College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.
Shandong Academy of Pharmaceutical Sciences, Jinan 250098, China.
Molecules. 2022 Dec 20;28(1):4. doi: 10.3390/molecules28010004.
The selection of key variables is an important step that improves the prediction performance of a near-infrared (NIR) real-time monitoring system. Combined with chemometrics, NIR spectroscopy was employed to construct high predictive accuracy, interpretable models for the rapid detection of the alcohol precipitation process of Lanqin oral solution (LOS). The variable combination population analysis-iteratively retaining informative variables (VCPA-IRIV) was innovatively introduced into the variable screening process of the model of geniposide and baicalin. Compared with the commonly used synergy interval partial least squares regression, competitive adaptive reweighted sampling, and random frog, VCPA-IRIV achieved the maximum compression of variable space. VCPA-IRIV-partial least squares regression (PLSR) only needs to use about 1% of the number of variables of the original data set to construct models with Rp values greater than 0.95 and RMSEP values less than 10%. With the advantages of simplicity and strong interpretability, the prediction ability of the PLSR models had been significantly improved simultaneously. The VCPA-IRIV-PLSR models met the requirements of rapid quality detection. The real-time detection system can help researchers to understand the quality rules of geniposide and baicalin in the alcohol precipitation process of LOS and provide a reference for the optimization of a LOS quality control system.
关键变量的选择是提高近红外（NIR）实时监测系统预测性能的重要步骤。结合化学计量学，采用近红外光谱法构建了高预测精度、可解释的模型，用于快速检测蓝芩口服液（LOS）的醇沉过程。创新性地将变量组合种群分析-迭代保留信息变量（VCPA-IRIV）引入到模型的栀子苷和黄芩苷的变量筛选过程中。与常用的协同区间偏最小二乘回归、竞争自适应重加权采样和随机蛙算法相比，VCPA-IRIV 实现了变量空间的最大压缩。VCPA-IRIV-偏最小二乘回归（PLSR）仅需使用原始数据集变量数的约 1%，即可构建 Rp 值大于 0.95 和 RMSEP 值小于 10%的模型。具有简单和强解释性的优点，同时显著提高了 PLSR 模型的预测能力。VCPA-IRIV-PLSR 模型满足快速质量检测的要求。实时检测系统可以帮助研究人员了解 LOS 醇沉过程中栀子苷和黄芩苷的质量规律，为 LOS 质量控制系统的优化提供参考。
