Selection Of Optimum number Of Sensors Of An Electronic Tongue For Efficient Classification Of Black Tea: A Combinatorial Approach Based On Discrete Cosine Transform and Artificial Neural Network

被引：0
|
作者：
Acharya, Srikanta [1 ]
Chatterjee, Trisita Nandy [2 ]
Mukherjee, Soumen [3 ]
Das, Debangana [2 ]
Roy, Runu Banerjee [2 ]
Tudu, Bipan [2 ]
Bandyopadhyay, Rajib [2 ,4 ]
机构：
[1] RCC Inst Informat Technol, Dept Elect & Commun Engn, Kolkata, India
[2] Jadavpur Univ, Dept Instrumentat & Elect Engn, Kolkata, India
[3] RCC Inst Informat Technol, Dept Comp Applicat, Kolkata, India
[4] ITMO Univ, Lab Artificial Sensory Syst, St Petersburg, Russia
来源：
2018 FOURTH IEEE INTERNATIONAL CONFERENCE ON RESEARCH IN COMPUTATIONAL INTELLIGENCE AND COMMUNICATION NETWORKS (ICRCICN) | 2018年
关键词：
Electronic tongue (ET); Discrete cosine transform (DCT); Artificial Neural Network (ANN); Multi-Layer Perceptron (MLP); Sensor; Figure of merit (FOM);
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In order to develop an electronic tongue for black tea classification, nine different electrodes have been developed using polymer-graphite composites. Among these nine sensors, a set of five electrodes have been chosen to form an array by analyzing the response which is taken by nuance of Cyclic voltammetry (CV) using a three electrode system. The data set so obtained from CV where subjected to discrete cosine transform (DCT) based feature extraction technique followed by the artificial neural network (ANN) based classification. Best five sensors have been selected keeping threshold value of classification accuracy rate to 90%.
引用
收藏
页码：108 / 111
页数：4
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