Suppression of Strong Background Interference on E-Nose Sensors in an Open Country Environment

The feature extraction technique for an electronic nose (e-nose) applied in tobacco smell detection in an open country/outdoor environment with periodic background strong interference is studied in this paper. Principal component analysis (PCA), Independent component analysis (ICA), re-filtering and a priori knowledge are combined to separate and suppress background interference on the e-nose. By the coefficient of multiple correlation (CMC), it can be verified that a better separation of environmental temperature, humidity, and atmospheric pressure variation related background interference factors can be obtained with ICA. By re-filtering according to the on-site interference characteristics a composite smell curve was obtained which is more related to true smell information based on the tobacco curer’s experience.

[1]  A. Taroni,et al.  Development and application of mass sensors based on flexural resonances in alumina beams , 1996, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[2]  Xue Li,et al.  An Effective Approach to Handling Noise and Drift in Electronic Noses , 2014, ADC.

[3]  K. R. Kashwan,et al.  Robust electronic-nose system with temperature and humidity drift compensation for tea and spice flavour discrimination , 2005, 2005 Asian Conference on Sensors and the International Conference on New Techniques in Pharmaceutical and Biomedical Research.

[4]  Pengfei Jia,et al.  Improving the performance of electronic nose for wound infection detection using orthogonal signal correction and particle swarm optimization , 2014 .

[5]  V. Barnett,et al.  Applied Linear Statistical Models , 1975 .

[6]  Jianfang Shi,et al.  Application of wavelet neural network and multi-sensor data fusion technique in intelligent sensor , 2008, 2008 7th World Congress on Intelligent Control and Automation.

[7]  Zvezditza Nenova,et al.  Compensation of the Impact of Disturbing Factors on Gas Sensor Characteristics , 2013 .

[8]  R. Bandyopadhyay,et al.  Monitoring of black tea fermentation process using electronic nose , 2007 .

[9]  Noel D.G. White,et al.  Development of a polymer-based gas sensor - humidity and CO2 sensitivity , 2009 .

[10]  S. D. Vito,et al.  CO, NO2 and NOx urban pollution monitoring with on-field calibrated electronic nose by automatic bayesian regularization , 2009 .

[11]  Chun Chen,et al.  A study on local sensor fusion of wireless sensor networks based on the neural network , 2008, 2008 International Conference on Machine Learning and Cybernetics.

[12]  Cihat Tasaltin,et al.  A study on the development of a compensation method for humidity effect in QCM sensor responses , 2010 .

[13]  T Chau,et al.  A review of analytical techniques for gait data. Part 2: neural network and wavelet methods. , 2001, Gait & posture.

[14]  A. Cappello,et al.  A new formulation of the coefficient of multiple correlation to assess the similarity of waveforms measured synchronously by different motion analysis protocols. , 2010, Gait & posture.

[15]  Eugenio Martinelli,et al.  Counteraction of environmental disturbances of electronic nose data by independent component analysis , 2002 .

[16]  Lina Pan,et al.  A background elimination method based on wavelet transform in wound infection detection by electronic nose , 2011 .

[17]  Tian Xian.  Research on Artificial Olfactory Sensor Technology for Liquor Identification , 2004 .

[18]  Evor L. Hines,et al.  Prediction of health of dairy cattle from breath samples using neural network with parametric model of dynamic response of array of semiconducting gas sensors , 1999 .

[19]  Lei Zhang,et al.  A novel background interferences elimination method in electronic nose using pattern recognition , 2013 .

[20]  Gareth Irwin,et al.  Elbow joint variability for different hand positions of the round off in gymnastics. , 2015, Human movement science.

[21]  H. S. Wolff,et al.  iRun: Horizontal and Vertical Shape of a Region-Based Graph Compression , 2022, Sensors.

[22]  Łukasz Lentka,et al.  DETERMINATION OF GAS MIXTURE COMPONENTS USING FLUCTUATION ENHANCED SENSING AND THE LS-SVM REGRESSION ALGORITHM , 2015 .

[23]  Pengfei Jia,et al.  Background Interference Elimination in Wound Infection Detection by Electronic Nose Based on Reference Vector-based Independent Component Analysis , 2012 .