针对中药质量标准从单一成分测定向多指标成分测定转变过程中HPLC分析方法重建繁琐费时的难题，围绕如何根据复方中药特点高效地建立HPLC分析方法的问题，本课题选择丹参、川芎、红花、赤芍4味中药及其配伍而成的多种复方中药为研究案例，拟利用实验设计、多元曲线分辨、多元统计建模、Monte-Carlo仿真等方法及前馈控制策略，重点研究单味中药与复方中药共有的定量色谱保留规律（即分析参数与中药成分色谱参数间的定量关系），建立多元统计模型对该规律进行表征，并研究不同色谱柱之间的模型转移方法，从而形成基于色谱保留多元统计模型的复方中药分析方法高效构建模式，为色谱分析方法的建立探索新途径。这不仅能有效地总结和利用单味中药分析方法开发经验，显著提高复方中药分析方法开发效率，而且能在含测指标增加或改变时高效地调整得到相适应的分析方法，从而为中药质量标准提升和中药配伍物质基础变化规律研究提供更好的支持。

The redevelopment of HPLC analytical methods for traditional Chinese medicine (TCM) formulae is usually complex and time-consuming, which is one of the technological problems frequently encountered during the single component determination to multi-component determination upgrade of the quality standards of TCMs. More efficient development approaches based on the characteristics of TCM formulae are worthy of study. Therefore, this project aims to explore a rapid development approach of HPLC methods for TCM formulae, which takes advantage of the mutual regularities of chromatographic retention behaviors between TCM formulae and their constituting single TCMs (i.e. the quantitative relationships between the analytical parameters and the chromatographic parameters of TCM components). Four TCMs including Salviae Miltiorrhizae Radix et Rhizoma, Chuanxiong Rhizoma, Carthami Flos, Paeoniae Radix Rubra and various TCM formulae derived from them will be taken for case studies in this project. The quantitative regularities of the chromatographic retention behaviors of the components in the four TCMs will be studied using design of experiments methodology, multivariate curve resolution and multivariate statistical modeling methods, and then represented in the form of multivariate statistical models. Based on these models and Monte-Carlo simulations, the HPLC methods for different TCM formulae derived from the four TCMs will be optimized rapidly using feedforward control strategy. Meanwhile, the methods for transferring the statistical models between different chromatographic columns will be studied. Based on the results of these studies, a rapid development approach of HPLC methods for TCM formulae based on multivariate statistical modeling of chromatographic retention behaviors will be sufficiently developed, which will provide a new pattern of HPLC method development. The approach is expected to effectively summarize and utilize the experiences from the development of analytical methods of single TCMs to significantly facilitate the development of HPLC methods for TCM formulae, and be able to efficiently adjust the analytical methods for the determination of more different components. Therefore, the approach proposed will provide a good support for the quality standard upgrade of TCMs and also for the study of material basis transformation during the concerting of single TCMs.