查詢結果分析
來源資料
相關文獻
- Identification for Raw Materials of Chinese Herbal Medicines Using FT-NIR Spectroscopy
- A Robust Identification Model for Herbal Medicine Using Near Infrared Spectroscopy and Artificial Neural Network
- 近紅外光譜儀器之分析技術及其應用
- NIR應用於汽油品質量測
- HPLC分析法在中草藥質量鑑定及其方法建立之進行模式
- 近年有關中草藥之臨床研究論文評介
- 抗老化中草藥的作用機轉及其有效成分的研究
- 中草藥原粉末與水萃取粉末效果比較試驗
- 一種研究環氧樹脂化學反應動力學的新方法:霍氏轉換近紅外光譜分析
- 中藥,臺灣生技強心針?
第1筆 /總和 1 筆
/ 1 筆
頁籤選單縮合
題名 | Identification for Raw Materials of Chinese Herbal Medicines Using FT-NIR Spectroscopy=以FT-NIR鑑別中草藥原料之研究 |
---|---|
作者姓名(中文) | 楊翕雯; 陳世銘; 楊宜璋; 莊永坤; | 書刊名 | 農業機械學刊 |
卷期 | 21:1 2012.03[民101.03] |
頁次 | 頁21-30 |
分類號 | 341.95 |
關鍵詞 | 近紅外光; 傅立葉轉換近紅外光; 中草藥; Near-infrared; Fourier transform near-infrared; Herbal medicine; |
語文 | 英文(English) |
中文摘要 | 中草藥在醫學上的用途越來越多而廣,因此中草藥的原料控管是藥廠很重要的一項工作。本研究以擷取自傅立葉轉換之近紅外光光譜(Fourier transform near-infrared, FT-NIR)建立18種中草藥粗原料之鑑別模式。在這項研究中,不同的中草藥原料以研磨成粗粉末型態進行光譜量測,並建立中草藥之光譜資料庫。以NIRFlex進行分光光度計之光譜取得,而光譜前處理和鑑別模式之建立方法則使用NIRCal進行資料轉換與分析。以粉末光譜建立之鑑別模式,對校正組71個樣本,可以達到100%的辨識率;對於驗證組34個樣本,以校正組所建立之模式進行預測分析,可準確辨識33個樣本,其辨識正確率達97%,綜合上論,以FT-NIR進行18種中草藥105個樣本之鑑別可達99%之辨識正確率。 |
英文摘要 | The inclusion of herbal medicines in modern medication is steadily increasing, and the control and monitoring of the herbal medicine materials is a crucial work in the pharmaceutical factory. This study examined the discrimination analysis on FTNIR spectra of 18 kinds of raw medicinal herb materials. Several pattern recognition methods were compared in the identification of these herbal raw materials using Fourier transform near-infrared spectroscopy (FT-NIR). In this study, raw herbal materials (ground into a coarse powder) were used to develop the herbal spectra library. Samples were scanned and pattern recognition techniques were adopted to calculate variations among herbal materials. The spectral pretreatments and pattern recognition methods were conducted using NIRFlex and NIRCal. Regarding 71 samples in the calibration set, the identification accuracy was 100%. In the validation set of 34 samples, 33 samples were successfully discriminated, and the identification accuracy was 97%. |
本系統之摘要資訊系依該期刊論文摘要之資訊為主。