CAJ | 학술논문

目的：将电子鼻引入中药研究领域，探讨其在实际应用中的难点并提出解决方案，建立优化判别模型，为中药鉴别提供一种简便、快速、有效的分析方法，同时为气敏传感器的研发及应用提供新思路。方法：采用电子鼻提取中药气味特征，基于MOS传感器的离子迁移谱，建立中药气味指纹图谱。以传感器最大响应值为分析指标，针对鉴别难点，提出两种解决方案：尝试不同检测器，即扩充传感器数量，尽量缩小“嗅觉盲区”；采用“级联分类器”构建法，即采用径向基函数（RBF）与随机森林（RF）二级级联分类器构建判别模型。通过十折交叉验证和外部测试集验证对所建模型进行系统性能的评估。结果：两种方案准确、可行，具有较高的正判率和较好的泛化能力（所得最高正判率分别为95%和100%、96%和80%）。结论：本研究首次采用“级联分类器”模式构建中药电子鼻鉴别的判别模型，在传感器数量有限的情况下，从所得数据中挖掘最大信息量；以“拆分任务、剥离难点、由易到难、分级递进”为原则，实现电子鼻对中药的快速、准确鉴别。所建模式识别法在可操作性、鉴别准确率和稳定性上均优于传统嗅觉识别法，为中药鉴别提供一种简便、快速的分析方法。
목적：장전자비인입중약연구영역，탐토기재실제응용중적난점병제출해결방안，건립우화판별모형，위중약감별제공일충간편、쾌속、유효적분석방법，동시위기민전감기적연발급응용제공신사로。방법：채용전자비제취중약기미특정，기우MOS전감기적리자천이보，건립중약기미지문도보。이전감기최대향응치위분석지표，침대감별난점，제출량충해결방안：상시불동검측기，즉확충전감기수량，진량축소“후각맹구”；채용“급련분류기”구건법，즉채용경향기함수（RBF）여수궤삼림（RF）이급급련분류기구건판별모형。통과십절교차험증화외부측시집험증대소건모형진행계통성능적평고。결과：량충방안준학、가행，구유교고적정판솔화교호적범화능력（소득최고정판솔분별위95%화100%、96%화80%）。결론：본연구수차채용“급련분류기”모식구건중약전자비감별적판별모형，재전감기수량유한적정황하，종소득수거중알굴최대신식량；이“탁분임무、박리난점、유역도난、분급체진”위원칙，실현전자비대중약적쾌속、준학감별。소건모식식별법재가조작성、감별준학솔화은정성상균우우전통후각식별법，위중약감별제공일충간편、쾌속적분석방법。
This study was aimed to apply the electronic nose (E-nose) in the research of traditional Chinese medicine (TCM). The discussion was made on difficulties of using E-nose. The solution plan was proposed and the discrimination model was established. It provided a simple, rapid and effective analysi method in the identification of TCM. It also provided new ideas for the research and application of gas sensor arrays. E-nose was used in the ex-traction of TCM scent characteristics. Based on ion mobility spectrometry of MOS sensor, the fingerprint of TCM scent was established. The maximum response value of the sensor was used as analysis index. According to the diffi-culties of identification, two solution plans were proposed. Firstly, different detectors were employed to complete the classification. Secondly, radial basis function (RBF) and random forests (RF) were combined and then a cascade classifier was constructed in order to achieve the maximum of information obtained in conditions where the number of measurements, metal oxide semiconductor sensors in E-nose was limited. The results showed that both plans were accurate and practical with relatively high upper correct judge rate and better cross-validation (The highest upper correct judge rates were 95% and 100%, 96% and 80%, respectively). It was concluded that this study firstly ap-plied cascade classifier in the establishment of TCM identification by E-nose. With limited amount of sensors, the maximum information was received through data mining. Using E-nose in the identification of TCM was rapid and accurate. The established pattern recognition method was maneuverable with accurate identification rate and stability compared to conventional sensory identification method. It provided a simple and rapid analysis method for the iden-tification of TCM.