International Journal of Computer Applications (0975 – 8887) Volume 51– No.1, August 2012 7 Formulating a Mathematical Model for the E-Nose Application through Genetic Algorithm (GA) Krishnamurthy Nayak Research Scholar Dr. MGR University, Chennai Asst Professor, Dept of ECE, MIT Manipal Manipal University Deccaraman M Dean, Department of Industrial Biotechnology Dr. MGR University, Chennai Vijayashree Nayak Asst. professor, Department of Biological Sciences BITS Pilani, Goa ABSTRACT From a technical and commercial point of view it is found that electronic sensing technologies have emerged significant progresses over the last few decades. The potentiality of reproducing human senses by sensor arrays and pattern recognition systems is termed as electronic sensing. E-Nose provides an industry-specific management resolution for the perpetual and real-time monitoring of environmental odor and air quality resulting in higher profit and improved community relations. The device constitutes arrays of effective and rapid acting chemical sensors, supplemented by patented electronics and software. Chemicals in the air are identified by the sensor arrays, registering complex odor images in real time. By means of wireless connection or lines a permanent record is sent to the computer, where it is detected, computed and alarms for inconsistent events were sent or else it can be indicated by some displacement. Electronic nose instruments are exploited by research and development laboratories, quality control laboratories, process & production department’s of environmental protection, all these are done for the detection of volatile organic compounds in air, water and soil samples, and the measurement and comparison of the effects of manufacturing process on products are also determined. In this paper, An E-Nose is proposed to identify the gas component. For this process, the soft computing technique called Genetic algorithm is used. This provides an optimized weight to identify the gas component by means of the input concentration range and SMAC/hr (units in ppm). The intended Technique is evaluated with different training samples and results are produced. Keywords Electronic nose, odor, electronic sensing, concentration range and SMAC, volatile organic compounds, Genetic algorithm (GA). 1. INTRODUCTION Nowadays, sensor for gaseous molecules plays a significant role in monitoring the environment, controlling chemical processes, and in medical applications. Electronic Nose (E-nose) is a device used to detect and identify the odors/vapors. Although it has been in the market for several years, its size is large and also it is high-priced [3]. In an ever-growing world, the electronic devices are duplicating every other sense of perception; the sense of smell is lagging behind. But still, there has been a significant increase in the need for detecting odors, to replace the human job of sensing and quantification. Several important applications fall in the category where humans cannot meet the risk in smelling the substance. In many fields, the detection of volatile organic compounds (VOCs) has become a serious task as the fast evaporation rate and toxic nature of VOCs and working close to human life could be dangerous at high concentration levels in air for the health of human beings. In fact, the VOCs are also considered as the main reason for allergic pathologies, skin, and lung diseases [7]. Some necessary applications such as continuous monitoring, medical applications, etc., allow humans to perform tasks that were once considered as unfeasible. The fast paced technology has helped to develop sophisticated devices that have brought the electronic nose to small sizes with superior capabilities. The trend is such that there will be precise, qualitative, and quantitative measurements of odor in the near future. The growth of gas sensors is a field of great activity. Especially, the electronic noses are used for process control, quality control in the food and beverage industry, pollution monitoring, and airport security. [1] Unpleasant odor or malodor has been regarded as an indicator of potential risks to human health but not necessarily the direct trigger of health effects. Thus, development of E-nose for medical diagnostics has become one of the important issues in the biomedical engineering research now days [2]. Electrical properties will be changed when the sensors react with odorant [3]. A chief concern of scientists is global warming, largely due to huge emissions of carbon dioxide (CO2) which is considered as one of the main greenhouse gases inducing a warming climate, CO2 concentration in the atmosphere is under special analysis of many weather services in the world. Suburban areas continue to grow rapidly and are potentially an important land- use category for anthropogenic carbon-dioxide emissions [4]. The core component of an E-Nose is an array of non-specific chemical sensors. An odor examines and stimulates many of the sensors in the array and extracts characteristic response pattern. The sensors inside e-Nose can be made up of various technologies, but in all cases certain physical property are to be measured and a set of signals is generated [6]. It consists of a set of different gas sensors which can detect different toxic gasses. Output of sensor array is connected with encoder circuit and gas sensors often respond to an open range of gas species and are therefore only partially selective [5]. Generally, source localization is done by the support of electronic nose and mobile vehicles since robotic techniques are getting mature. However, certain problems could occur during specific situations, such as instantaneous emission and complex landscape environment [8]. If gas sensor array discovers any toxic gasses of dangerous level of lethal concentration, then processor will switch on the exhaust fan in the laboratory [5]. The toxin gasses in the room are to be collected in a proper manner and should be removed by means of appropriate techniques. 2. GENETIC ALGORITHM In 1970, John Holland proposed the Genetic Algorithm (GA). GA modeled on the process of natural selection for stressing