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首页> 外文期刊>Pharmacognosy magazine >On-line quantitative monitoring of liquid-liquid extraction of Lonicera japonica and Artemisia annua using near-infrared spectroscopy and chemometrics
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On-line quantitative monitoring of liquid-liquid extraction of Lonicera japonica and Artemisia annua using near-infrared spectroscopy and chemometrics

机译:利用近红外光谱和化学计量学在线监测忍冬忍冬和青蒿的液-液萃取

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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.
机译:背景:日本忍冬和青蒿(JQ)的液体提取在制造热定宁注射液中起着重要作用。许多工艺参数可能会影响液-液萃取并导致产品质量波动。目的:开发一种近红外(NIR)光谱方法来在线监测JQ的液体和液体。材料与方法:11批JQ获得了提取液,其中十种用于建立定量模型,一种用于评估已建立模型的预测准确性。选择新绿原酸(NCA),绿原酸(CA),隐绿原酸(CCA),异绿原酸B(ICAB),异绿原酸A(ICAA),异绿原酸C(ICAC)和可溶性固形物(SSC)指标,并通过参考方法进行测量。 NIR光谱以透射模式收集。在选择光谱子范围,优化光谱预处理并忽略离群值之后,建立了偏最小二乘回归模型来预测指标的内容。通过确定系数(R2),预测的均方根误差(RMSEP)和预测的相对标准误差(RSEP)评估模型性能。结果:对于NCA,CA,CCA,ICAB,ICAA,ICAC和SSC,R2为0.9674、0.9704、0.9641、0.9514、0.9436、0.9640、0.9809,RMSEP为0.0280、0.2913、0.0710、0.0590、0.0815、0.1506、1.167,RSEP为2.32%,4.14%,3.86%,5.65%,7.29结论:本研究表明,近红外光谱技术可以监测JQ液液萃取中质量控制指标的含量,具有良好的预测能力。

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