一种基于电子鼻的辛味中药材的分类鉴别方法研究

为获取新的气味识别方法以提高智能传感器模式分类识别准确率和速度，使用了内置10个传感器的便携式电子鼻PEN3对辛味中药材进行气味采集检测.将辛味中药材在烧杯中进行密封静置待其形成稳定的气味顶空环境时，运用电子鼻对其进行检测采样得到样品高维气味数据信息.与传统的线性数据分析方法不同，针对气味蕴含多种诸如浓度、各种挥发性物质成分等特征，可知气味非线性的本质特征，在本次分析中采用了流形算法中的非线性的局部线性嵌入（Locally Linear Embedding,LLE）算法对非线性的气味数据进行特征提取与降维，再采用基于Fisher的线性判别分析（Linear Discriminant Analysis,LDA）实现对特征子空间的模式聚类与分类，通过多次实验优化LLE算法的参数，得到了最佳的辛味中药材的模式识别效果.分析结果表明,运用LLE和LDA相结合的算法（即LLE_LDA）可以很好地完成不同种类辛味中药材的挥发性气味信息的模式分类,为深层次地分析基于电子鼻的气味数据信息提供了一种新方法.

Classification of Pungent Chinese Herbals by Using Electronic Nose

In this approach, the intelligent portable electronic nose with a gas sensor array containing ten gas sensors was used to collect the odor information of different varieties of pungent Chinese herbals. In order to get a new sensor data processing solution to pattern recognition of the Chinese herbals accurately, a manifold algorithm named Locally Linear Embedding（LLE）was used to finish the dimensionality reduction and feature extraction of the odor data. And then the Fisher Linear Discriminant Analysis(LDA) was adopted to complete the identification via clustering. After many times of experiments to optimize the parameters of LLE, the classification results are quite perfect. The results demonstrate that by using this new combined method, the electronic nose can not only distinguish 6 types of Chinese herbals, but also identify the 3 different batches and the 3 different denominations of origin accurately, which provides a new method for further analysis.