Abstract
Electronic nose composed of MOS sensors cannot be used when there are unwanted gases; therefore, it is urgent to solve the problem of interferences elimination. The presented method in Chap. 15 tends to discriminate the interference gases and target gases and depends on the number of types of interference gases. However, there are numerous interferences in real-world application scenario, which is impossible to be sampled in laboratory experiments. Considering that the target gases rather than interferences can be fixed as invariant information, a novel and effective pattern mismatch-based interference elimination (PMIE) method is proposed in this chapter. It contains two parts: discrimination (i.e., pattern mismatch) and correction (i.e., interference elimination). Specifically, the principle behind is that the interference discrimination is achieved by deciding whether a new pattern violates the rules established on the invariant target gases information (i.e., interference gas) or not (i.e., target gas). If the current pattern of the sensor array is interference, orthogonal signal correction (OSC) algorithm is used for interference correction. Experimental results prove that the proposed PMIE method is very effective for interferences elimination in electronic nose.
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S.M. Scott, D. James, Z. Ali, Data analysis for electronic nose systems. Microchim. Acta 156, 183–207 (2007)
L. Zhang, F. Tian, Performance study of multilayer perceptrons in a low-cost electronic nose. IEEE Trans. Instrum. Meas. 63, 1670–1679 (2014)
X. Tian, Y. Yin, H. Liu, Research on artificial olfactory sensor technology for liquor identification. Food Sci. 2, 29–32 (2004)
B. Mumyakmaz, A. Özmen, M.A. Ebeoğlu, C. Taşaltın, İ. Gürol, A study on the development of a compensation method for humidity effect in QCM sensor responses. Sens. Actuators, B 1, 277–282 (2010)
K.R. Kashwan, M. Bhuyan, in Robust electronic-nose system with temperature and humidity drift compensation for tea and spice flavour discrimination, Sensors and the International Conference on new Techniques in Pharmaceutical and Biomedical Research, Asian Conference on, 2005-07-20 (2005)
J.W. Gardner, E.L. Hines, F. Molinier, P.N. Bartlett, T.T. Mottram, Prediction of health of dairy cattle from breath samples using neural network with parametric model of dynamic response of array of semiconducting gas sensors, Science, Measurement and Technology, IEEE Proceedings—2, 102–106 (1999)
X. Xiao-Liang, Q. Jun-Na and C. Chun, A study on local sensor fusion of wireless sensor networks based on the neural network, Machine Learning and Cybernetics, International Conference on, Kunming, 2008-01-01 (2008)
S. Jianfang, T. Hongbiao, G. Haiyan, Application of wavelet neural network and multi-sensor data fusion technique in intelligent sensor, Intelligent Control and Automation. WCICA 2008. 7th World Congress on, Chongqing, 2008-01-01 (2008)
T.A. Emadi, C. Shafai, M.S. Freund, D.J. Thomson, D.S. Jayas, N.D.G. White, Development of a polymer-based gas sensor—humidity and CO2 sensitivity, Microsystems and Nanoelectronics Research Conference. MNRC 2009. 2nd, Ottawa, ON, Canada, 2009-01-01 (2009)
C. Di Natale, E. Martinelli, A. D’Amico, Counteraction of environmental disturbances of electronic nose data by independent component analysis. Sens. Actuators, B. 82(2–3), 158–165 (2002)
L. Zhang, F. Tian, L. Dang, G. Li, X. Peng, X. Yin, S. Liu, A novel background interferences elimination method in electronic nose using pattern recognition. Sens. Actuators, A 201, 254–263 (2013)
J. Feng, F. Tian, J. Yan, Q. He, Y. Shen, L. Pan, A background elimination method based on wavelet transform in wound infection detection by electronic nose. Sens. Actuators, B 2, 395–400 (2011)
F. Tian, J. Yan, S. Xu, J. Feng, Q. He, Y. Shen, P. Jia, Background interference elimination in wound infection detection by electronic nose based on reference vector-based independent component analysis. Inf. Technol. J. 7 (2012)
N.G. Yee, G.G. Coghill, Factor selection strategies for orthogonal signal correction applied to calibration of near-infrared spectra. Chemometr. Intell. Lab. Syst. 67, 145–156 (2003)
J. Feng, F. Tian, P. Jia, Q. He, Y. Shen, S. Fan, Improving the performance of electronic nose for wound infection detection using orthogonal signal correction and particle swarm optimization. Sens. Rev. 34, 389–395 (2014)
X. Zhang, X. Li, Y. Feng, Z. Liu, The use of ROC and AUC in the validation of objective image fusion evaluation metrics. Sig. Process. 115, 38–48 (2015)
V. Nykänen, I. Lahti, T. Niiranen, K. Korhonen, Receiver operating characteristics (ROC) as validation tool for prospectivity models—A magmatic Ni–Cu case study from the Central Lapland Greenstone Belt. Northern Finl. Ore Geol. Rev. 71, 853–860 (2015)
M. Thomas, K. De Brabanter, J.A.K. Suykens, B. De Moor, Predicting breast cancer using an expression values weighted clinical classifier. BMC Bioinform. 15 (2014)
S. Wold, H. Antti, F. Lindgren, J. Öhman, Orthogonal signal correction of near-infrared spectra. Chemometr. Intell. Lab. Syst. 44, 175–185 (1998)
Z. Talebpour, R. Tavallaie, S.H. Ahmadi, A. Abdollahpour, Simultaneous determination of penicillin G salts by infrared spectroscopy: evaluation of combining orthogonal signal correction with radial basis function-partial least squares regression. Spectrochim. Acta Part A Mol. Biomol. Spectrosc. 76, 452–457 (2010)
L. Laghi, A. Versari, G.P. Parpinello, D.Y. Nakaji, R.B. Boulton, FTIR spectroscopy and direct orthogonal signal correction preprocessing applied to selected phenolic compounds in red wines. Food Anal. Methods 4, 619–625 (2011)
D.J. Bouveresse, A. Moya-González, F. Ammari, D.N. Rutledge, Two novel methods for the determination of the number of components in independent components analysis models. Chemometr. Intell. Lab. Syst. 112, 24–32 (2012)
S. Balasubramanian, S. Panigrahi, C.M. Logue, C. Doetkott, M. Marchello, J.S. Sherwood, Independent component analysis-processed electronic nose data for predicting Salmonella typhimurium populations in contaminated beef. Food Control 19, 236–246 (2008)
T. Aguilera, J. Lozano, J.A. Paredes, F.J. Alvarez, J.I. Suarez, Electronic nose based on independent component analysis combined with partial least squares and artificial neural networks for wine prediction. Sens. Basel 6, 8055–8072 (2012)
M. Padilla, A. Perera, I. Montoliu, A. Chaudry, K. Persaud, S. Marco, Drift compensation of gas sensor array data by orthogonal signal correction. Chemometr. Intell. Lab. Syst. 100, 28–35 (2010)
L. Zhang, F. Tian, S. Liu, L. Dang, X. Peng, X. Yin, Chaotic time series prediction of E-nose sensor drift in embedded phase space. Sens. Actuators, B 182, 71–79 (2013)
M. Holmberg, F.A.M. Davide, C. Di Natale, A. D’Amico, F. Winquist, I. Lundström, Drift counteraction in odour recognition applications: lifelong calibration method. Sens. Actuators, B 42, 185–194 (1997)
L. Zhang, D. Zhang, Domain adaptation extreme learning machines for drift compensation in E-nose systems. IEEE Trans. Instrum. Meas. 64, 1790–1801 (2015)
L. Zhang, F.C. Tian, C. Kadri, B. Xiao, H. Li, L. Pan, H. Zhou, On-line sensor calibration transfer among electronic nose instruments for monitor volatile organic chemical in indoor air quality. Sens. Actuators, B 160, 899–909 (2011)
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Zhang, L., Tian, F., Zhang, D. (2018). Pattern Mismatch Guided Interference Elimination. In: Electronic Nose: Algorithmic Challenges. Springer, Singapore. https://doi.org/10.1007/978-981-13-2167-2_16
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DOI: https://doi.org/10.1007/978-981-13-2167-2_16
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