Institute of Modern Chinese Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, PR China.
Jiangsu Kanion Pharmaceutical Co., Ltd., Lianyungang 222001, PR China.
Spectrochim Acta A Mol Biomol Spectrosc. 2019 Jul 5;218:33-39. doi: 10.1016/j.saa.2019.03.113. Epub 2019 Mar 29.
Characteristic variables are essential and necessary basis in model construction, and are related to the prediction result closely in near infrared spectroscopy (NIRS) analysis. However, the same compound usually has different characteristic variables for different analysis and it would be lower correlation between variables and structure in many researches. So, the accuracy and reliability are expected to improve by exploring characteristic variables in different spectrum analysis. In this study, competitive adaptive weighted resampling method (CARS) was applied to select characteristic variables related to baicalin from NIRS analysis data, which were applied to analysis of baicalin in three different processes including the herb, extraction process and concentration process of Scutellaria baicalensis. After application of CARS method, 70, 50 and 50 variables were selected respectively from three processes above. The selected variables were firstly analyzed by statistical methods that they were found to be consistent and correlated among three different processes after one-way analysis of variance test and Kendall's W. Partial least-squares (PLS) regression and extreme learning machine (ELM) models were constructed based on optimized data. Models after variable selection were less complicated and had better prediction results than global models. After comparison, CARS-PLS was suitable for the prediction of extraction process, while for the concentration process and herb, CARS-ELM performed better. The Rc value of the herb, extraction and concentration model were 0.9469, 0.9841 and 0.9675, respectively. The RSEP values were 4.54%, 6.96% and 8.37%, respectively. The results help to frame a theoretical basis for characteristic variables of baicalin.
特征变量是模型构建的重要基础，与近红外光谱（NIRS）分析的预测结果密切相关。然而，同一化合物在不同分析中通常具有不同的特征变量，在许多研究中变量与结构之间的相关性较低。因此，通过探索不同光谱分析中的特征变量，有望提高分析的准确性和可靠性。本研究应用竞争自适应加权重采样法（CARS）从 NIRS 分析数据中选择与黄芩苷相关的特征变量，用于分析黄芩的三种不同过程，包括黄芩药材、提取过程和黄芩浓度过程。CARS 方法应用后，分别从上述三个过程中选择了 70、50 和 50 个变量。通过统计方法分析所选变量，发现它们在单向方差检验和 Kendall W 检验后在三个不同过程中具有一致性和相关性。基于优化数据构建偏最小二乘（PLS）回归和极限学习机（ELM）模型。与全局模型相比，经过变量选择的模型更简单，预测结果更好。比较后发现，CARS-PLS 适用于提取过程的预测，而对于浓度过程和药材，CARS-ELM 表现更好。药材、提取和浓缩模型的 Rc 值分别为 0.9469、0.9841 和 0.9675。RSEP 值分别为 4.54%、6.96%和 8.37%。结果有助于为黄芩苷的特征变量构建理论基础。
