基于电子舌和电子鼻结合DenseNet-ELM的陈醋年限检测

目的：实现陈醋酿造年限的快速检测。方法：采用电子舌(ET)和电子鼻(EN)结合密集卷积网络—极限学习机(DenseNet-ELM)模型对陈醋酿造年限进行快速检测，设计两种不同结构的密集卷积神经网络模型ET-DenseNet和EN-DenseNet，分别提取电子舌和电子鼻信号的特征信息，进而采用特征级信息融合方法，获得两种人工感官设备的融合特征向量，然后采用极限学习机(ELM)对融合的特征向量进行分类识别。结果：DenseNet能够有效提取到电子舌和电子鼻信号中深层特征，其特征提取能力优于离散小波变换(DWT)和卷积神经网络(CNN)；相比于单独使用电子舌或者电子鼻，信息融合方法对不同年限陈醋检测的准确性和鲁棒性更优，其测试集准确率、查准率、召回率、F1-Score分别达到99.1%，0.98，0.99和0.99。结论：采用密集卷积网络缓解了深度学习模型由于深度增加导致的模型退化、泛化能力弱等问题，可对7种不同酿造年限的陈醋进行有效分类。

Age detection of mature vinegar based on electronic tongue and electronic nose combined with DenseNet-ELM

Objective:In order to realize the rapid detection of the brewing age of mature vinegar.Methods:Electronic tongue (ET) and electronic nose (EN) combined with Densely Connected Convolutional Networks-Extreme Learning Machine (DenseNet-ELM) model were used to quickly detect the brewing age of mature vinegar. Two DenseNet models with different structures, ET-DenseNet and EN-DenseNet, were designed to extract the feature information of the electronic tongue and electronic nose signals respectively. And then the feature level information fusion method was used to obtain the fusion feature vectors of the two artificial sensory devices. Then Extreme Learning Machine (ELM) was used to classify and recognize the fused feature vectors.Results:DenseNet can effectively extract the deep features of electronic tongue and electronic nose signals, and its feature extraction ability was better than Discrete Wavelet Transform (DWT) and Convolutional Neural Network (CNN); Compared with the use of electronic tongue or electronic nose alone, the information fusion method had better accuracy and robustness for the detection of mature vinegar of different years. The Accuracy, Precision, Recall and F1-score of the test set reach 99.1%, 0.98, 0.99 and 0.99, respectively.Conclusion:The dense convolution network can alleviate the problems of model degradation and weak generalization ability caused by the increase of depth of the deep learning model, and can effectively classify seven kinds of aged vinegar with different brewing years.