Artificial odor discrimination system using multiple quartz resonator sensor and FNLVQ-MSA neural network for recognizing concentration of odor

An electronic odor discrimination system had been developed. The developed system showed high recognition probability to discriminate various single odors to its high generality properties, however the system had a limitation in recognizing the fragrances mixture. In order to improve the performance of the proposed system, development of the sensor and other neural network are being sought. This paper explains the improvement of the capability of that system. In this experiment, the improvement is conducted not only by replacing the last hardware system from 4 quartz resonator-basic resonance frequencies 10 MHz with new 16 quartz resonator-basic resonance frequencies 20 MHz, but also by replacing the pattern classifier from Back Propagation (BP) neural network with Variance of Back Propagation, Probabilistic Neural Network (PNN) and Fuzzy-Neuro Learning Vector Quantization. Matrix similarity analysis (MSA) is then proposed to increase the accuracy of the FNLVQ, become FNLVQ-MSA neural system in determining the best exemplar vector, for speeding up its convergence. The purpose of the recent study is to construct a new artificial odor discrimination system for recognizing the concentration of fragrance. The using of new sensing system and FNLVQ-MSA has produced higher capability to recognize the concentration of fragrance compared to the earlier mentioned system.