基于流速调制的电子鼻系统开发及其在黄酒酒龄分类中的应用

The development of electronic nose system based on flow modulation and its application in the wine age classification of the Chinese yellow wine

一种基于流速调制的电子鼻系统,在流速可变的情况下,通过改变进气流速来扩大传感器对不同气体的响应范围,以此来提高识别正确率,缩短检测时间。应用改进的自适应主成分分析算法(Adaptive Principal Component Analysis,AD-PCA)对黄酒酒龄进行分类来验证此电子鼻系统,并将该算法的结果与支持向量机算法(Support Vector Machine,SVM)和误差反向传播神经网络算法(Back-Propagation Neural Network,BPNN)的结果进行对比,实验结果表明:对于5种不同酒龄的黄酒,AD-PCA得到的平均正确分类率为93.6%,SVM得到的平均正确分类率为92%,BPNN得到的平均正确分类率为100%,与固定流速相比,可以在保证较高准确率的基础上做到快速分类,并且有效缩短检测时间。

An electronic nose system based on flow modulation in the case of variable flow rates was designed herein to improve the recognition accuracy and shorten the detection time.The gas response range of sensors to different components and concentrations was maximized by changing the intake flow rate.The age of Chinese yellow wine was classified through adaptive principal component analysis(AD-PCA)to verify this system.The results from AD-PCA were compared with those from support vector machine(SVM)and back-propagation neural network(BPNN).The experimental results showed that among these 5 different ages of Chinese yellow wine,the average correct classification rate of AD-PCA was 93.6%,and that of SVM and BPNN was 92%and 100%,respectively.It was proved that the system could quickly classify the wine age on the basis of a higher accuracy rate,and can shorten the detection time compared with the fixed flow rate.