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该文采用约束背景双线性分解算法(CBBL)对以高效液相色谱(HPLC)方法分离分析的灰色分析体系进行了多元校正研究.针对采用包括CBBL在内的矩阵校正方法处理HPLC灰色分析体系的固有缺陷,即在相关组分的色谱保留时间重现性较低的情形下多元校正的结果不理想,对CBBL方法进行了改进,即将待测组分的浓度与组分的色谱保留时间同时作为优化的参量引入CBBL,并采用遗传算法(GA)优化CBBL,对于模拟的组分保留时间飘移严重的HPLC灰色分析体系及保留时间重现性不佳的多种酚类化合物组成的实际HPLC灰色分析体系进行了多元校正分析,成功克服了经典CBBL的固有缺陷,取得了较理想的多元校正结果.另外,该研究所建议的方法的校正结果也显著优于传统的残差双线性分解法(RBL)以及秩消失因子分析法(RAFA).

Constrained background bilinearization (CBBL) method was applied for multivariate calibration analysis of the grey analytical system in high performance liquid chromatography (HPLC).By including the variables of the concentrations and the retention time of the analytes simultaneously,the standard CBBL was modified for the multivariate calibration of the HPLC system with poor retention precision.The CBBL was optimized globally by genetic algorithm (GA).That is to say,both the concentrations and the retention times of the analytes were optimized globally and simultaneously by GA.The modified CBBL was applied in the calibration analysis for both simulated and experimental HPLC system with poor retention precision.The experimental data were collected from HPLC separation system for phenolic compounds.The modified CBBL was verified to be useful to prevent the inherent limitation of the standard CBBL,which means that the standard CBBL may result in poor calibration results in the case of poor retention precision in chromatography system.Moreover,the modified CBBL can give not only the concentrations but also the retention time of the analytes.i.e.,more useful information of the analytes can be generated by the modified CBBL.Subsequently,nearly ideal calibration results were obtained.On the other hand,comparing with the calibration results by the classical rank annihilation factor analysis (RAFA) and residual bilinearization (RBL) method,the results given by the modified CBBL were also improved significantly for the HPLC systems studied in this work.