基于RBF-RF 级联分类器电子鼻对中药的快速鉴别

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。

Rapid Identification of Traditional Chinese Medicine Using Electronic Nose Based on RBF-RF Cascade Classifier

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 extraction 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 difficulties 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 applied 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 identification of TCM.