Wu Sha, Liu Qi-an, Wang Wei, Su Guang, Wu Jian-xiong, Bi Yu-an, Wang Zhen-zhong, Xiao Wei
Zhongguo Zhong Yao Za Zhi. 2015 Feb;40(3):437-42.
Quantitative models were established to analyze the content of chlorogenic acid and soluble solid content in the liquid-liquid extraction of Reduning injection by near-infrared (NIR) spectroscopy. Seven batches of extraction solution from the liquid-liquid extraction of Lonicerae Japonicae Flos and Artemisiae Annuae Herba were collected and NIR off-line spectra were acquired. The content of chlorogenic acid and soluble solid content were determined by the reference methods. The partial least square (PLS) and artificial neural networks (ANN) were used to build models to predict the content of chlorogenic acid and soluble solid content in the unknown samples. For PLS models, the R2 of calibration set were 0.9872, 0.9812, RMSEC were 0.1533, 0.7943, the R2 of prediction set were 0.9837, 0.9733, RMSEP were 0.2464, 1.2594, RSEP were 3.25%, 3.31%, for chlorogenic acid and soluble solid content, respectively. For ANN models, the R2 of calibration set were 0.9903, 0.9882, RMSEC were 0.0974, 0.4543, the R2 of prediction set were 0.9868, 0.9699, RMSEP were 0.1920, 0.9427, RSEP were 2.61%, 2.75%, for chlorogenic acid and soluble solid content, respectively. Both the RSEP values of chlorogenic acid and soluble solid content were lower than 6%, which can satisfy the quality control standard in the traditional Chinese medicine production process. The RSEP values of ANN models were lower than PLS models, which indicated the ANN models had better predictive performance for chlorogenic acid and soluble solid content. The established method can rapidly measure the content of chlorogenic acid and soluble solid content. The method is simple, accurate anc reliable, thus can be used for quality control of the liquid-liquid extraction of Reduning injection.
建立了定量模型，以通过近红外（NIR）光谱分析热毒宁注射液液液萃取中绿原酸的含量和可溶性固形物含量。收集了七批金银花和青蒿液液萃取的萃取液，并采集了NIR离线光谱。采用参考方法测定绿原酸含量和可溶性固形物含量。使用偏最小二乘法（PLS）和人工神经网络（ANN）建立模型，以预测未知样品中绿原酸的含量和可溶性固形物含量。对于PLS模型，绿原酸和可溶性固形物含量的校正集R2分别为0.9872、0.9812，RMSEC分别为0.1533、0.7943，预测集R2分别为0.9837、0.9733，RMSEP分别为0.2464、1.2594，RSEP分别为3.25%、3.31%。对于ANN模型，绿原酸和可溶性固形物含量的校正集R2分别为0.9903、0.9882，RMSEC分别为0.0974、0.4543，预测集R2分别为0.9868、0.9699，RMSEP分别为0.1920、0.9427，RSEP分别为2.61%、2.75%。绿原酸和可溶性固形物含量的RSEP值均低于6%，可满足中药生产过程中的质量控制标准。ANN模型的RSEP值低于PLS模型，表明ANN模型对绿原酸和可溶性固形物含量具有更好的预测性能。所建立的方法能够快速测定绿原酸的含量和可溶性固形物含量。该方法简单、准确、可靠，可用于热毒宁注射液液液萃取的质量控制。
