研究生: |
林子瑜 |
---|---|
論文名稱: |
薑黃、鬱金、莪朮之比較研究 |
指導教授: |
許順吉
Xu, Shun-Ji |
學位類別: |
碩士 Master |
系所名稱: |
化學系 Department of Chemistry |
論文出版年: | 2004 |
畢業學年度: | 92 |
語文別: | 中文 |
論文頁數: | 232 |
中文關鍵詞: | 莪朮 、高效液相層析儀 、質譜儀 、薑黃屬 、基原辨識 、化學指紋圖譜 |
英文關鍵詞: | Zedoariae Rhizoma, HPLC, MS, Curcuma, species identification, chemical fingerprints |
論文種類: | 學術論文 |
相關次數: | 點閱:214 下載:0 |
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高效液相層析儀(HPLC)本為測定中藥指標成分的常用分析工具,加上高效液相層析質譜儀(LC-MS)的長足進展,遂使其成為評估中草藥品質的最佳方法。本研究即在使用光二極陣列(PDA)及質譜儀偵測器於莪朮藥材的分析。薑黃屬藥材主要有薑黃、鬱金及莪朮,其中薑黃、鬱金已有分析報告,因此本研究僅自莪朮藥材分離出十個倍半萜成分、開發一合適的高效液相層析法,以測定莪朮藥材中十個倍半萜及薑黃素(curcumin)的含量。
本研究分三個部分,第一部分為分離莪朮藥材十個倍半萜成分,並利用高效能液相層析(HPLC)開發莪朮藥材成分的分析方法。於室溫下以80%丙酮萃取莪朮藥材,萃取液經乙酸乙酯及水分配分離,再經矽膠、HPLC C-18 column層析得到十個倍半萜成分,分別為zedoaronedil(1)、procurcumenol(2)、(+)-germacrone 4, 5-epoxide(3)、dehydrocurdione(4)、curcumenone(5)、zederone(6)、curcumenol(7)、curzerenone(8)、furanodienone(9)、germacrone(10)。實驗結果顯示,利用醋酸、氰甲烷及甲醇水溶液為沖提液,在波長254 nm、280 nm及410 nm偵測下,可在120分鐘內成功地分析出這十個倍半萜成分及薑黃素。
第二部分為莪朮藥材之LC-MS定性及定量。將所分離出的標準品以infusion的方式進行MSn定性分析,並推測其碎裂途徑,挑選適合的斷裂片進行定量分析,分別比較各狀態之相對最佳條件,結果顯示以APCI做為離子源,醋酸水溶液作為流動相A,Probe溫度300℃下分析為最佳條件,偵測極限最低可達約16 pg,為PDA偵測極限的1/12。
第三部分為運用該分析方法於市售薑黃屬藥材之基原辨識研究。市售藥材主要有薑黃(Curcuma longa L.)、蓬莪朮(C. phaeocaulis Valeton)、廣西莪朮(C. kwangsiensis S. G. Lee et C. F. Liang)及溫鬱金(C. wenyujin Y. H. Chen et C. Ling)、川鬱金(C. chuanyujin C. K. Hsieh et H. Zhang)等五種植物的根莖及塊根,本研究收集55批樣本,利用各種相同或相異成分之間含量比值的差異,進行化學指紋圖譜品種鑑別,再輔以多變項統計分析,以找到最好的辨別模式。
High-performance liquid chromatography (HPLC) is originally a commonly used analysis tool that has been used in analyzing the marker substances of Chinese herb drugs. Furthermore, high-performance liquid chromatography-mass spectrometry (LC-MS) has made great advancement. As a consequence, the tools have become the best approaches for assessing the quality of Chinese herb drugs. In this study, we used photodiode array (PDA) and LC-MS in the analysis of zedoary as a drug material. Drug materials of the curcuma group include Curcumae Rhizoma, Curcumae Tuber and Zedoariae Rhizoma, of which the first two items have already been analyzed and reported. Hence, in this study we simply isolated ten sesquiterpenes from zedoary and developed a suitable HPLC analysis method for determining the contents of the ten sesquiterpenes and curcumin in zedoary.
This study is divided into three parts. In the first part, ten sesquiterpenes are isolated from zedoary and an analysis method is developed based on HPLC. The method is to extract the zedoary drug materials with 80% acetone at room temperature. The extract is partitioned with ethyl acetate and water and then through silicagel and HPLC with C-18 column to separate out the ten sesquiterpenes, namely zedoaronedil (1), procurcumenol (2), (+)-germacrone-4,5-epoxide (3), dehydrocurdione (4), curcumenone (5), zederone (6), curcumenol (7), curzerenone (8), furanodienone (9) and germacrone (10). Experimental results show that with acetic acid, acetonitrile and methanol water solution as the eluent, and detection wavelength at 254 nm, 180 nm and 410 nm, the ten sesquiterpenes and curcumin can be isolated within 120 min.
The second part involves the qualitative and quantitative analyses by LC-MS for the zedoary drug materials. The isolated authentic standards are subjected to MSn qualitative analysis under the infusion mode for speculating the fragmenting paths, and suitable fragmented pieces are used for quantitative analysis. As the various states are compared for their optimal conditions, the results show that the optimal analysis condition is with APCI as the ion source, acetic acid water solution as mobile phase A, and probe temperature at 300°C. The detection limit can reach 16 pg, which is 1/12 the detection limit of PDA.
The third part applies the above method to identifying the sources of commercial curcuma-group drug materials. Commercial articles of this group are chiefly derived from the rhizome or tuber of Curcuma longa L., C. phaeocaulis Valeton, C. kwangsiensis S. G. Lee et C. F. Liang, C. wenyujin Y. H. Chen et C. Ling and C. chuanyujin C. K. Hsieh et H. Zhang. In this study, we have gathered 55 samples, wherefore we have compared the ratios of contents of the same or different constituents, made species identification by chemical fingerprints and used multivariance statistic analysis to find the best identification mode.
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[145] http://www.cs.umd.edu/hcil/multi-cluster/; Hierarchical Clustering Explorer for Multidimensional Cluster and Outlier Detection (HCE), Human-Computer Interaction Laboratory(HCIL), University of Maryland, College Park.