Mining Data of Noisy Signal Patterns in Recognition of Gasoline Bio-Based Additives using Electronic Nose

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The paper analyses the distorted data of an electronic nose in recognizing the gasoline bio-based additives. Different tools of data mining, such as the methods of data clustering, principal component analysis, wavelet transformation, support vector machine and random forest of decision trees are applied. A special stress is put on the robustness of signal processing systems to the noise distorting the registered sensor signals. A special denoising procedure based on application of discrete wavelet transformation has been proposed. This procedure enables to reduce the error rate of recognition in a significant way. The numerical results of experiments devoted to the recognition of different blends of gasoline have shown the superiority of support vector machine in a noisy environment of measurement.


  • [1] McCarrick, C.W., Ohmer, D.T., Gillil L.A., Edwards, P.A. (1996). Fuel identification by neural network analysis of the response of vapour-sensitive sensor arrays. Analytical Chemistry, 68, 4264−4269.

  • [2] Brudzewski, K., Osowski, S., et al.(2006). Classification of gasoline with supplement of bio-products by means of an electronic nose SVM neural network. Sensors and Actuators B, 113, 135−141.

  • [3] Di Natale, C., Martinelli, E., D’amico, A. (2005). Pre-processing and pattern recognition methods for artificial olfaction systems: a review. Metrol. Meas. Syst., 12(1), 3–26.

  • [4] Bielecki, Z., Janucki, J., et al. (2012). Sensors and systems for the detection of explosive devices – an overview. Metrol. Meas. Syst., 19(1), 3–28.

  • [5] Boeker, P. (2014). On ‘Electronic Nose’ methodology. Sensors and Actuators B, 204, 2–17.

  • [6] Jha, S.K., Yadava, R.D. (2011). Denoising by singular value decomposition its application to electronic nose data processing. IEEE Sensors Journal, 11, 1, 35–44.

  • [7] Hassanpour, H. (2008). A time-frequency approach for noise reduction. Digital Signal Processing, 18, 728–738.

  • [8] Fonollosa, J., Fernández, L., et al. (2016). Calibration transfer and drift counteraction in chemical sensor arrays using direct standardization. Sensors and Actuators B, 236, 1044−1053.

  • [9] Zuppa, M., Distante, C., Siciliano, P., Persaud, K.C. (2004). Drift counteraction with multiple self-organizing maps for an electronic nose. Sensors and Actuators B, 98, 305–317.

  • [10] Kalinowski, P., Jasiński, G., Jasiński, P. (2014). Stabilność odpowiedzi półprzewodnikowych czujników gazu w zmiennych warunkach środowiskowych: badania długoterminowe oraz korekcja dryftu. Elektronika: konstrukcje, technologie, zastosowania, 55(9), 119−121.

  • [11] Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32.

  • [12] Schölkopf, B., Smola, A. (2002). Learning with kernels. Cambridge MA: MIT Press.

  • [13] Wiziack, N.K.L., Catini, A., Santonico, M., D’Amico, A., Paolesse, R., Paterno, L.G., Fonseca, F.J., Di Natale, C. (2009). A sensor array based on mass capacitance transducers for the detection of adulterated gasolines. Sensors and Actuators B, 140, 508−513.

  • [14] Osowski, S., Tran Hoai, L., Brudzewski, K. (2004). Neuro-fuzzy TSK network for calibration of semiconductor sensor array for gas measurements. IEEE Trans. on Measurements Instrumentation, 53, 330−637.

  • [15] Guney, S., Atasoy, A. (2012). Multiclass classification of n-butanol concentrations with k-nearest neighbor algorithm support vector machine in an electronic nose. Sensors Actuators B, 166–167, 721–725.

  • [16] Botre, B.A., Gharpure, D.C., Shaligram, A.D. (2010). Embedded electronic nose supporting software tool for its parameter optimization. Sensors and Actuators B, 146, 453–459.

  • [17] Pardo, M., Sberveglieri, G. (2005). Classification of electronic nose data with support vector machines. Sensors and Actuators B, 107, 730–737.

  • [18] Liu, M., Wang, M., Wang, J., Li, D. (2013). Comparison of random forest, support vector machine back propagation neural network for electronic tongue data classification: Application to the recognition of orange beverage Chinese vinegar. Sensors and Actuators B, 177, 970−980.

  • [19] Matlab user manual (2014). Natick, USA: MathWorks.

  • [20] Tan, P.N., Steinbach, M., Kumar, V. (2006). Introduction to data mining. Boston: Pearson Education Inc.

  • [21] Daubechies, I. (1992). Ten lectures on wavelets. SIAM, Philadelphia.

  • [22] Mallat, S. (1989). A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11, 674−692.

  • [23] Murguía, J.S., Vergara, A., Vargas-Olmos, C., Wong, T.J., Fonollosa, J., Huerta, R. (2013). Two-dimensional wavelet transform feature extraction for porous silicon chemical sensors. Analytica Chimica Acta, 785, 1−15

Metrology and Measurement Systems

The Journal of Committee on Metrology and Scientific Instrumentation of Polish Academy of Sciences

Journal Information

IMPACT FACTOR 2016: 1.598

CiteScore 2016: 1.58

SCImago Journal Rank (SJR) 2016: 0.460
Source Normalized Impact per Paper (SNIP) 2016: 1.228


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