摘要
采用6只掺杂纳米ZnO厚膜传感器构成的电子鼻对15种商业食醋进行了测量,采用主元分析法(PCA)对电子鼻信号与食醋种类、配料、发酵方式和产地之间的关系进行了分析,电子鼻信号在上述食醋特征上表现出很强的聚类特性。针对上述特征,结合人工神经网络(ANN)、K近邻法(KNN)对15种食醋进行了识别,识别率为98.3%。研究表明:采用特征识别的电子鼻具有良好的灵活性和强健性。
Fifteen commercial Chinese vinegars are studied by an electronic nose containing six doped nano ZnO thick-film gas sensors. The relationships between the signal of electronic nose and the kinds, raw materials, fermentation method and origin of the vinegars are analyzed by principal component analysis （PCA） algorithm. The signal of electronic nose presents dustering characteristics according to above characteristics. Artificial neural network （ANN） incorporating with K nearest neighbors （KNN） is used to perform the identification of 15 kinds of vimegar. The identification accuracy of the vinegars is 98. 3%. It is proved that electronic nose used the characteristics for vinegars identification is flexible and robust.
出处
《传感器与微系统》
CSCD
北大核心
2008年第6期18-20,23,共4页
Transducer and Microsystem Technologies
关键词
电子鼻
食醋
特征
识别
electronic nose
vinegar
characteristic
identification
