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ORIGINAL ARTICLE
Year : 2015  |  Volume : 11  |  Issue : 43  |  Page : 643-650

On-line quantitative monitoring of liquid-liquid extraction of Lonicera japonica and Artemisia annua using near-infrared spectroscopy and chemometrics


1 College of Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 100102, China
2 College of Pharmaceutical Science, Zhejiang University, Hangzhou, 310058, China
3 National Key Laboratory of Pharmaceutical New Technology for Chinese Medicine, Kanion Pharmaceutical Corporation, Lianyungang, 222000, China
4 College of Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 100102; National Key Laboratory of Pharmaceutical New Technology for Chinese Medicine, Kanion Pharmaceutical Corporation, Lianyungang, 222000, China

Correspondence Address:
Wei Xiao
National Key Laboratory of Pharmaceutical New Technology for Chinese Medicine, Kanion Pharmaceutical Corporation, Lianyungang, 222000
China
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Source of Support: We gratefully acknowledge financial support provided by the National Science and Technology Major Project “Key New Drug Creation and Manufacturing Program” (Grant nos.2013ZX09402203)., Conflict of Interest: None declared.


DOI: 10.4103/0973-1296.160465

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Background: Liquid liquid extraction of Lonicera japonica and Artemisia annua (JQ) plays a significant role in manufacturing Reduning injection. Many process parameters may influence liquid liquid extraction and cause fluctuations in product quality. Objective: To develop a near infrared (NIR) spectroscopy method for on line monitoring of liquid liquid extraction of JQ. Materials and Methods: Eleven batches of JQ extraction solution were obtained, ten for building quantitative models and one for assessing the predictive accuracy of established models. Neochlorogenic acid (NCA), chlorogenic acid (CA), cryptochlorogenic acid (CCA), isochlorogenic acid B (ICAB), isochlorogenic acid A (ICAA), isochlorogenic acid C (ICAC) and soluble solid content (SSC) were selected as quality control indicators, and measured by reference methods. NIR spectra were collected in transmittance mode. After selecting the spectral sub ranges, optimizing the spectral pretreatment and neglecting outliers, partial least squares regression models were built to predict the content of indicators. The model performance was evaluated by the coefficients of determination (R2), the root mean square errors of prediction (RMSEP) and the relative standard error of prediction (RSEP). Results: For NCA, CA, CCA, ICAB, ICAA, ICAC and SSC, R2 was 0.9674, 0.9704, 0.9641, 0.9514, 0.9436, 0.9640, 0.9809, RMSEP was 0.0280, 0.2913, 0.0710, 0.0590, 0.0815, 0.1506, 1.167, and RSEP was 2.32%, 4.14%, 3.86%, 5.65%, 7.29%, 6.95% and 4.18%, respectively. Conclusion: This study demonstrated that NIR spectroscopy could provide good predictive ability in monitoring of the content of quality control indicators in liquid liquid extraction of JQ.


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