Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 300193, China.
Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2019 Jul 5;218:271-280. doi: 10.1016/j.saa.2019.03.110. Epub 2019 Mar 29.
Near-infrared spectroscopy (NIRS) combined with chemometrics was used to analyze the main active ingredients including chlorogenic acid, caffeic acid, luteoloside, baicalin, ursodesoxycholic acid, and chenodeoxycholic acid in the Tanreqing injection. In this paper, first, two hundred samples collected in the product line were divided into the calibration set and prediction set, and the reference values were determined by the High Performance Liquid Chromatography- Diode Array Detector/Evaporative Light Scattering Detector (HPLC-DAD/ELSD) method. Partial least squares (PLS) analysis was implemented as a linear method for models calibrated with different preprocessing means. Wavelet transformation (WT) was introduced as a variable selection technique by means of multiscale decomposition, and wavelet coefficients were employed as the input for modeling. Furthermore, two nonlinear approaches, least squares support vector machine (LS-SVM) and Gaussian process (GP), were applied to exploit the complicated relationship between the spectra and active ingredients. The optimal models for each ingredient were obtained by LS-SVM and GP methods. The performance of the final models was evaluated by the root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP) and correlation coefficient (R). All of the models in the paper give a good calibration ability with an R value above 0.92, and the prediction ability is also satisfactory, with an R value higher than 0.85. The overall results demonstrate that nonlinear models are more stable and predictable than linear ones, and they will be more suitable for the CHM system when high accuracy analysis is required. It can be concluded that NIRS with the LS-SVM and GP modeling methods is promising for the implementation of process analytical technology (PAT) in the pharmaceutical industry of Chinese herbal injections (CHIs).
近红外光谱(NIRS)结合化学计量学用于分析包括绿原酸、咖啡酸、芦丁、黄芩苷、熊去氧胆酸和鹅去氧胆酸在内的主要活性成分。在本文中,首先,从生产线收集了两百个样本,分为校准集和预测集,并通过高效液相色谱-二极管阵列检测器/蒸发光散射检测器(HPLC-DAD/ELSD)方法确定了参考值。偏最小二乘法(PLS)分析被用作线性方法,用于校准不同预处理方法的模型。小波变换(WT)被引入作为一种变量选择技术,通过多尺度分解,将小波系数用作建模的输入。此外,两种非线性方法,最小二乘支持向量机(LS-SVM)和高斯过程(GP),被应用于挖掘光谱和活性成分之间的复杂关系。通过 LS-SVM 和 GP 方法获得了每个成分的最优模型。通过校准均方根误差(RMSEC)、交叉验证均方根误差(RMSECV)、预测均方根误差(RMSEP)和相关系数(R)评估最终模型的性能。本文中的所有模型都具有良好的校准能力,R 值均高于 0.92,预测能力也令人满意,R 值均高于 0.85。总的来说,结果表明非线性模型比线性模型更稳定和可预测,当需要高精度分析时,它们将更适合中药系统。可以得出结论,近红外光谱与 LS-SVM 和 GP 建模方法结合,有望在中药注射剂(CHIs)的制药行业中实施过程分析技术(PAT)。