Sensor Array Optimization for Mobile Electronic Nose: Wavelet Transform and Filter Based Feature Selection Approach

Dedy Rahman Wijaya; Riyanarto Sarno; Enny Zulaika

doi:10.15866/irecos.v11i8.9425

Sensor Array Optimization for Mobile Electronic Nose: Wavelet Transform and Filter Based Feature Selection Approach

Dedy Rahman Wijaya^(1*), Riyanarto Sarno⁽²⁾, Enny Zulaika⁽³⁾

^(*) Corresponding author

Authors' affiliations

DOI: https://doi.org/10.15866/irecos.v11i8.9425

Abstract

Mobile Electronic Nose (MoLen) is a prospective concept for Sensing as a Service (S2aaS) development. Furthermore, gas sensor array is a substantial part of MoLen. This work treats about two issues related with gas sensor array. First, commonly used resistive sensor e.g. MOS (Metal-Oxide Semiconductor) gas sensor is highly contaminated with noise. Second, a poor combination of sensor array leads to features redundancy issue. These problems cause significant performance degradation on classifier. It will get worse if the classifier runs on S2aaS environment. To deal with these issues, this study proposes the robust sensor array optimization method based on Wavelet Transform to handle the noise and the modified Fast Correlation-Based Filter (FCBF) to find the best combination of sensor array with minimizing feature redundancy. This study has the following contribution: i) reducing the noise from gas sensor array that generated irrelevant data; ii) finding the best sensor array for beef quality classification to improve the quality of input to classifier. The experimental results show that the proposed method successfully reduces the noise power at maximum 14.41% and it is able to determine the best combination of sensors in sensor array with the 16% of improvement of General Resolution Factor (GRF)that is associated with larger classification rate.
Copyright © 2016 Praise Worthy Prize - All rights reserved.

Keywords

Sensor Array Optimization; Mobile Electronic Nose (MoLen); Feature Selection; Wavelet Transform; Fast Correlation-Based Filter (FCBF)

Full Text:

PDF

References

S.A. Abdallah, L.A. Al-shatti, A.F. Alhajraf, N. Al-hammad, B. Al-awadi, The detection of foodborne bacteria on beef : the application of the electronic nose, pp.1–9, 2013.
http://dx.doi.org/10.1186/2193-1801-2-687

S. Balasubramanian, C.M. Logue, M. Marchello, Spoilage Identification of Beef Using an Electronic Nose System, Transactions of the ASAE. Vol. 47, pp.1625–1633, 2004.
http://dx.doi.org/10.13031/2013.17593

S. Balasubramanian, S. Panigrahi, C.M. Logue, H. Gu, M. Marchello, Neural networks-integrated metal oxide-based artificial olfactory system for meat spoilage identification, Journal of Food Engineering. Vol. 91, pp.91–98, 2009.
http://dx.doi.org/10.1016/j.jfoodeng.2008.08.008

N. El Barbri, E. Llobet, N. El Bari, X. Correig, B. Bouchikhi, Electronic Nose Based on Metal Oxide Semiconductor Sensors as an Alternative Technique for the Spoilage Classification of Red Meat, Sensors. Vol. 8, pp.142–156, 2008.
http://dx.doi.org/10.3390/s8010142

M. Ghasemi-Varnamkhasti, S.S. Mohtasebi, M. Siadat, S. Balasubramanian, Meat Quality Assessment by Electronic Nose (Machine Olfaction Technology), Sensors. Vol. 9, pp.6058–6083, 2009.
http://dx.doi.org/10.3390/s90806058

V.Y. Musatov, V.V. Sysoev, M. Sommer, I. Kiselev, Assessment of meat freshness with metal oxide sensor microarray electronic nose: A practical approach, Sensors and Actuators B: Chemical. Vol. 144, pp.99–103, 2010.
http://dx.doi.org/10.1016/j.snb.2009.10.040

O.S. Papadopoulou, E.Z. Panagou, F.R. Mohareb, G.J.E. Nychas, Sensory and microbiological quality assessment of beef fillets using a portable electronic nose in tandem with support vector machine analysis, Food Research International. Vol. 50, pp.241–249, 2013.
http://dx.doi.org/10.1016/j.foodres.2012.10.020

Z. Ali, W.T.O. Hare, B.J. Theaker, Detection of Bacterial Contaminated Milk By Means of a Quartz Crystal Microbalance Based Electronic Nose, Journal Of Thermal Analysis. Vol. 71, pp.155–161, 2003.

S. Ampuero, T. Zesiger, V. Gustafsson, A. Lunden, J.O. Bosset, Determination of trimethylamine in milk using an MS based electronic Nose, European Food Research Technology. Vol. 214, pp.163–167, 2002.
http://dx.doi.org/10.1007/s00217-001-0463-0

A.K. Bag, B. Tudu, Rough Set Based Classification on Electronic Nose Data for Black Tea Application, pp.23–31, n.d.
http://dx.doi.org/10.1007/978-3-642-31600-5_3

S. Borah, E.L. Hines, M.S. Leeson, D.D. Iliescu, M. Bhuyan, J.W. Gardner, Neural network based electronic nose for classification of tea aroma, Sensing and Instrumentation for Food Quality and Safety. Vol. 2, pp.7–14, 2008.
http://dx.doi.org/10.1007/s11694-007-9028-7

P. Puligundla, J. Jung, S. Ko, Carbon dioxide sensors for intelligent food packaging applications, Food Control. Vol. 25, pp.328–333, 2012.
http://dx.doi.org/10.1016/j.foodcont.2011.10.043

M. Falasconi, I. Concina, E. Gobbi, V. Sberveglieri, A. Pulvirenti, G. Sberveglieri, Electronic Nose for Microbiological Quality Control of Food Products, Vol. 2012, 2012.
http://dx.doi.org/10.1155/2012/715763

T.A. McMeekin, J.T. Patterson, Characterization of hydrogen sulfide-producing bacteria isolated from meat and poultry plants, Applied Microbiology. Vol. 29, pp.165–169, 1975. http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1167774.

Najam ul Hasan, N. Ejaz, W. Ejaz, H.S. Kim, Meat and fish freshness inspection system based on odor sensing, Sensors (Switzerland). Vol. 12, pp.15542–15557, 2012.
http://dx.doi.org/10.3390/s121115542

M. Rambla-Alegre, B. Tienpont, K. Mitsui, E. Masugi, Y. Yoshimura, H. Nagata, et al., Coupling gas chromatography and electronic nose detection for detailed cigarette smoke aroma characterization., Journal of Chromatography. A. Vol. 1365, pp.191–203, 2014.
http://dx.doi.org/10.1016/j.chroma.2014.09.015

F. Tian, S.X. Yang, K. Dong, Circuit and Noise Analysis of Odorant Gas Sensors in an E-Nose, Sensors. Vol. 5, pp.85–96, 2005.
http://dx.doi.org/10.3390/s5010085

D.R. Wijaya, R. Sarno, Mobile Electronic Nose Architecture for Beef Quality Detection Based on Internet of Things Technology, in: Bandung, 2015: pp. 655–663. http://www.globalilluminators.org/wp-content/uploads/2015/05/GTAR-15-331.pdf.

Z. Xu, S. Lu, Multi-objective optimization of sensor array using genetic algorithm, Sensors and Actuators, B: Chemical. Vol. 160, pp.278–286, 2011.
http://dx.doi.org/10.1016/j.snb.2011.07.048

Z. Xu, X. Shi, S. Lu, Integrated sensor array optimization with statistical evaluation, Sensors and Actuators, B: Chemical. Vol. 149, pp.239–244, 2010.
http://dx.doi.org/10.1016/j.snb.2010.05.038

A. Kumar bag, B. Tudu, J. Roy, N. Bhattacharyya, R. Bandyopadhyay, Optimization of Sensor Array in Electronic Nose :, Vol. 11, pp.3001–3008, 2011.
http://dx.doi.org/10.1109/jsen.2011.2151186

P.M. Szecówka, a. Szczurek, B.W. Licznerski, On reliability of neural network sensitivity analysis applied for sensor array optimization, Sensors and Actuators B: Chemical. Vol. 157, pp.298–303, 2011.
http://dx.doi.org/10.1016/j.snb.2011.03.066

P. Saha, S. Ghorai, B. Tudu, R. Bandyopadhyay, N. Bhattacharyya, Optimization of sensor array in electronic nose by combinational feature selection method, 2012 Sixth International Conference on Sensing Technology (ICST). pp.341–346, 2012.
http://dx.doi.org/10.1109/icsenst.2012.6461698

Sarno, R., Nugraha, B., Munawar, M., Real Time Fatigue-Driver Detection from Electroencephalography Using Emotiv EPOC+, (2016) International Review on Computers and Software (IRECOS), 11 (3), pp. 214-223.
http://dx.doi.org/10.15866/irecos.v11i3.8562

Y. Saeys, I. Inza, P. Larranaga, A review of feature selection techniques in bioinformatics, Bioinformatics. Vol. 23, pp.2507–2517, 2007.
http://dx.doi.org/10.1093/bioinformatics/btm344

N. Sánchez-Maroño, A. Alonso-Betanzos, M. Tombilla-Snaromán, Filter methods for feature selection–a comparative study, Intelligent Data Engineering and Automated Learning - IDEAL 2007. pp.178–187, 2007.
http://dx.doi.org/10.1007/978-3-540-77226-2_19

L. Yu, H. Liu, Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution, in: Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003), AAAI, Washington DC, 2003: pp. 856–863.

Sarno, R., Munawar, M., Nugraha, B., Real-Time Electroencephalography-Based Emotion Recognition System, (2016) International Review on Computers and Software (IRECOS), 11 (5), pp. 456-465.
http://dx.doi.org/10.15866/irecos.v11i5.9334

P.F. Jia, F.C. Tian, S. Fan, Q.H. He, J.W. Feng, S.X. Yang, A novel sensor array and classifier optimization method of electronic nose based on enhanced quantum-behaved particle swarm optimization, Sensor Review. Vol. 34, pp.304–311, 2014.
http://dx.doi.org/10.1108/sr-02-2013-630

S. Zhang, C. Xie, D. Zeng, H. Li, Y. Liu, S. Cai, A sensor array optimization method for electronic noses with sub-arrays, Sensors and Actuators, B: Chemical. Vol. 142, pp.243–252, 2009.
http://dx.doi.org/10.1016/j.snb.2009.08.015

I. Guyon, S. Gunn, M. Nikravesh, L.A. Zadeh, Feature extraction: foundations and applications, 2006.
http://dx.doi.org/10.1007/978-3-540-35488-8

http://books.google.com/books?hl=en&lr=&id=x5hdbK8bIG0C&oi=fnd&pg=PA1&dq=Feature+Extraction+Foundations+and+Applications&ots=AtwkYzEUoc&sig=vgFlwPeyRlHejtN3OaVPmfrzUYk.

J.R. Vergara, P.A. Este, A review of feature selection methods based on mutual information, Neural Computing and Applications. 2013.
http://dx.doi.org/10.1007/s00521-013-1368-0

N.K. Al-qazzaz, S. Ali, S.A. Ahmad, S. Islam, Selection of Mother Wavelets Thresholding Methods in Denoising Multi-channel EEG Signals during Working Memory Task, in: 2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES), IEEE, Kuala Lumpur, 2014: pp. 8–10.
doi:10.1109/IECBES.2014.7047488

N. Al-Qazzaz, S. Hamid Bin Mohd Ali, S. Ahmad, M. Islam, J. Escudero, Selection of Mother Wavelet Functions for Multi-Channel EEG Signal Analysis during a Working Memory Task, Sensors. Vol. 15, pp.29015–29035, 2015.
http://dx.doi.org/10.3390/s151129015

U. Seljuq, F. Himayun, H. Rasheed, Selection of an optimal mother wavelet basis function for ECG signal denoising, 17th IEEE International Multi Topic Conference 2014. pp.26–30, 2014.
http://dx.doi.org/10.1109/inmic.2014.7096905

S. Saraçli, N. Dogan, I. Dogan, Comparison of hierarchical cluster analysis methods by cophenetic correlation, Journal of Inequalities and Applications. Vol. 1, pp.1–8, 2013.
http://dx.doi.org/10.1186/1029-242x-2013-203

B. Senliol, G. Gulgezen, L. Yu, Z. Cataltepe, Fast Correlation Based Filter (FCBF) with a different search strategy, 2008 23rd International Symposium on Computer and Information Sciences, ISCIS 2008. pp.0–3, 2008.
http://dx.doi.org/10.1109/iscis.2008.4717949

B.J. Doleman, M.C. Lonergan, E.J. Severin, T.P. Vaid, N.S. Lewis, Quantitative study of the resolving power of arrays of carbon black-polymer composites in various vapor-sensing tasks., Analytical Chemistry. Vol. 70, pp.4177–4190, 1998.
http://dx.doi.org/10.1021/ac971204+

R.X. Gao, R. Yan, Wavelets: Theory and Applications for Manufacturing, Springer, New York, 2011.

Y. Shin, D. Kwiatkowski, P. Schmidt, P.C.B. Phillips, Testing the Null Hypothesis of Stationarity Against the Alternative of a Unit Root: How Sure Are We That Economic Time Series Are Nonstationary?, Journal of Econometrics. Vol. 54, pp.159–178, 1992.
http://dx.doi.org/10.1016/0304-4076(92)90104-y

J.P. Harley, L.M. Prescott, Microbiology 5th ed, Fifth Edit, McGraw-Hill, 2002.
http://dx.doi.org/10.1007/s13398-014-0173-7.2

Refbacks

» —
» —
» —
» —

Username
Password
Remember me