Table_1_Pharmaceutical Analysis Model Robustness From Bagging-PLS and PLS Using Systematic Tracking Mapping.DOC

Our work proved that processing trajectory could effectively obtain a more reliable and robust quantitative model compared with the step-by-step optimization method. The use of systematic tracking was investigated as a tool to optimize modeling parameters including calibration method, spectral pretreatment and variable selection latent factors. The variable was selected by interval partial least-squares (iPLS), backward interval partial least-square (BiPLS) and synergy interval partial least-squares (SiPLS). The models were established by Partial least squares (PLS) and Bagging-PLS. The model performance was assessed by using the root mean square errors of validation (RMSEP) and the ratio of standard error of prediction to standard deviation (RPD). The proposed procedure was used to develop the models for near infrared (NIR) datasets of active pharmaceutical ingredients in tablets and chlorogenic acid of Lonicera japonica solution in ethanol precipitation process. The results demonstrated the processing trajectory has great advantages and feasibility in the development and optimization of multivariate calibration models as well as the effectiveness of bagging model and variable selection to improve prediction accuracy and robustness.

本研究证实，相较于逐步优化方法，采用处理轨迹（processing trajectory）可有效构建更为可靠且稳健的定量模型。本研究探究了将系统追踪（systematic tracking）作为工具，用于优化建模参数，涵盖校准方法、光谱预处理以及变量选择潜因子。变量选择采用区间偏最小二乘法（interval partial least-squares, iPLS）、反向区间偏最小二乘法（backward interval partial least-square, BiPLS）以及协同区间偏最小二乘法（synergy interval partial least-squares, SiPLS）。模型构建采用偏最小二乘法（Partial least squares, PLS）与Bagging-PLS方法。模型性能通过验证集均方根误差（root mean square errors of validation, RMSEP）以及预测标准误差与标准差之比（ratio of standard error of prediction to standard deviation, RPD）进行评估。本研究所提出的流程被用于构建两类近红外（near infrared, NIR）数据集模型：一类为片剂中活性药物成分的近红外数据集，另一类为乙醇沉淀过程中忍冬（Lonicera japonica）溶液内绿原酸的近红外数据集。研究结果表明，处理轨迹在多变量校准模型的构建与优化中具备显著优势与可行性，同时证实了Bagging模型与变量选择方法对提升预测精度与模型稳健性的有效性。