Case Study on the Identification of a Direction-Dependent Electronic Nose System

  • Ai Hui TanEmail author
  • Keith Richard Godfrey
Part of the Advances in Industrial Control book series (AIC)


The identification of an electronic nose system having direction-dependent properties, where the dynamics depend on whether the output is increasing or decreasing, is described. The system and experimental setup are explained and results from step response tests are presented where the difference in dynamics can be observed. It is shown that the use of maximum length binary signals allows the detection of the nonlinearities through the input–output crosscorrelation function due to the shift-and-multiply property. When inverse-repeat maximum length binary signals are applied, the effects of even-order nonlinearities can be eliminated. The detection of the even-order nonlinearities is also possible through analysis of the output spectrum. The best linear approximation of the system is estimated using various methods in the time and frequency domains. Finally, it is shown that the identification tests lead to the estimation of a Wiener model for the system. The application of the inverse-repeat signal is advantageous here, as the odd-order and even-order components can be optimised separately resulting in higher accuracy in the parameter estimates.


  1. Barker HA, Obidegwu SN (1973) Effects of nonlinearities on the measurement of weighting functions by crosscorrelation using pseudorandom signals. IEE Proc Control and Science 120:1293–1300CrossRefGoogle Scholar
  2. Barker HA, Tan AH, Godfrey KR (2003) Wiener models of direction-dependent dynamic systems. Automatica 39:127–133MathSciNetCrossRefGoogle Scholar
  3. Dragonieri S, Quaranta VN, Carratu P, Ranieri T, Marra L, D’Alba G, Resta O (2016) An electronic nose may sniff out amyotrophic lateral sclerosis. Respir Physiol Neurobiol 232:22–25CrossRefGoogle Scholar
  4. Gardner JW, Bartlett PN (1999) Electronic noses—principles and applications. Oxford University Press, Oxford, UKGoogle Scholar
  5. Gardner JW, Vincent TA (2016) Electronic noses for well-being: breath analysis and energy expenditure. Sensors 16:947CrossRefGoogle Scholar
  6. Guo W, Gan F, Kong H, Wu J (2015) Signal model of electronic noses with metal oxide semiconductor. Chemometr Intell Lab Syst 143:130–135CrossRefGoogle Scholar
  7. Kollár I (1994) Frequency domain system identification toolbox for use with MATLAB. The MathWorks Inc., Natick, MAGoogle Scholar
  8. Qiu S, Wang J (2017) The prediction of food additives in the fruit juice based on electronic nose with chemometrics. Food Chem 230:208–214CrossRefGoogle Scholar
  9. Romero-Flores A, McConnell LL, Hapeman CJ, Ramirez M, Torrents A (2017) Evaluation of an electronic nose for odorant and process monitoring of alkaline-stabilized biosolids production. Chemosphere 186:151–159CrossRefGoogle Scholar
  10. Rosenqvist F, Tan AH, Godfrey KR, Karlström A (2006) Direction-dependent system modeling approaches exemplified through an electronic nose system. IEEE Trans Control Syst Technol 14:526–531CrossRefGoogle Scholar
  11. Tan AH (2009) Direction-dependent systems—a survey. Automatica 45:2729–2743MathSciNetCrossRefGoogle Scholar
  12. Tan AH, Godfrey KR (2001) Identification of processes with direction-dependent dynamics. IEE Proc Control Theory Appl 148:362–369CrossRefGoogle Scholar
  13. Tan AH, Godfrey KR (2004) Modeling of direction-dependent processes using Wiener models and neural networks with nonlinear output error structure. IEEE Trans Instrum Meas 53:744–753CrossRefGoogle Scholar
  14. Wei Z, Wang J, Zhang W (2015) Detecting internal quality of peanuts during storage using electronic nose responses combined with physicochemical methods. Food Chem 177:89–96CrossRefGoogle Scholar
  15. Wojnowski W, Majchrzak T, Dymerski T, Gębicki J, Namieśnik J (2017) Electronic noses: powerful tools in meat quality assessment. Meat Sci 131:119–131CrossRefGoogle Scholar
  16. Zhao H-T, Pang K-Y, Lin W-L, Wang Z-J, Gao D-Q, Guo M-J, Zhuang Y-P (2016) Optimization of the n-propanol concentration and feedback control strategy with electronic nose in erythromycin fermentation processes. Process Biochem 51:195–203CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of EngineeringMultimedia UniversityCyberjayaMalaysia
  2. 2.School of EngineeringUniversity of WarwickCoventryUK

Personalised recommendations