A sensor array optimization method for electronic noses with sub-arrays

被引:29
作者
Zhang, Shunping [2 ]
Xie, Changsheng [1 ,2 ]
Zeng, Dawen [1 ]
Li, Huayao [2 ]
Liu, Yuan [2 ]
Cai, Shuizhou [1 ]
机构
[1] Huazhong Univ Sci & Technol, Nanomat & Smart Sensor Res Lab, Dept Mat Sci & Engn, Wuhan 430074, Peoples R China
[2] Huazhong Univ Sci & Technol, State Key Lab Mat Proc & Die & Mould Technol, Wuhan 430074, Peoples R China
关键词
Sensor array optimization; Sub-array; Electronic nose; Selectivity similarity; VARIABLE SELECTION; GENETIC ALGORITHM; GAS; PERFORMANCE;
D O I
10.1016/j.snb.2009.08.015
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Sensor array configuration needs to be optimized in developing an application-specific instrument of electronic noses. Currently, sensor array optimization is commonly done by feature selection techniques. These methods could solve how to optimize a sensor array. However, they could not figure out what are the unique functions that each sensor plays in the optimized sensor array. The method proposed in this paper could solve this problem by sensor clustering and dividing the whole sample classification mission into several small recognition tasks. A measurement with a six Taguchi Gas Sensors (TGS sensor hereinafter) sensors array to classify 11 gas sorts was used in the data validation. The sensor array was optimized to three sensors with the proposed method. Each sensor in the optimized array had unique functions to solve different recognition tasks. TGS2600 had the unique functions to discriminate butanone and acetaldehyde. TGS2602 had the unique functions to discriminate benzene and cyclohexane, methanol and ethanol. TGS813 had the unique functions to discriminate cyclohexane and pentane. The combination of TGS2600 and TGS2602 had the unique functions to discriminate acetone and butanone, acetone and acetaldehyde. The proposed method might be a new generation of sensor array optimization methods. (C) 2009 Elsevier B. V. All rights reserved.
引用
收藏
页码:243 / 252
页数:10
相关论文
共 14 条
[1]   A chemometric approach based on a novel similarity/diversity measure for the characterisation and selection of electronic nose sensors [J].
Ballabio, Davide ;
Cosio, Maria Stella ;
Mannino, Saverio ;
Todeschini, Roberto .
ANALYTICA CHIMICA ACTA, 2006, 578 (02) :170-177
[2]   A method for selecting an optimum sensor array [J].
Chaudry, AN ;
Hawkins, TM ;
Travers, PJ .
SENSORS AND ACTUATORS B-CHEMICAL, 2000, 69 (03) :236-242
[3]   Enhancing electronic nose performance by sensor selection using a new integer-based genetic algorithm approach [J].
Gardner, JW ;
Boilot, P ;
Hines, EL .
SENSORS AND ACTUATORS B-CHEMICAL, 2005, 106 (01) :114-121
[4]   Coupling fast variable selection methods to neural network-based classifiers:: Application to multisensor systems [J].
Gualdrón, O ;
Llobet, E ;
Brezmes, J ;
Vilanova, X ;
Correig, X .
SENSORS AND ACTUATORS B-CHEMICAL, 2006, 114 (01) :522-529
[5]  
GUDLRON O, 2007, SENSOR ACTUAT B-CHEM, V122, P259
[6]   In situ and operando spectroscopy for assessing mechanisms of gas sensing [J].
Gurlo, Alexander ;
Riedel, Ralf .
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION, 2007, 46 (21) :3826-3848
[7]   Building parsimonious fuzzy ARTMAP models by variable selection with a cascaded genetic algorithm:: application to multisensor systems for gas analysis [J].
Llobet, E ;
Brezmes, J ;
Gualdrón, O ;
Vilanova, X ;
Correig, X .
SENSORS AND ACTUATORS B-CHEMICAL, 2004, 99 (2-3) :267-272
[8]   Comparing the performance of different features in sensor arrays [J].
Pardo, M. ;
Sberveglieri, G. .
SENSORS AND ACTUATORS B-CHEMICAL, 2007, 123 (01) :437-443
[9]  
Phaisangittisagul E., 2010, SENSOR ACTUAT B-CHEM, V145, P507
[10]   A combinatorial technique for the search of solid state gas sensor materials [J].
Scheidtmann, J ;
Frantzen, A ;
Frenzer, G ;
Maier, WF .
MEASUREMENT SCIENCE AND TECHNOLOGY, 2005, 16 (01) :119-127