CAJ | 학술논문

目的：探讨基于脂类成分气-质（GC-MS）联用图谱数据的中药寒热药性识别模型，建立药性特征标记统计模式识别方法。方法：以60种寒或热性中药脂类成分的GC-MS图谱为研究对象，建立数据库，比较5种建模策略，筛选识别脂类成分GC-MS指纹图谱药性特征标记的最优模型。结果：支持向量机（SVM）为筛选60味中药的寒热药性特征标记的最佳模型，能够有效标记中药寒热药性特征区域。趋于寒性最强的标记在保留时间61.550 min处，趋于热性最强的标记在保留时间31.395 min处。结论：中药寒热药性与脂类成分密切相关，脂类成分是中药药性的物质基础之一，基于物质成分与药性建立的数理统计模型可识别和预测中药的寒热药性。
목적：탐토기우지류성분기-질（GC-MS）련용도보수거적중약한열약성식별모형，건립약성특정표기통계모식식별방법。방법：이60충한혹열성중약지류성분적GC-MS도보위연구대상，건립수거고，비교5충건모책략，사선식별지류성분GC-MS지문도보약성특정표기적최우모형。결과：지지향량궤（SVM）위사선60미중약적한열약성특정표기적최가모형，능구유효표기중약한열약성특정구역。추우한성최강적표기재보류시간61.550 min처，추우열성최강적표기재보류시간31.395 min처。결론：중약한열약성여지류성분밀절상관，지류성분시중약약성적물질기출지일，기우물질성분여약성건립적수리통계모형가식별화예측중약적한열약성。
This study was aimed to explore recognition models and to establish statistical pattern recognition methods of cold-hot property markers based on lipid components GC-MS chromatogram of Chinese materia medica (CMM). GC-MS fingerprints of lipid components contained in 60 kinds of cold or hot property of CMM were used as the research object. The database was established. Five types of model establishment strategies were compared. Optimal modeling patterns were screened for the identification of herbal property markers of lipid components GC-MS chromatogram. The results showed that support vector machine (SVM) was the best model to discriminate cold or hot property among 60 types of CMM, which were able to effectively mark the characteristic area. The strongest markers tending to cold property was at the retention time of 61.550 min, while the strongest markers tending to hot property was at the retention time of 31.395 min. It was concluded that cold or hot property of CMM had close relationship with lipid components. The lipid component was one of the material bases of CMM. The mathematical statistical model based on material base and herbal property can be used to identify and predict the cold and hot property of CMM.