay data of inorganic elements (44 elements) of Chia-wei-hsiao-yao-san (Bupleurum and Peony Formula), adopted manual induction and statistical analysis method to set up rules for distinguishing the brands. The differences of component 2 ( Gallic acid, GA ) and component 26 ( Paeonol, PN ) of the organic ingredients of each samples were observed and it was found that all of the samples could be obviously distinguished into two groups by utilizing the ratio of component 2/IS. The values of Sun Ten Pharmaceutical, Sheng Chang Pharmaceutical and KODA Pharmaceutical were greater than 1.45 while Kaiser Pharmaceutical and Chuang Song Zong Pharmaceutical were lower than 1.45. If they were subdivided by the ratio of 2/26 (Sun Ten > 2.1, 1.8 < Sheng Chang <2.1, KODA <1.8, Kaiser <2.3, Chuang Song Zong > 2.5), the products of each brand would then be recognized smoothly. According to the above rules, only 4 batches out of 33 samples from five brands were classified incorrectly: 2 batches of Sheng Chang and 1 batch of Kaiser with the 2/IS ratio at 2.21, 2.37 and 1.67 respectively and the 2/26 ratio greater than 2.1 was determined as Sun Ten. One batch of Sun Ten with the 2/IS ratio at 1.65 and the 2/26 ratio between 1.8 and 2.1 was determined as Sheng Chang. Overall, the accuracy of recognition by hand was about 88%. The Multivariate Analysis was performed on the analytical data of 30 peaks: it was found in the principal component analysis (PCA) that PC1 (1, 11 and 14), PC2 (2, 6, 24 and 25) and PC3 (23, 27 and 28) represented 61.343% of total variability. The three-dimensional PCA chart formed by PC1, PC2 and PC3 showed that the samples of each brand clustered at specific position respectively: Sun Ten was in the center top position, Sheng Chang in the lower left corner, KODA in the center, Kaiser in the lower right corner and Chuang Song Zong in the center bottom with good classification. They also could be clearly categorized by carrying out the cluster analysis (CA). After the cluster distance was set at 0.38 (the maximum scale is 1), five clusters of Sun Ten, Chuang Song Zong, Kaiser, Sheng Chang and KODA appeared successively. While the linear discriminant analysis (LDA) was performed, the original classification reached 100%. However, the accuracy only resulted in 13.8% when it was cross-validated. Therefore, stepwise-LDA was firstly adopted to select 8 peaks of 1(nicotinic acid), 8(Geniposide), 13, 17, 19, 20, 23 and 24. Then the LDA was used to compile the statistics and the completely correct results of classification could be obtained. This method could also be used to determine accurately the brand of any sample in the market. In addition, the variables 17 and 19 possessed the highest weights in discriminant function 1 (-26.620 and –18.404). The discriminant function 2 was constituted mainly by 20 (-11.716) and 24 (-12.520). Discriminant function 3 was constituted mainly by 17 (21.550), 23 (20.372), and 24 (–17.723) and discriminant function 4 was constituted mainly by 17 (-8.869), 24 (-6.954) and 20 (6.051).
While the ICP-MS was used to test each sample, the analysis data of 44 inorganic elements could be obtained. The differences of these data were compared carefully and only the products of three brands could be distinguished: Sun Ten, Sheng Chang and Kaiser. The contents of all elements of KODA were extremely close to the ones of Chuang Song Zong and the large overlapping within the tolerance was very difficult to be distinguished by hand. The multivariate analysis was conducted on the analysis data of these 44 inorganic elements: the PCA showed that PC1 (La, Ce, Nd and Pr), PC2 (Al, Mg and Br) and PC3 (I and As) represented 71.944% of total variability. The three-dimensional PCA chart formed by PC1, PC2 and PC3 showed that Sun Ten was in the lower left corner, Sheng Chang in the upper left corner, KODA in the lower right corner, Kaiser in the upper right corner and Chuang Song Zong in the center bottom with separate clusters in obvious partition. After the distance of cluster analysis was set at 0.41 (the maximum scale is 1), they were clearly divided into five clusters: Sun Ten, Chuang Song Zong, Kaiser, KODA and Sheng Chang successively. While the linear discriminant analysis was performed, the original classification could reach 100%. However, the accuracy only resulted in 17.2% when it was cross-validated. Therefore, the stepwise-LDA was firstly adopted to select 9 elements from these 44 elements: Na, Mg, Al, Ni, Cu, I, Ce, Bi and U. Then the LDA was used to compile the statistics and the completely correct results of classification could be obtained. All of the Chia-wei-hsiao-yao-san samples could be properly classified and this statistical analysis method could also be used to determine correctly the brand of any sample in the market. In addition, the variables of Mg, Na and Cu (-2.443, 2.203 and 2.140) possessed the highest weights in discriminant function 1. The discriminant function 2 was constituted mainly by Ce (-6.208) and U (4.730). Discriminant function 3 was constituted mainly by Cu (1.843), Mg (-1.126) and I (1.063), and discriminant function 4 was constituted mainly by U (1.185), Mg (1.115) and Ce (-1.021).
Furthermore, three samples were randomly selected to compare the product consistency of these five brands and it was found that the samples from different batches produced by Sun Ten possessed the highest conformity which resulted in 0.23 with the most stable quality. The ones of Chuang Song Zong and Sheng Chang were in the second place which resulted in 0.22 and 0.21 respectively. The commodities produced by Kaiser and KODA had poorer conformity which represented 0.16 and 0.09 respectively with the quality rather unstable.
The second part comprised the analysis and discrimination of Aurantii Fructus Immaturus and Aurantii Fructus Maturus. The samples including 20 batches of Immaturus (Poncitrus trifoliata) and 30 batches of Maturus (Citrus aurantium and C. wilsonii) were collected from the herbal medicine markets in Taiwan and China. After
identifying by the external appearance and pharmacognostic histological anatomy, the HPLC was performed to analyze the 12 principle components in these samples. The Cosmosil 5C18-AR was used as the analysis column and the mixture of aq. KH2PO4 and MeOH/CH3CN was used as elution in the HPLC analytical method. The separation could be achieved smoothly within 60 minutes. The RSD of peak area were 0.30-1.21 (intraday) and 0.45-1.93 (interday). The detecting limits were between 2.84 and 6.85 ng. The analysis results showed that immature samples and mature samples could be distinguished by the constituents of NR, HE, NE and QU. The immature samples did not or hardly contain HE or NE while the mature ones did not contain or contained very few constituents of NR or QU. The difference between both mature samples (C. aurantium and C. wilsonii) could be distinguished by the constituents of NGC and HE as well as the ratio of HE/NG, HE/NE and NGC/NE. The ratios in C. aurantium were above 0.49, 2.30 and 0.21 while the ones in C. wilsonii were 0.40, 1.31 and below 0.21 respectively. However, 5 batches which were not easy to be distinguished and were recognized as two species according to the close data. The analytical data of 12 peaks were entered into the multivariate analysis software and it was found that whether the PCA, CA or LDA could definitely distinguish the P. trifoliate, C. aurantium and C. wilsonii. The completely correct results could be achieved in the cross validation or adjunction experiment. It was notable that the 5 batches of samples not easy to be distinguished by hand were clearly grouped into the cluster of C. wilsonii.
In the third part, we used the HPLC method available in our laboratory to measure the contents of major constituents and trace elements of Moutan Cortex, Magnoliae Cortex and Fangchi Radix. We had compared both pre-treatments of boiling and digestion and found that the latter was around 102 to 104 times of the former. However, all of the values were in parallel correlation. Therefore, we decided to use the boiling method which was equivalent to the decoction of Chinese herbal medicine for sample preparation.
1. Moutan Cortex
The samples of 23 batches of Moutan Cortex sold in the market were collected: 10 batches of Paeonia delavayi and 13 batches of P. suffruticosa. The HPLC was conducted to measure the contents of 10 constituents: 4,6-di-GG(1, G=glucose), 1,2,3,6-tetra-GG(2), 1,2,3,4,6-penta-GG(3), 1,3,4,6-tetra-GG(4), 3,4,6-tri-GG (5), 1,3,6-tri-GG(6), 3,6-di-GG(7), 1,2,6-tri-GG(8), paeoniflorin(Pf) and paeonol(PN). It was found that the ratios of PN/Pf and 3/2 could be adopted to distinguish P. delavayi (PN/Pf > 1, 3/2>15.1) and P. suffruticosa (PN/Pf < 1, 3/2<11.4). Overall, the accuracy of recognition by hand was 100%. The analytical data of 10 peaks were entered into the software of PCA, CA and LDA, and it was found that the species of each batch of drug materials could be correctly distinguished in both principle component analysis and linear discriminant analysis. The cross validation of the latter also proved 100% accuracy, but they could not be discriminated in cluster analysis. Therefore, the stepwise-LDA was performed to select three constituents: peak1(4,6-di-GG), peak4(1,3,4,6-tetra-GG) and Pf (paeoniflorin) and then the cluster analysis was carried out. However, the correct classification could not be obtained either. Nevertheless, performing the linear discriminant analysis by using the analysis data of only three peaks could achieve 100% correct results. This showed that the statistical analysis of LDA could clearly distinguish the origins regardless of 10 or 3 constituents, but the latter is simple and speedy.
The analytical data of 62 elements of 18 batches of Moutan Cortex samples (9 batches of P. delavayi and 9 batches of P. suffruticosa) were observed carefully and it was found that five elements: Ge, Mo, Cd, Ce and Tl possessed the most noticeable heterology in these two species. When the analytical data of these five elements were used as the tool of recognition by hand, the accuracy could reach 100%. If the analytical data of 62 elements were processed by the software of PCA, CA and LDA, only PCA could achieve the goal of completely correct classification and they could not be distinguished in CA. Although the original classification of LDA possessed 100% accuracy, the discrimination ratio only reached 50% in cross validation. We used stepwise-LDA to select 8 elements with the optimal discrimination effects: Be, V, Ag, Cd, Tl, Pb, Bi and Th. Let the 8 elements be variables and both CA and LDA analysis modes could completely confirm the origins of all samples.
2. Magnoliae Cortex
22 batches of Magnoliae Cortex samples were collected: 12 batches of Magnolia officinalis, 4 batches of M. officinalia ssp. biloba（Rehd. et Wils.）and 6 batches of M. obovata THUNB. The HPLC was performed to measure the contents of seven alkaloids: (-)-magnocurarine(1), (+)-magnoflorine(2), (+)-laurifoline(3), (+)-oblongine(4), (+)-menisperine(5), (+)-xanthoplanine(6), (+)-N-methylglaucine(7) and two phenols: honokiol(8) and magolol(9). It was found that the ratios of 2/1 and 9/8could sort out the samples that belonged to M. officinalis (2/1>5.102 and 9/8<1.397) while the ratios of 2/3 and 4/3 could distinguish M. officinalia ssp. Biloba (2/3<6.458 and 4/3<1.130) and M. obovata (2/3>18.901 and 4/3>3.156). The accuracy of recognition by hand adopting this procedure could reach 100%. The quantitative results of 9 peaks were entered into the software of PCA, CA and LDA. It was found that each statistical method could rapidly induce the correlation between the origin and constituent with completely correct discrimination. The stepwise-LDA was performed to select two constituents 7 and 8 as new variables. The result showed that the origin to which any sample belonged could be determined accurately by only adopting the analysis data of two absorption peaks.
The analytical data of 46 inorganic elements of 9 batches of Magnoliae Cortex samples (7 batches of M. officinalis and 2 batches of M. officinalia ssp. biloba) were collected. The differences were compared carefully and it was found that seven elements: Fe, Pb, V, Zr, Sb, Bi and Th could be the basis of recognition by hand. We used PCA, CA and LDA to process the analytical data of these 46 elements, and the results showed that they could be correctly classified by PCA and LDA, but clusters could not be evidently divided by CA. Thus the stepwise-LDA was performed to select 5 from these 46 elements: Sc, Cu, Sr, Zr and Th. These 5 data were taken as variables and no matter CA or LDA could distinguish them correctly. The former could divide M. officinalis and M. officinalia ssp. biloba into two clusters at the distance of 0.31 (the maximum scale is 1) while the latter could obtain 100% cross validation by using both five elements and forty-six elements.
3. Fangchi Radix
37 batches of Fangchi Radix samples were collected: 5 batches of Sinomenium acutum、11 batches of S. tetrandra、15 batches of Aristolochia fangchi and 6 batches of Cocculus trilobus. The HPLC was carried out to analyze each drug material and was found that S. acutum contained acutumidine (1), magnoflorine (2), stepharine (3), sinomenine (4), acutumine (5), and tetrandrine (9); S. tetrandra has sinomenine (4), cyclanoline (6), fangchinoline (7), berbamine (8), tetrandrine (9), and isotetrandrine (10); A. fangchi contains magnoflorine (2), tetrandrine (9), aristolochic acid II (12), aristolochic acid I (13), aristololactam (14), and an unknown component X; and, C. trilobus has magnoflorine (2), sinomenine (4), tetrandrine (9), trilobine (11), isotrilobine (15), and unknown components A and B. Each drug material of different species could be distinguished by above fingerprint. The analytical data of 18 peaks were adopted to perform the multivariate analysis and it was found that all of the PCA, CA and LDA could rapidly classify each sample according to the origin where it belonged with precise accuracy and similarity. The origin of unknown sample could also be determined. The stepwise-LDA was used to select 4 constituents 2, 4, 7, 11 and 15 from 18 peaks. Then the LDA was performed to compile the statistics and the completely correct results of classification could also be obtained.
The ICP-MS was carried out to measure the contents of 55 trace elements of 16 batches of Fangchi Radix samples (2 batches of S. acutum, 4 batches of S. tetrandra and 10 batches of A. fangch). It was found that the differences between Be, Mn, Br, Yb, Tl, U and Bi could be the basis of recognition by hand. When the analytical data of 55 elements were entered into the multivariate analysis software, it was found that only PCA could achieve the goal of origin discrimination. Although the original classification of LDA possessed 100% accuracy, the discrimination result only reached 12.5% in cross validation. Thus we used stepwise-LDA to select 7 elements to be the variables: Be, Mn, Br, Yb, Tl, U and Bi. Then the CA and LDA were performed and the results showed that both methods could obtain completely correct classification.
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