College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.
J Pharm Biomed Anal. 2013 Apr 15;77:32-9. doi: 10.1016/j.jpba.2013.01.012. Epub 2013 Jan 11.
The application of near infrared (NIR) spectroscopy for on-line quantitative monitoring of alcohol precipitation of the Danhong injection was investigated. For the NIR measurements, two fiber optic probes designed to transmit NIR radiation through a 2mm path length flow cell were applied to collect spectra in real-time. Particle swarm optimization- (PSO-) based least square support vector machines (LS-SVM) and partial least squares (PLS) models were developed for quantitative analysis of the critical intermediate quality attributes: the soluble solid content (SSC) and concentrations of danshensu (DSS), protocatechuic aldehyde (PA), hydroxysafflor yellow A (HSYA) and salvianolic acid B (SAB). The optimal models were then used for on-line quantitative monitoring of alcohol precipitation. The results showed that the PSO-based LS-SVM with a radial basis function (RBF) kernel was slightly better than the conventional PLS method, even though both methods exhibited satisfactory fitting results and predictive abilities. In this study, successful models were built and applied on-line; these models proffer real-time data and instant feedback about alcohol precipitation.
本研究应用近红外（NIR）光谱法在线定量监测丹红注射液醇沉过程。在 NIR 测量过程中，采用两根光纤探头通过 2mm 光程流通池传输 NIR 辐射，实时采集光谱。基于粒子群优化（PSO）的最小二乘支持向量机（LS-SVM）和偏最小二乘法（PLS）模型被开发用于定量分析关键的中间质量属性：可溶性固形物含量（SSC）和丹参素（DSS）、原儿茶醛（PA）、羟基红花黄色 A（HSYA）和丹酚酸 B（SAB）的浓度。然后，最优模型用于在线定量监测醇沉过程。结果表明，基于 PSO 的 LS-SVM 与传统的 PLS 方法相比略有优势，尽管两种方法均显示出令人满意的拟合结果和预测能力。在本研究中，成功建立并在线应用了模型；这些模型提供了实时数据和关于醇沉过程的即时反馈。
