气相色谱柱温智能优化用于实际样品分离的方法发展

Practical Applications of Gas Chromatographic Intelligent Optimization Method

本文叙述了气相色谱柱温智能最佳化程序在分离三个空气中毒物样品的应用。针对“难分离物质对”设计升温阶梯不仅容易找到最佳条件，而且计算量少。预测的保留时间精密度在±１．５％以内，难分离对的总分离效能指标的精密度在±５％以内。

Three air samples containing toxics were separated by GC intelligent optimization method.Results showed that it is much easy for“difficult to separate pairs”to design temperature steps based on“difficult to separate pairs”.The retention time predicted by this method are accurate within ±1.5%and the resolution values for difficult to separate pairs are reliable within ±5%.