• Title/Summary/Keyword: 천궁도

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Discrimination of Geographical Origin for Herbal Medicine by Mineral Content Analysis with Energy Dispersive X-Ray Fluorescence Spectrometer (에너지분산형 X-선 형광분석기를 이용한 한약재의 무기질 분석 및 이에 의한 원산지 판별)

  • Jeong, Myeong-Sil;Lee, Soo-Bok
    • Korean Journal of Food Science and Technology
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    • v.40 no.2
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    • pp.135-140
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    • 2008
  • In this study, the macromineral content ratios of four herbal medicine samples(Saposhnikoviae Radix, Bupleuri Radix, Cnidii Rhizoma, and Astragali Radix) were analyzed to discriminate their geographical origins using an energydispersive x-ray fluorescence (EDXRF) technique. EDXRF is a rapid, non-destructive, and multi-elemental analysis technique. Initially, samples of both domestic and imported herbal medicines were pulverized, and then their macromineral contents, including P, S, K, and Ca, were analyzed using EDXRF. For the discrimination of their geographical origins, canonical discriminant analysis was carried out based on the estimated macromineral relative content ratios of the samples. According to the results, the discrimination accuracies were as follows: 93.3% for Saposhnikoviae Radix, 95.7% for Bupleuri Radix, 98.8% for Cnidii Rhizoma, and 87.5% for Astragali Radix. Overall, the results imply that this technique could be used as a standard method, to discriminate their geographical origins between domestic and imported herbal medicines.

Lipids from the rhizome of Cnidium officinalis Makino (천궁으로부터 lipid 의 분리 동정)

  • Kim, Hyoung-Geun;Jeon, Hyeong-Ju;Nguyen, Trong Nguyen;Lee, Dae Young;Baek, Nam-In
    • Journal of Applied Biological Chemistry
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    • v.64 no.4
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    • pp.343-349
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    • 2021
  • The rhizomes of Cnidium officinalis were extracted in aqueous MeOH, and the concentrate was fractionated via systematic solvent fractionation to EtOAc, n-BuOH, and aqueous fractions. The repeated column chromatography of EtOAc and n-BuOH fractions using silica gel, octadecyl silica gel, and Sephadex LH-20 as stationary phase to afford five lipids. They were identified to be methyl linoleate (1), linoleic aicd (2) 6-linoleoyl-𝛼-D-glucopyranosyl 𝛽-D-fructofuranoside (3), 1-linolenoyl-3-(𝛼-D-galactopyranosyl (1→6)-𝛽-D-galactopyranosyl) glycerol (4), and 1-linoleoyl-3-(𝛼-D-galactopyranosyl (1→6)-𝛽-D-galactopyranosyl) glycerol (5) on the basis of spectroscopic data such as IR, MS, and Nuclear magnetic resonance (NMR). Compounds 1 and 3-5 were isolated for the first time from this plant in this study. The NMR data of fatty acids 1 and 2 reported in literatures are different each other. Authors identified the NMR data without ambiguity. Compound 3, a conjugate of sucrose and fatty acid, and compounds 4 and 5, digalactosyl monoglyceride, are very rarely occurred in natural source. Through the immune enhancement and anticancer activity of the reported lipid compounds, the potential as various pharmacologically active materials of Cnidium officinalis rhizome can be expected.

A Study on the Prediction Model for Bioactive Components of Cnidium officinale Makino according to Climate Change using Machine Learning (머신러닝을 이용한 기후변화에 따른 천궁 생리 활성 성분 예측 모델 연구)

  • Hyunjo Lee;Hyun Jung Koo;Kyeong Cheol Lee;Won-Kyun Joo;Cheol-Joo Chae
    • Smart Media Journal
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    • v.12 no.10
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    • pp.93-101
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    • 2023
  • Climate change has emerged as a global problem, with frequent temperature increases, droughts, and floods, and it is predicted that it will have a great impact on the characteristics and productivity of crops. Cnidium officinale is used not only as traditionally used herbal medicines, but also as various industrial raw materials such as health functional foods, natural medicines, and living materials, but productivity is decreasing due to threats such as continuous crop damage and climate change. Therefore, this paper proposes a model that can predict the physiologically active ingredient index according to the climate change scenario of Cnidium officinale, a representative medicinal crop vulnerable to climate change. In this paper, data was first augmented using the CTGAN algorithm to solve the problem of data imbalance in the collection of environment information, physiological reactions, and physiological active ingredient information. Column Shape and Column Pair Trends were used to measure augmented data quality, and overall quality of 88% was achieved on average. In addition, five models RF, SVR, XGBoost, AdaBoost, and LightBGM were used to predict phenol and flavonoid content by dividing them into ground and underground using augmented data. As a result of model evaluation, the XGBoost model showed the best performance in predicting the physiological active ingredients of the sacrum, and it was confirmed to be about twice as accurate as the SVR model.

약용작물 병해층 도감(8) - 천궁(川芎)

  • An, Tae-Jin
    • Life and Agrochemicals
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    • s.282
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    • pp.38-39
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    • 2012
  • 산형과에 속하는 다년생 초본식물로, 약용으로 재배하며 뿌리를 이용한다. 대장균이나 피부진균의 발육을 억제시키는 항균(抗菌)과 진정(鎭靜), 혈압강하(血壓降下)의 효능이 탁월하다.

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