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Objective:To comprehensively evaluated the quality of Sargentodoxae Caulis from different habitats with a combination of indexes and characteristic chromatogram method from Chinese Pharmcopoeia (Edition 2020). Methods:The contents of water content, total ash, ethanolic extract, sulfur dioxide residue, heavy metals and harmful elements, total phenols, chlorogenic acid, salidroside and characteristic chromatogram of 17 batches of Sargentodoxae Caulis were determined. The quality of Sargentodoxae Caulis was comprehensively evaluated by combining chemical pattern recognition method. Results:The water content, total ash content, extracts, and content determination of 17 batches of Sargentodoxae Caulis from different habitats complyed with the provisions of the Chinese Pharmcopoeia (Edition 2020). There were differences in the contents of extracts, chlorogenic acid, and salidroside, among which the content of Anhui origin was higher. A total of 8 common peaks were identified from the 17 batches samples. Conclusion:Comprehensive evaluation of multiple indicators can demonstrate the quality of Sargentodoxae Caulis more correctly, and shows that the quality of Sargentodoxae Caulis from different habitats is different. The quality of Sargentodoxae Caulis from Anhui is better than that from other habitats.
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OBJECTIVE@#To rapidly identify the two morphologies and chemical properties of similar herbal medicines, Blumea riparia and B. megacephala as the basis for chemical constituent analysis.@*METHODS@#UPLC-Q-Exactive-MS/MS was utilized for profiling and identification of the constituents in B. riparia and B. megacephala. Chemical pattern recognition (CPR) was further used to compare and distinguish the two herbs and to identify their potential characteristic markers. Then, an HPLC method was established for quality evaluation.@*RESULTS@#A total of 93 constituents are identified, including 54 phenolic acids, 35 flavonoids, two saccharides, one phenolic acid glycoside, and one other constituent, of which 67 were identified in B. riparia and B. megacephala for the first time. CPR indicates that B. riparia and B. megacephala samples can be distinguished from each other based on the LC-MS data. The isochlorogenic acid A to cryptochlorogenic acid peak area ratio calculated from the HPLC chromatograms was proposed as a differentiation index for distinguishing and quality control of B. riparia and B. megacephala.@*CONCLUSION@#This study demonstrates significant differences between B. riparia and B. megacephala in terms of chemical composition. The results provide a rapid and simple strategy for the comparison and evaluation of the quality of B. riparia and B. megacephala.
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Objective:To establish the HPLC fingerprint of Centellae herba and determine the content of asiaticoside, madecassic acid and asiaticoside B simultaneously; To compare the quality differences of Centellae herba collected in different months. Methods:The chromatographic condition was a Shimadzu InertSustain C18 column (4.6 mm×250 mm, 5 μm) with a mobile phase consisting of acetonitrile and 2 mmol/L beta cyclodextrin in gradient elution at the flow rate of 0.8 ml/min. The detection wavelength was 204 nm, and the column temperature was 30 ℃. The different Centellae herba materials of collected in 2-12 months from Chenzhou were studied by the similarity evaluation combined with cluster analysis, principal component analysis and the three contents determination. Results:The HPLC fingerprint of Centellae herba was established and 9 common peaks were designated. The eleven samples were different, which can be aggregated into 4 categories and the quality of Centellae herba collected in July was the best. Conclusion:The established fingerprint and multi-components quantitative method are stable and reliable, which can provide a reference for the quality control and the utilization of Centellae herba resource.
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OBJECTIVE To establish quantitative analysis of multi -components by single marker （QAMS） method to simultaneously detect the contents of cinnamic acid ，cinnamaldehyde，plantamajoside，verbascoside，isoacteoside，calceolarioside B ， psoralen，isopsoralen，neobavaisoflavone and bavachin in Gushen dingchuan pill ，and to perform quality evaluation of Gushen dingchuan pill by combining with chemical pattern recognition . METHODS High-performance liquid chromatography was adopted . Using psoralen as internal standard ，the relative correction factors of the other 9 components were established ，and the contents of each component were calculated and compared with those determined by external standard method . Cluster analysis ，principal component analysis and partial least squares discrimination analysis were performed by the results of QAMS method ，and the qualities of 15 batches of Gushen dingchuan pills were evaluated . RESULTS The above 10 components showed a good linear relationship in their respective ranges （r＞0.999 0）. RSDs of precision ，repeatability，stability and recovery tests were all lower than 2.00%. There was no significant difference between QAMS method and external standard method （P＞0.05）. The results of cluster analysis and principal component analysi showed that 15 batches of Gushen dingchuan pills could be clustered into 3 categories. The results of partial least squares discrimination analysis showed that psoralen ，verbascoside，cinnamaldehyde and isopsoralen were the main potential markers affecting the quality of Gushen dingchuan pills . CONCLUSIONS Established QAMS method for quantitative control of multi index components and chemical pattern recognition can be used for the quality evaluation of Gushen dingchuan pills .
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OBJECTIVE To establish the method for simultaneous determination of 11 components as narirutin in Biantong capsules，to conduct chemical pattern recognition analysis and to screen differential markers affecting their quality . METHODS HPLC method was adopted . The separation was carried out on Venusil XBP C 18 column with mobile phase consisted of acetonitrile - 0.1％ phosphoric acid solution with gradient elution at flow rate of 1.0 mL/min. The sample size was 10 µL，and column temperature was set at 30 ℃. The detection wavelengths were set at 283，330，520，220 nm，respectively. Using verbascoside as an internal standard ，the contents were determined by quantitative analysis of mult -components by single marker （QAMS），and the results were compared with those of external standard method . Cluster analysis ，principle component analysis and orthogonal partial least squares -discriminant analysis were performed with SPSS 26.0 and SIMCA 14.1 software. The differential markers affecting the quality of Biantong capsules were screened using the variable importance in projection （VIP）value greater than 1 as the standard . RESULTS The contents of narirutin ，naringin，neohesperidin，echinacoside，tubuloside A ，isoacteoside，cyanidin-3-O-glucoside， cyanidin-3-O-rutoside，atractylolide Ⅲand atractylolide Ⅰ were 0.739-1.265，1.134-2.158，1.407-2.359，1.368-2.502，0.304-0.522， 0.257-0.521，0.423-0.727，0.375-0.733，0.130-0.283 and 0.062-0.166 mg/g，respectively. The relative average deviation of them from the external standard method was less than 2％. The results of cluster analysis showed that 15 batches of samples could be grouped into three categories ，S1-S7 as a category ，S8-S10 as a category ，and S 11-S15 as a category ，which was consistent with the classification results of principal component analysis . The results of orthogonal partial least squares -discriminant analysis showed that the VIP values of cyanidin -3-O-rutoside，atractylolide Ⅲ， naringin，neohesperidin，echinacoside and verbascoside were all greater than 1. CONCLUSIONS The method for simultaneous determination of 11 components in Biantongcapsules， including narirutin ， is successfully established . Combined with chemical pattern recognition analysis ，it can be used for the quality control of Biantong capsules . Six components such as cyanidin -3-O-rutoside may be the differential markers that affect the quality of Biantong capsules .
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OBJECTIVE To establish the HPLC fingerprint of Mongolian medicine Sanzisan ，and to evaluate its internal quality by chemical pattern recognition technique comprehensively. METHODS HPLC method was used. Using geniposide as reference，HPLC fingerprints of 15 batches of Sanzisan were drawn with Similarity Evaluation System of TCM Chromatogram Fingerprint（2012 edition）. Similarity evaluation and common peaks identification were conducted. Combined with cluster analysis （CA），principal component analysis （PCA），and orthogonal partial least squares-discriminant analysis （OPLS-DA），the quality of 15 batches of Sanzisan was evaluated ，and the differential markers that affected its quality were screened. RESULTS There were 29 common peaks in 15 batches of Sanzisan ，and the similarity was no less than 0.952，indicating that the chemical composition of the 15 batches of Sanzisan had good consistency. A total of 13 common peaks were identified ，which were chebulic acid ，gallic acid，punicalin，punicalagin A ，punicalagin B ，jasminoside B ，caffeic acid ，corilagin，geniposide，chebulagic acid ，1，2，3，4，6- O-galloylglucose，chebulinic acid ，ellagic acid. Both CA and PCA could divide 15 batches of Sanzisan into four categories ，and the classification results were consistent ，indicating that the quality of 15 batches of Sanzisan had certain differences. Fourteen differential markers （chebulic acid ，gallic acid ，ellagic acid ，etc）that lead to the quality difference between batches were screened out by OPLS-DA. CONCLUSIONS Established HPLC fingerprint analysis method is simple and stable. Combined with chemical pattern recognition analysis ，it can be used for the quality control of Sanzisan.
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OBJECTIVE To establish the fingerprint of Tibetan medicine Adhatoda vasica ，and determine the contents of vasicine and vasicinone ，so as to comprehensively evaluate its quality combined with chemical pattern recognition. METHODS Using vasicine as control ，HPLC fingerprints of 11 batches of A. vasica were established with Similarity Evaluation System for Chromatographic Fingerprints of TCM （2012 edition）. The common peaks were identified and their similarities were evaluated. Cluster analysis （CA），principal component analysis （PCA）and orthogonal partial least squares-discriminant analysis （OPLS-DA） were performed by using SPSS 25 software and SIMCA 14.1 software. The variable importance in the projection （VIP）value＞1.0 was used as the standard to screen the differential components affecting the quality of A. vasica ；the contents of vasicine and vasicinone were determined by HPLC simultaneously. RESULTS A total of 23 common peaks were found ，and peak 2 was identified as vasicine ，and peak 4 was identified as vasicinone. Their similarities ranged 0.920-0.994. The results of CA showed that 11 batches of samples were clustered into 3 categories（distance was 14）：S1-S8 as one category （origin：Yunnan，Tibet），S9 as one category （origin：Yunnan），S10-S11 as one category （origin：Sichuan）；the results of P CA and OPLS-DA showed that S 9 and S10-S11 were divided into one category respectively ，and S1-S8 were further divided into 2 categories：S1，S4 as one category，S2-S3，S5-S8 as one category ；the common peaks with VIP value ＞1.0 included peak 2，peak 16，peak 21，peak 17，peak 1 and peak 13. Among 11 batches of samples ， contents of vasicine and vasicinone were 4.12-10.22 and 0.60-3.26 mg/g， respectively. CONCLUSIONS Established edu.cn HPLC fi ngerprint and content determination method are simple and accurate ，and can be used for the quality evaluation of Tibetan medicine A. vasica ，by combining with chemical pattern recognition. Vasicine and other components may be the differential components that affect the quality of the drug.
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OBJECTIV E To establish the method for evaluating the quality o f Plantago asi atica and fried P. asiatica . METHODS The fingerprints of 15 batches of P. asiatica and 15 batches of fried P. asiatica were established by HPLC. The common peaks were identified with the Similarity Evaluation System for Chromatographic Fingerprinting of TCM （2012 edition）， and similarity evaluation was performed. Analysis of chemical pattern recognition was performed by using SPSS 25.0 and SIMCA-P 14.1 software（cluster analysis ，principal component analysis and orthogonal partial least squares regression analysis ）. The markers which affected the difference in the quality between P. asiatica and fried P. asiatica were screened with variable importance projection（VIP）value greater than 1. RESULTS There were 18 common peaks in the fingerprints of 15 batches of P. asiatica and 13 common peaks in the fingerprints of 15 batches of fried P. asiatica . A total of 8 common peaks were found in both of them. Their similarities were greater than 0.920. Two common peaks were identified as geniposidic acid ，acteoside. The results of cluster analysis showed that when the spacing was 10，the 30 batches of samples could be clustered into three categories ，with S 1-S5 as one，S16-S20 as one ，S6-S15 and S 21-S30 as one . The results of the pri ncipal component analysis showed that the cumulative variance contribution rate of the first two principal components was 82.575％ . The results of the orthogonal partial least squares regression analysis showed that the VIP values of the three common peaks were greater than 1，namely peak E（acteoside）， peak D （geniposidic acid ） and peak G. CONCLUSIONS Established fingerprints are stable ，simple sina.com and rapid. It can be used for the quality evaluation of P. asiatica and fried P. asiatica ，by combining with analysis of chemical pattern recognition. Acteoside ，geniposidic acid and the component represented by peak G may be the markers affecting the difference in quality of P. asiatica and fried P. asiatica .
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OBJECTIVE To establish the fingerprints of Kangfuyan capsules and carry out chemical pattern recognition analysis，and simultaneously determine the contents of five components so as to promote the quality standard of the drug. METHODS High performance liquid chromatography （HPLC）fingerprints of 11 batches of Kangfuyan capsules （S1-S11）were established by Similarity Evaluation System of TCM Chromatographic Fingerprint （2012 edition）；identification and attribution analysis of chromatographic peaks were carried out by comparison with the chromatograms of the reference substance and the decoction pieces of single ingredient. SPSS 26.0 and SIMCA 14.1 software were used for cluster analysis and principal component analysis. HPLC method was used to determine the contents of matrine ，phellodendrine chloride ，rutin，forsythoside A and berberine hydrochloride. RESULTS There were 29 common peaks in the fingerprints for 11 batches of samples ，and the similarity was higher than 0.99. A total of 5 chromatographic peaks were identified ，which are matrine （peak 3），phellodendron chloride （peak 14），rutin （peak 20），forsythiaside A （peak 22） and berberine hydrochloride （peak 28）. The results of cluster analysis and principal component analysis showed that S 1-S9 were clustered into one category ，and S 10 and S 11 were clustered into another category. The contents of above 5 components were 29.320 5-60.144 3，0.621 6-1.076 6，1.025 9-2.830 5，2.899 3-6.212 7 and 4.425 1-8.581 6 mg/g， respectively. CONCLUSIONS The established fingerprint and content determination method are stable and reliable ，and can provide reference for the quality control of the preparation in combination with chemical pattern recognition analysis.
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ObjectiveIn order to find a fast odor-based method for the identification of sulfur fumigated Gastrodiae Rhizoma， an ultra-fast gas phase electronic nose technology was used to identify the odors of different degrees of sulfur fumigated Gastrodiae Rhizoma decoction pieces. MethodHeracles NEO ultra-fast gas phase electronic nose was employed to collect gas chromatograms of unsulfured and sulfured with different degrees of Gastrodiae Rhizoma decoction pieces， gas chromatograms were performed under programmed temperature （initial temperature of 40 ℃， 0.2 ℃·s-1 to 60 ℃， and then 4 ℃·s-1 to 250 ℃）， the sample volume was 5 mL， the incubation temperature was 65 ℃ and incubation time was 35 min. Kovats retention index and the AroChemBase database were used for qualitative analysis， and stoichiometric analysis was performed on this basis. Principal component analysis （PCA）， discriminant factor analysis （DFA） and partial least squares-discriminant analysis （PLS-DA） models were established to identify the Gastrodiae Rhizoma decoction pieces with different degrees of sulfur fumigation. ResultAccording to the comparative analysis of AroChemBase database， there were significant differences in the odor characteristics of sulfur fumigated and non-sulfur fumigated Gastrodiae Rhizoma， cyclopentane， acetone and heptane might be the odor components to distinguish the degree of sulfur fumigation in Gastrodiae Rhizoma decoction pieces. The identification index of PCA model was 81， the accumulative discriminant index of the discriminating factors was 92.09% in DFA model， the supervisory model interpretation rate of PLS-DA model was 0.963 and the predictive ability parameter was 0.956， indicating that PCA， DFA and PLS-DA models could well distinguish Gastrodiae Rhizoma decoction pieces with different sulfur fumigation degrees. ConclusionHeracles NEO ultra-fast gas phase electronic nose can be used as a rapid method to identify and distinguish Gastrodiae Rhizoma decoction pieces with different levels of sulfur fumigation. Meanwhile， it can provide a rapid， simple and green method and technology for identification of traditional Chinese medicine decoction pieces by sulfur fumigation.
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OBJECTIVE To determine the conte nts of 4 main components in Rougui renshen granules ，and to establish the fingerprint and to screen differential markers affecting its quality. METHODS HPLC method was employed to determine the contents of ammonium glycyrrhizinate ，glycyrrhizin，cinnamic acid and cinnamaldehyde. HPLC fingerprints of 10 batches of Rougui renshen granules were established simultaneously. Similarity Evaluation System of Chromatographic Fingerprint of TCM （2012 edition）was used to evaluate the similarity and determine the common peak ；SPSS 25.0 and SIMCA 14.1 software were applied for cluster analysis （CA），principal component analysis （PCA）and partial least square-discriminant analysis （OPLS-DA）. The differential markers affecting sample quality were screened by using the variable importance in projection （VIP）value＞ 1 as standard. RESULTS The methodology of content determination met the relevant requirements. The contents of ammonium glycyrrhizinate，glycyrrhizin，cinnamic acid and cinnamaldehyde were 1.808 4-2.770 0，1.137 2-1.481 4，0.076 5-0.091 8 and 0.130 9-0.478 4 mg/g，respectively. A total of 16 common peaks were found in the fingerprints of 10 batches of Rougui renshen granules. Four chromatographic peaks were identified ，i.e. glycyrrhizin （peak 6），cinnamic acid （peak 10），cinnamaldehyde（peak 11）and ammonium glycyrrhizinate （peak 15）. The similarities of samples were ＞0.95. Results of CA showed that 10 batches of samples could be classified into three categories ：S3 was grouped into one category ；S1-S2，S4-S5 and S 10 were grouped into one category；S6-S9 were grouped into one category. The results of PCA showed that the cumulative contribution rate of the first three principal components was 91.918％，and the classification results were consistent with CA. The results of OPLS-DA showed that the four peaks with VIP value ＞1 were peak 11（cinnamaldehyde），peak 15（ammonium glycyrrhizinate ），peak 6（glycyrrhizin） and peak 9. CONCLUSIONS Established methods of content determination and fingerprint are accurate and reproducible ，and can be used for the quality evaluation of Rougui renshen granules. The components as ammonium glycyrrhizinate ，cinnamaldehyde， glycyrrhizin may be differential markers affecting the quality of Rougui renshen granules.
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The quality control and evaluation methods of Schizonepeta tenuifolia were established by HPLC fingerprint, multi index component content determination and chemical pattern recognition to provide basis for the quality control of medicinal materials. The chemical components of 25 batches of Schizonepeta tenuifolia panicle medicinal materials and decoction pieces collected were analyzed by high performance liquid chromatography, and the common pattern of fingerprint was established. A total of 22 common chromatographic peaks were calibrated, and their similarity was more than 0.9. The samples were divided into three categories according to different producing areas by cluster analysis. The results of principal component analysis and cluster analysis were consistent. Finally, five differential markers of different batches of Schizonepeta tenuifolia were selected by orthogonal partial least squares discriminant analysis. Through the identification of the reference substance, it was determined that peak 9 was hesperidin, peak 10 was rosmarinic acid, peak 13 was tilianin, peak 14 was quercetin, and peak 20 was pulegone. The quality evaluation method established in this study is stable and reliable, and is suitable for the quality control of Schizonepeta tenuifolia.
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OBJECTIVE To e stablish the fingerprint of Qings hen tiaozhi xiaoke tablets （QTXT）and carry out the analysis of chemical pattern recognition ，and determine the contents of seven active components simultaneously. METHODS Using coptisine hydrochloride as reference ，the Similarity Evaluation System for Chromatographic Fingerprint of TCM （2012 edition）was utilized to establish the HPLC fingerprints of 13 batches of QTXT and analyze their similarity. The common peaks were confirmed by comparing with the chromatogram of the mixed control ；the attribution of the common peak was determined by comparing the chromatograms of the sample solutions of single decoction pieces and negative sample solutions ；using SPSS 22.0 and SIMCA 14.1 software，cluster analysis （CA），principal component analysis （PCA）and orthogonal partial least squares-discriminant analysis （OPLS-DA）were carried out ，and the markers affecting the quality of QTXT were screened ，using the variable importance in projection（VIP）value greater than 1 as the standard. Using coptisine hydrochloride as internal reference ，the contents of naringin ， hesperidin，neohesperidin，berberine hydrochloride ，palmatine hydrochloride and lovastatin were determined by quantitative analysis of multicomponents by single marker （QAMS），and then compared wi th the result s（except for coptisine hydrochloride ） of external standard method. RESULTS There were 17 Δ 基金项目：江苏省“双创团队”项目[No.（2018）2024号] ＊硕士研究生。研究方向：中药新药药学。E-mail：2769544062@ common peaks in 13 batches of QTXT ，and the similarity was qq.com 0.987-0.999. Seven chromatographic peaks were identified ， # 通信作者：副研究员，硕士生导师，博士。研究方向：中药药剂 namely naringin （peak 4）， hesperidin （peak 5）， 学。E-mail：tsliur411@sina.com neohesperidin（peak 6），coptisine hydrochloride （peak 8）， ·1204· China Pharmacy 2022Vol. 33 No. 10 中国药房 2022年第33卷第10期 palmatine hydrochloride （peak 9），berberine hydrochlo ride（peak 10），lovastatin（peak 14）. Peaks 7-10 were the exclusive peaks of Coptis chinensis ；peaks 3-6 and 11-13 were the exclusive peaks of bran-fried Fructus aurantii ；peak 14 was the exclusive peak of Monascus purpureus ；peak 1 was the common peak of C. chinensis and M. purpureus . Peak 2 and 15 were the common peak of bran-fried F. aurantii and M. purpureus ；peaks 16 and 17 were the common peaks of 6 traditional Chinese medicines. The results of CA showed that 13 batches of QTXT could be divided into three categories ，S2 was clustered into one category ，S1，S9，S10 were clustered into one category ，S3-S8 and S 11-S13 were clustered into one category. The results of PCA showed that accumulative variance contribution of the first three principal components was 85.120％. Compared with CA ，S1 was further distinguished from S9 and S 10 by PCA. OPLS-DA showed that 7 common peaks with VIP value greater than 1（from large to small ）were peak 10 （berberine hydrochloride ），peak 9（palmatine hydrochloride ），peak 5（hesperidin），peak 11 and peak 8（coptisine hydrochloride ）， peak 12 and peak 6（neohesperidin）. The contents of naringin ，hesperidin，neohesperidin，berberine hydrochloride ，palmatine hydrochloride and lovastatin measured by QAMS were 40.198-77.552，6.138-13.413，71.823-125.868，11.274-49.951，3.303- 5.367，1.821-3.185 mg/g，respectively. The contents of naringin ，hesperidin，neohesperidin，berberine hydrochloride ，coptisine hydrochloride，palmatine hydrochloride and lovastatin measured by external reference method were 41.454-79.976，6.404-13.993， 74.068-129.081，11.627-51.512，5.922-12.020，3.158-5.131 and 1.901-3.325 mg/g，respectively. The deviations of the two methods （except for coptisine hydrochloride ）were all less than 3.00％. CONCLUSIONS The established HPLC fingerprint and the method of QAMS are simple ，accurate and reproducible. Combined with chemical pattern recognition analysis ，it can be used for the quality evaluation of QTXT. Berberine hydrochloride ，palmatine hydrochloride and other components may be the markers affecting the quality of the drug.
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Based on ITS sequences, the molecular identification of Cordyceps cicadae and Tolypocladium dujiaolongae was carried out, and high-performance liquid chromatography(HPLC) fingerprint combined with chemical pattern recognition method was established to differentiate C. cicadae from its adulterant T. dujiaolongae. The genomic DNA from 10 batches of C. cicadae and five batches of T. dujiaolongae was extracted, and ITS sequences were amplified by PCR and sequenced. The stable differential sites of these two species were compared and the phylogenetic tree was constructed via MEGA 7.0. HPLC was used to establish the fingerprints of C. cicadae and T. dujiaolongae, and similarity evaluation, cluster analysis(CA), principal component analysis(PCA), and partial least squares discriminant analysis(PLS-DA) were applied to investigate the chemical pattern recognition. The result showed that the sources of these two species were different, and there were 115 stable differential sites in ITS sequences of C. cicadae and T. dujiao-longae. The phylogenetic tree could distinguish them effectively. HPLC fingerprints of 18 batches of C. cicadae and 5 batches of T. dujiaolongae were established. The results of CA, PCA, and PLS-DA were consistent, which could distinguish them well, indicating that there were great differences in chemical components between C. cicadae and T. dujiaolongae. The results of PLS-DA showed that six components such as uridine, guanosine, adenosine, and N~6-(2-hydroxyethyl) adenosine were the main differential markers of the two species. ITS sequences and HPLC fingerprint combined with the chemical pattern recognition method can serve as the identification and differentiation methods for C. cicadae and T. dujiaolongae.
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Chromatography, High Pressure Liquid/methods , Cordyceps/genetics , Hypocreales , PhylogenyABSTRACT
Twenty batches of Aurantii Fructus Immaturus(AFI) were collected, with their peel and pulp taken as research objects. Ultra-high performance liquid chromatography(UPLC) fingerprints of peel and pulp of AFI were established with 17 common peaks in peel and 10 in pulp. Six kinds of flavonoids were identified, i.e., narirutin, naringin, rhoifolin, hesperidin, neohesperidin and nobiletin. The Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine was employed for similarity analysis, which showed that the chromatographic peaks of peel and pulp were basically similar to their respective reference fingerprints, with all similarities greater than 0.90. The similarity between peel and pulp of the same batch of AFI ranged from 0.850 to 0.983. Cluster analysis(CA), principal component analysis(PCA), and orthogonal partial least squares discriminant analysis(OPLS-DA) were conducted on the common peaks of peel and pulp of AFI with SPSS 17.0 and SIMCA 14.1. Combined with the reference fingerprints, these analyses revealed 12 differential components regarding peel and pulp. Further, the content of the 6 flavonoids and synephrine was determined. The proposed method integrating UPLC fingerprint and multicomponent quantitative analysis is applicable to the quality evaluation of AFI. The results provide a certain basis for the scientific connotation about the appearance characteristic of AFI.
Subject(s)
Chromatography, High Pressure Liquid , Citrus , Drugs, Chinese Herbal , SynephrineABSTRACT
OBJECTIVE：To estab lish fingerprint of Duzhong butiansu pill s，analyze its chemical pattern recognition ，and determine the contents of 7 components in Duzhong butiansu pills ，so as to provide reference for the quality control of the preparation. METHODS ：HPLC method was adopted. The determination was performed on Pntulips BP-C 18 Plus column with 0.2％ phosphoric acid water-acetonitrile as mobile phase （gradient elution ）at the flow rate of 1.0 mL/min. The detection wavelength was set at 330 nm，and column temperature was 35 ℃. The sample size was 20 μL. With paeonol as the reference，the HPLC fingerprints of 12 batches of Duzhong butiansu pills （S1-S12） were established with Similarity Evaluation System for TCM Chromatographic Fingerprint （2012 edition）； common peaks were determined and the similarity was evaluated. The chromatographic peaks were identified by comparing with the reference substance. SPSS 21.0 and SIMCA 13.0 software were used for cluster analysis and principal component analysis ，and 22 common peaks were evaluated. The contents of the identified components in 12 batches of samples was determined by the above HPLC method. RESULTS ：A total of 22 common peaks were identified in the HPLC fingerprint of 12 batches of Duzhong butiansu pills ，and the similarity was no loss than 0.960. There were 7 chemical components identified ，which were gallic acid （peak 1），chlorogenic acid （peak 3），liquiritoside（peak 6），hyperoside （peak 7），verbascoside（peak 8），icariin（peak 14）and paeonol （peak 15）. Among the 12 batches of samples ，S1，S3-S5，S7， S9 and S 11 were classified as one category ，S2，S10 and S 124Y091 were clustered into one category ，S6 was one category and S was one category. The 22 common peaks were divided into three principal components. The characteristic value （15.130） and contribution rate （68.775％） of principal component 1 were the largest ，and the score coefficients of peak 3（0.305）and peak 4（0.298）were the highest. Among 12 batches of samples，the cont ents of above 7 components were 18.196 231.951 3，0.000 6-0.049 4，0.234 8-0.415 9，0.039 5-0.079 1，0.053 5-0.249 3，0.000 5-0.000 8，0.646 4-1.146 9 mg/g，respectively. CONCLUSIONS：HPLC fingerprint of Duzhong butiansu pills is established successfully. Twelve batches of samples are clustered into 4 category. Peak 3（chlorogenic acid ）and peak 4（unknown）may be the important factors causing the difference of samples. The content of gallic acid is the highest among the 7 components.
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OBJECTIVE: To establish a chemical pattern recognition method for Panax notoginseng (P. notoginseng) and classify the main root, rhizome, and rootlet. METHODS: The fingerprints of P. notoginseng samples in three different parts were established based on HPLC method. The similarity was calculated by the Similarity Evaluation System of Chromatographic Fingerprints of Traditional Chinese Medicine (2012 edition). The pattern recognition were carried out by principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). RESULTS: The HPLC fingerprint common pattern of 35 batches of P. notoginseng samples was established. Similarity values were in the range of 0.994 to 1.000, which indicated that similarity analysis could not classify them. The established PCA model could only identify the rhizome, besides PLS-DA can completely identify the three parts of P. notoginseng. Seven characteristic peaks, such as ginsenoside Rg1, ginsenosides Rb1 were screened as biomarkers. CONCLUSION: The combination of HPLC fingerprint and chemical pattern recognition could provide a comprehensive reference for the quality control and quality evaluation of P. notoginseng.
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OBJECTIVE：To provide reference for the identification of Chebulae Fructus and Chebulae Fructus Immaturus . METHODS：UPLC method was adopted. The determination was performed on Waters Cortecs T 3 C18 column with mobile phase consisted of acetonitrile- 0.2％ phosphoric acid solution （gradient elution ）at the flow rate of 0.35 mL/min. The column temperature was 30 ℃，and the detection wavelength was set at 270 nm. The sample size was 1 μL. Using gallic acid as reference，UPLC fingerprints of 17 batches of Chebulae Fructus and 14 batches of Chebulae Fructus Immaturus were established and their similarity was evaluated by TCM Chromatographic Fingerprint Similarity Evaluation System （2012 edition）. By comparing substance control ， UV absorption spectrum and related literaturs ，common peaks were identified. PCA and PLS-DA were performed by using SPSS 20.0 and SIMCA 14.1 software. The contents of main difference components in Chebulae Fructus and Chebulae Fructus Immaturus were determined by above UPLC method and compared. RESULTS ：There were 8 common peaks in UPLC fingerprint of Chebulae Fructus and Chebulae Fructus Immaturus ，i.e. chebulic acid （peak 1），gallic acid （peak 2），punicalagin A （peak 3），punicalagin B （peak 4），corilagin（peak 6），chebulagic acid （peak 7）and chebulinic acid （peak 8）. The similarities of 17 batches of Chebulae Fructus were from 0.92 to 0.99，while 14 batches of Chebulae Fructus Immaturus were all above 0.99. The similarity of control fingerprint between Chebulae Fructus and Chebulae Fructus Immaturus was 0.909. PCA demonstrated the differences between Chebulae Fructus and Chebulae Fructus Immaturus . The results of PLS-DA were consistent with those of PCA ，and the variable importance in projection （VIP）values of peak 5，4，7，3 and 2 were above 1 in the PLS-DA model. In 31 batches of samples ，the contents of gallic acid （peak 2），punicalagin A（peak 3），punicalagin B （peak 4）and chebulagic acid （peak 7）were 2.63-10.31， 5.37-44.63，8.02-60.77，44.07-162.98 mg/g；RSDs were 40.14％， 47.91％ ，53.97％ ，36.22％（n＝31）. There was statistical significance in the differences of the mentioned 4 components between Chebulae Fructus and Chebulae Fructus Immaturus 719412818@qq.com （P＜0.05）. CONCLUSIONS ：There are significant differences between Chebulae Fructus and Chebulae Fructus Immaturus gallic acid ，punicalagin A ，punicalagin B and chebulagic acid are the main difference components for identification.
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Objective: To compare the identification effects of different pattern recognition methods combined with chemical fingerprints on Tetrastigma hemsleyanum from different habitats, and propose a new identification method for the habitats of T. hemsleyanum. Methods: A total of 72 batches of T. hemsleyanum samples were collected from Zhejiang, Yunnan and Guizhou. HPLC fingerprints were collected, 18 common peaks were marked, and the difference of principal component analysis (PCA), orthogonal partial least squares-discriminant analysis (OPLS-DA) and random forest (RF) in processing complex data of samples from different habitats was compared. Results: A total of 72 batches of T. hemsleyanum samples can only be divided into two categories by PCA, the results of OPLS were better than PCA, and the RF can completely separate the samples from three habitats. The RF combined with fingerprint can effectively identify and distinguish T. hemsleyanum from different habitats. Conclusion: This study can be used as an effective method for the quality control of T. hemsleyanum from different habitats and provide an effective reference for multi-index complex fingerprint identification from different habitats.
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Objective: To evaluate the quality of Zidan Huoxue Tablets (ZHT) based on HPLC fingerprint and multi-component content determination combined with chemical pattern recognition method. Methods: ACE Neptune-C18 column (250 mm × 4.6 mm, 5 μm) was used with acetonitrile-0.1% phosphoric acid aqueous solution as mobile phase at a flow rate of 1.0 mL/min. The detection wavelength was 275 nm and the column temperature was 25 ℃. A total of 18 batches of ZHT was analyzed, and the quality of ZHT was evaluated by the similarity evaluation system of traditional Chinese medicine chromatographic fingerprints combined with cluster analysis and principal component analysis. Results: The fingerprint of ZHT was established. Twenty-six common peaks were identified and nine of them were identified, including 2-sodium danshensu, 8-rosmarinic acid, 9-lithospermic acid, 10-salvianolic acid B, 12-salvianolic acid A, 21-dihydrotanshinone I, 22-cryptotanshinone, 23-tanshinone I, and 25-tanshinone IIA. The similarity of fingerprints of 18 batches of ZHT was between 0.93 and 1.00. The results of cluster analysis and principal component analysis were basically consistent with similarity results. After validating the multiple component quantitative analysis condition through methodology, the average recoveries were between 98.27% and 103.42%, and the RSD were in the range of 0.86%-2.53%. The content of sodium danshensu, rosmarinic acid, lithospermic acid, salvianolic acid B, dihydrotanshinone I, cryptotanshinone, tanshinone I, tanshinone IIA in 18 batches of ZHT were in the range of 0.149%-0.218%, 0.179%-0.225%, 0.222%-0.286%, 3.570%-6.399%, 0.048%-0.136%, 0.122%-0.309%, 0.061%-0.215%, 0.093%-0.413%, respectively. Conclusion: There is a certain quality difference between different batches of ZHT. Through the combination of fingerprinting, cluster analysis and principal component analysis, the quality of ZHT can be comprehensively evaluated. The establishment of this method can provide reference for the quality control and evaluation of ZHT.