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Vol. 17, Special Issue, December 2012, pp. 50-59

 

Selected papers from the 4th International Conference on Intelligent & Advanced Systems (ICIAS' 2012),

12-14 June 2012, Malaysia, Kuala Lumpur

 

Bullet

 

Artificial Neural Network-based Electronic Nose for the Detection of Sulfate-reducing Bacteria
 
1 Earn Tzeh TAN, 2 Zaini ABDUL HALIM, 3 Darah IBRAHIM, 3 Rashidah ABDUL RAHIM, 1 Junita MOHAMAD SALEH, 1 Umadevi CHANDARAN

1 School of Electrical and Electronic Engineering, Universiti Sains Malaysia

14300 Nibong Tebal, Penang, Malaysia

Tel.: +6012 5250719

2 Collaborative Microelectronic Design Excellence Centre, Universiti Sains Malaysia

14300 Nibong Tebal, Penang, Malaysia

3 School of Biological Sciences, Universiti Sains Malaysia

14300 Nibong Tebal, Penang, Malaysia

E-mail: tanearntzeh@yahoo.com

 

Received: 29 September 2012   /Accepted: 29 October 2012   /Published: 18 December 2012

Digital Sensors and Sensor Sysstems

 

Abstract: The paper demonstrates a method for the implementation of an olfactory system for the detection of sulfate-reducing bacteria. The unchecked growth of sulfate-reducing bacteria causes serious microbiologically-influenced corrosion problems in various industrial systems. Conventional techniques or instruments used to detect the presence of bacteria are time consuming and require a high degree of profession skill. This work examines the potential of using an electronic nose, equipped with a metal oxide semiconductor and temperature sensors, combined with an artificial neural network, to measure and evaluate the presence of the bacteria. The input values for the neural network are represented by sensors’ data set, whereas the output values indicate the presence of bacteria. This study also demonstrates the modeling techniques of a trained neural network using a Xilinx System Generator toolbox in a Matlab environment, so that the system can be implemented in a field programmable gate array for portable electronic nose applications. The preliminary design of the electronic nose and artificial neural network data processing promise a successful technique and has the potential to be an effective means of detecting the presence of sulfate-reducing bacteria.

 

Keywords: Electronic nose, Sulfate-reducing bacteria, Artificial neural network

 

 

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