Development of Discrimination Techniques for the Detection of Single and Multicomponent Gas Mixture using Tin Oxide (SnO2) based Sensor Array
Keywords:
Tin Oxide, Pattern Recognition, Artificial Neural Network, Sensor ArrayAbstract
Tin oxide-based gas sensors have been widely used to detect single-target and multi-component gas mixtures in ambient atmospheres, utilizing sophisticated classification techniques such as Artificial Neural Networks (ANN). These techniques optimize pattern recognition, using sensor arrays formed by multiple commercially available sensors, like the TGS class from FIGARO, and Inc. The integration of ANN with these sensor arrays allows for the effective use of odor sensors, capable of processing data and extracting hidden information regarding the nature and concentrations of various gases, including toxic and residue components. The development and refinement of these discrimination techniques hold significant promise for advanced detection and identification of complex gas mixtures, potentially establishing SnO2-based sensor technology as a reliable and sophisticated method for gas analysis. Comparative analysis of discrimination techniques, including PCA, LDA, PLSR, SVM, and ANN, reveals that KNN regression outperforms gas concentration estimation.
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