萜类化合物的QSRR研究及其在结构鉴定中的应用

Quantitative Structure-Retention Relationship for Terpenes and Their Application in Structure Characterization

采用顶空固相微萃取(SPME)-气相色谱质谱(GC-MS)联用技术分析石香薷和腊梅鲜花中的萜类化合物,通过保留指数与质谱解析相结合,分别对化合物进行结构鉴定,共鉴定出17种单萜化合物,30种倍半萜化合物.采用遗传算法(GFA)分别对单萜及倍半萜化合物建立定量结构-色谱保留关系(QSRR)预测模型,并对该模型进行显著性及预测能力的检测.同时,利用计算所得到的模型分别对随机选取的几个萜类化合物进行保留指数预测.结果表明:计算保留指数与预测保留指数接近,模型预测性能较好.该研究为各种单萜化合物及倍半萜化合物保留指数的预测提供了一种有效手段,同时,为建立有效的GC-MS定性方法提供了一定的依据。

The terpenes released from Mosla chinensis Maxim and fresh flowers of Chimonanthus praecox were investigated by means of solid-phase microextraction （SPME） coupled to gas chromatography-mass spectrometry （GC-MS）. The structure of terpenes was identified by MS and retention index, by which 17 monoterpenes and 30 sesquitel-penes were identified in this study. The quantitative structure and retention relationships of these monoterpenes and sesquiterpenes were investigated by GFA models within Cerius2 molecular modeling program package and then validated by F-test and predictive-ability test. The retention indices of several terpenes randomly selected were then predicted by QSRR models. The results demonstrated that the models had perfect predictive ability. This investigation provided an effective method for predicting the retention indices of monoterpenes and sesquiterpenes. It is worthwhile in future study for GC-MS qualitative analysis.