Abstract
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.
Publication Date
7-20-2022
First Page
72
Last Page
80
DOI
10.13652/j.spjx.1003.5788.2022.90027
Recommended Citation
Shou-cheng, WANG; Xue-ying, YU; Ji-yong, GAO; and Zhi-qiang, WANG
(2022)
"Age detection of mature vinegar based on electronic tongue and electronic nose combined with DenseNet-ELM,"
Food and Machinery: Vol. 38:
Iss.
4, Article 12.
DOI: 10.13652/j.spjx.1003.5788.2022.90027
Available at:
https://www.ifoodmm.cn/journal/vol38/iss4/12
References
[1] 张磊,王争争,李婷,等.不同陈酿时间山西老陈醋中功能成分的变化分析[J].中国调味品,2015,40(6):43-46.ZHANG Lei,WANG Zheng-zheng,LI Ting,et al.Analysis of functional components during aging process of Shanxi aged vinegar[J].China Condiment,2015,40(6):43-46.
[2] PAOLO G,陈福生,杨浩然,等.中国谷物醋的感官风味特征分析[J].中国酿造,2017,36(9):1-5.PAOLO G,CHEN Fu-sheng,YANG Hao-ran,et al.Analysis of sensory flavor characteristics of Chinese cereal vinegar[J].China Brewing,2017,36(9):1-5.
[3] LASTRA-MEJÍAS M,GONZÁLEZ-FLORES E,IZQUIERDO M,et al.Cognitive chaos on spectrofluorometric data to quantitatively unmask adulterations of a PDO vinegar[J].Food Control,2020,108:106860.
[4] 秦刚,宋海燕,陆辉山.应用可见/近红外光谱技术快速鉴别山西陈醋品种[J].山西农业大学学报(自然科学版),2010,30(4):309-311.QIN Gang,SONG Hai-yan,LU Hui-shan.Discrimination of mature vinegars using vis/near infrared spectra[J].Journal of Shanxi Agricultural University(Natural Science Edition),2010,30(4):309-311.
[5] MOUFID M,HOFMANN M,EL BARI N,et al.Wastewater monitoring by means of e-nose,VE-tongue,TD-GC-MS,and SPME-GC-MS[J].Talanta,2021,221:121450.
[6] YANG Z W,MIAO N,ZHANG X,et al.Employment of an electronic tongue combined with deep learning and transfer learning for discriminating the storage time of Pu-erh tea[J].Food Control,2021,121:107608.
[7] WANG Y,DIAO J W,WANG Z,et al.An optimized deep convolutional neural network for dendrobium classification based on electronic nose[J].Sensors and Actuators A Physical,2020,307(1):111874.
[8] 朱建锡,马晓钟,张津阳,等.信息融合及其与电子鼻和电子舌相关的应用[J].现代食品,2020(11):118-122.ZHU Jian-xi,MA Xiao-zhong,ZHANG Jin-yang,et al.Application of information fusion related to electronic nose and electronic tongue[J].Modern Food,2020(11):118-122.
[9] DI ROSA A R,LEONE F,CHELI F,et al.Fusion of electronic nose,electronic tongue and computer vision for animal source food authentication and quality assessment:A review[J].Journal of Food Engineering,2017,210:62-75.
[10] 杨正伟,张鑫,李庆盛,等.基于电子舌及一维深度CNN-ELM模型的普洱茶贮藏年限快速检测[J].食品与机械,2020,36(8):45-52.YANG Zheng-wei,ZHANG Xin,LI Qing-sheng,et al.A fast detection Pu-erh tea storage based on the voltammertric electronic tongue and one-dimension CNN-ELM[J].Food & Machinery,2020,36(8):45-52.
[11] LODHI B,KANG J.Multipath-DenseNet:A Supervised ensemble architecture of densely connected convolutional networks[J].Information Sciences,2019,482:63-72.
[12] XIONG P,XUE Y P,ZHANG J S,et al.Localization of myocardial infarction with multi-lead ECG based on DenseNet[J].Computer Methods and Programs in Biomedicine,2021,203:106024.
[13] HUANG Z L,LIU C,FEI H B,et al.Urban sound classification based on 2-order dense convolutional network using dual features[J].Applied Acoustics,2020,164:107243.
[14] RÍOS-REINA R,AZCARATE S M,CAMIṄA J M,et al.Sensory and spectroscopic characterization of Argentinean wine and balsamic vinegars:A comparative study with European vinegars[J].Food Chemistry,2020,323:126791.
[15] 马泽亮.基于电子鼻和电子舌的智能感官检测系统设计与应用研究[D].淄博:山东理工大学,2019:8-26.MA Ze-liang.Design and application of intelligent sensory detection system based on electronic nose and electronic tongue[D].Zibo:Shandong University of Technology,2019:8-26.
[16] WANG R H,LI J,CHENCHO,et al.Densely connected convolutional networks for vibration based structural damage identification[J].Engineering Structures,2021,245:112871.
[17] HUANG G B,ZHU Q Y,SIEW C K.Extreme learning machine:Theory and applications[J].Neurocomputing,2006,70(1/2/3):489-501.
[18] MELIT DEVASSY B,GEORGE S.Dimensionality reduction and visualisation of hyperspectral ink data using t-SNE[J].Forensic Science International,2020,311:110194.
[19] 马泽亮,国婷婷,殷廷家,等.基于电子鼻系统的白酒掺假检测方法[J].食品与发酵工业,2019,45(2):190-195.MA Ze-liang,GUO Ting-ting,YIN Ting-jia,et al.Detection of liquor adulteration based on the electronic nose system[J].Food and Fermentation Industries,2019,45(2):190-195.
[20] 史庆瑞,国婷婷,殷廷家,等.基于电子舌检测的橙汁贮藏品质研究[J].食品与机械,2017,33(11):137-142,203.SHI Qing-rui,GUO Ting-ting,YIN Ting-jia,et al.Research on detection for the storage quality of orange juice based on the electronic tongue[J].Food & Machinery,2017,33(11):137-142,203.
[21] YIN T J,GUO T T,MA Z L,et al.Classification of wolfberry with different geographical origins by using voltammetric electronic tongue[J].IFAC-PapersOnLine,2018,51(17):654-659.
[22] 郭洁丽,毛立新,杨小兰.同步荧光法结合偏最小二乘法对老陈醋中掺杂食用冰醋酸的定量分析[J].食品工程,2013(3):48-51.GUO Jie-li,MAO Li-xin,YANG Xiao-lan.Synchronous fluorescence spectrometry combined with partial least squares method for quantitative analysis of edible acetic acid in adulterated Shanxi mature vinegar[J].Food Engineering,2013(3):48-51.
[23] 安政光,陆辉山.基于近红外光谱的老陈醋不同品牌的鉴别分析[J].机械管理开发,2012(3):26-27.AN Zheng-guang,LU Hui-shan.Discrimination between mature vinegars of different brands by NIRS[J].Mechanical Management and Development,2012(3):26-27.