Improvement of artificial odor discrimination system using fuzzy-LVQ neural network

Abstract

An artificial odor recognition system is developed in order to mimic the human sensory test in cosmetics, perfume and beverage industries. A backpropagation neural network is used as the pattern recognition system and shows high recognition capability. However, the system only works efficiently when it is used to discriminate a limited number of odors. The unlearned odor will be classified as one of the already learned category. To improve the system's capability, a fuzzy learning vector quantization neural network is developed and utilized in experiments on four different ethanol concentrations, and three different kinds of fragrance odor from Martha Tilaar Cosmetics. The results shows that the FLVQ has a comparable ability for recognizing the already known category of odors. However, the FLVQ algorithm can cluster the unknown odor in a different new class of odor.