An electronic nose is an intelligent system consists of a sensor network and a pattern recognition system able to know simple and complex odors. As the human nose, the artificial nose must learn to recognize different odors: the learning phase. There are several types of sensors such as fiber optic sensors, piezoelectric sensors, sensor type MOSFET. The performance of the sensor network is discussed by using pattern recognition methods. In this article, we tested Principal Component Analysis (PCA) to evaluate the ability of our sensor array to distinguish between different groups of target gases according to their nature: only in binary mixture and ternary mixture.

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Micro Hotplate (MHP) is the key component in micro-sensors, particularly gas sensors. Indeed, in metal oxide gas sensors MOX, micro-heater is used as a hotplate in order to control the temperature of the sensing layer which should be in the requisite temperature range over the heater area, so as to detect the resistive changes as a function of varying concentration of different gases. Hence, their design is a very important aspect. In this paper, we have presented the design and simulation results of a meander micro heater based on three different materials -platinum, titanium and tungsten. The dielectric membrane size is 1.4 mm × 1.6 mm with a thickness of 1.4 μm. Above the membrane, a meander heating film was deposed with a thickness of 100 nm. In order to optimize the geometry, a comparative study by simulating two different heater thicknesses, then two inter track widths has also been presented. Power consumption and temperature distribution were determined in the micro heater´s structure over a supply voltage of 5, 6, and 7 V.

With the advent of the Internet of Things Technologies (IOT), smart homes, and smart city applications, E-Nose was created. Almost of gas sensors consisting the electronic nose system suffer from cross sensitivity and lack of selectivity. Coupling smart gas sensors with artificial intelligence algorithms can thus empower conventional gas sensing technologies and increase accuracy in gas detection. This study describes the overall types of smart gas sensors used in air quality control, signal pre-processing and extraction features. Also it presents pattern recognition methods used in E-nose applications including linear methods such as Linear Discriminant Analysis LDA, K-Nearest Neighbors KNN and Non-linear such as algorithms Support vector Machine SVM and Artificial Neural Network ANN and their impact on improving accuracy rate for gas detection. Finally, this paper summarizes by providing directions for about how to leverage the benefits of combining these classifiers which is known as Data fusion approach and ensemble classifiers.