| Popis: |
Background: Odors are perceived by individuals through sensation, memory, experience, etc., and mainly subjective and qualitative evaluation has been performed until now. On the other hand, since Buck et al. succeeded in cloning the odorant receptor in 1991, researchers have shown interest in olfaction system, and molecular biology researches on olfaction system have begun actively. However, no objective evaluation has been made on the identification or similarity of odors. As for the odorant sensors, an odor is identified by using quartz oscillators or semiconductors and measuring a change in characteristics caused by odor molecules adhering to the sensor. In this study, we attempted to quantitatively express the direct association between odorant receptors and odor molecules at each concentration. The degree of similarity evaluated by taking inner product of the odorant receptor vectors was verified by comparing with the results by sensory evaluation tests published in past literatures in the form of correlation coefficient. A multi-layered artificial neural network whose input was the elements of prescribed odorant receptor vector was also introduced to identify odorant molecules in the output layer.Results: The inner product of proposed odorant receptor vector might evaluate the degree of similarity quantitatively among various odorants. Further, it might be possible to identify odorant molecules by the artificial neural network using EC50 of receptors to various odorants.Conclusions: In this paper, we proposed a vector consisting of the EC50 of odorant receptors and compared the inner product of the vector with the results of sensory evaluation tests based on the responses of odorant receptors to odorant substances at various concentrations. It was shown that not only the correlation between each odorant substance but also the correlation between the inner product of the odorant substance receptor vector and the results of the sensory evaluation test can be quantitatively evaluated. It was also shown that the EC50 of each odorant receptor can be placed in the input layer of an artificial neural network, and that an artificial neural network with odor molecules as the output layer can discriminate odors. |
