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。

We designed an electronic nose system based on flow modulated,in the case of variable flow rates,in order to improve the accuracy of recognition and short the detection time, we maximize the gas response range of sensors to different components and concentrations by changing the intake flow rate.We used adaptive principal component analysis（AD-PCA） to classify the age of Chinese yellow wine in order to verify the system, and  compare the result of AD-PCA with support vector machine（SVM）and back-propagation neural network（BPNN）, The experimental resultshows that among the5 different ages of Chinese yellow wine,the average correct classification rate by AD-PCA is93.6%, SVM is92% and BPNN is 100%,it is proved that the system can quickly classifythewineage on the basis ofa higher accuracy rate, and can shorten the detection time compared with the fixed flow rate.