紫外可见光谱结合化学计量法快速检测双黄连口服液大类成分

Rapid Detection of Major Components in Shuanghuanglian Oral Liquid by UV-Vis Spectroscopy Combined with Chemometrics

关键词:  紫外可见光谱  化学计量法  支持向量回归  最小二乘支持向量回归  BP神经网络  双黄连口服液  质量控制

Key words:  UV-Vis spectrum  chemometrics  support vector regression  least square support vector regression  BP neural network  Shuanghuanglian oral liquid  quality control

摘要:

目的 基于紫外可见光谱和化学计量学方法建立1种快速检测双黄连口服液质量的方法。方法 采集双黄连口服液紫外可见光谱数据，并使用主成分分析筛选剔除异常样本数据。采用Kennard-Stone算法将所有样本按照7∶3的比例划分为训练集和测试集。采用一阶求导、标准正态变量变换对数据进行预处理，然后结合竞争性自适应重加权采样法提取特征波长，最后将支持向量回归(support vector regression，SVR)、最小二乘支持向量回归(least squares of support vector regression，LS-SVR)、前馈型反向传播(back propagation，BP)神经网络3种方法用于可溶性固形物(soluble solids content，SSC)、总黄酮(total flavones，TF)的定量分析模型的建立。结果 3种模型的决定系数R²均≥0.816 8，均方根误差RMSE均≤4.378 2，均获得了较好的预测效果。对测试集SSC及TF预测结果进行对比发现，与BP神经网络、LS-SVR相比，SVR模型获得了最大R²以及最小RMSE。SVR-SSC模型的R²为0.999 8，RMSE为0.260 3，SVR-TF模型的R²为0.998 3，RMSE为0.543 3。结论 紫外可见光谱结合SVR可以提供1种双黄连口服液质量的高精度快速现场检测方法。

Abstract:

OBJECTIVE To study on a rapid method for determining the quality of Shuanghuanglian oral liquid based on UV-Vis spectroscopy and chemometrics. METHODS The UV-Vis spectrum data of Shuanghuanglian oral liquid were collected, and the principal component analysis was used to screen out the abnormal sample data. All samples were segmented into training sets and test sets in a 7:3 ratio using the Kennard-Stone algorithm. The screened data was pre-processed by first derivation and conventional normal variate transform. Then, data feature extraction was performed by competitive adaptive reweighted sampling. Consequently, support vector regression(SVR), least squares to support vector regression(LS-SVR) and back propagation(BP) neural network was used to establish a quantitative analysis model of soluble solids content(SSC) and total flavones(TF). RESULTS All the models achieved good prediction effect, R² was ≥ 0.816 8, RMSE was ≤ 4.378 2. After comparing the SSC model and the TF model, it was revealed that compared with BP neural network and LS-SVR, the SVR model obtained the highest R² and the lowest RMSE. The R² of the SVR-SSC model was 0.999 8, RMSE was 0.260 3, and R² of SVR-TF model was 0.998 3, RMSE was 0.543 3. CONCLUSION UV-Vis spectrum combined with SVR can provide a high-precision and rapid on-site detection method for the quality of Shuanghuanglian oral liquid.