Abstract
Research on the taste conduction mechanisms of data processing by the electronic tongue (e-tongue) is lacking. These mechanisms affect taste substance identification by the e-tongue system. This work proposed a computational model of taste pathways based on nerve conduction mechanisms to identify taste substances. This work modelled human taste pathways based on the Katchalsky models and validated the rationality of the modelling forms of the critical pathway structures. Next, this work simulated the response status and power spectral density of the model nodes and verified the bionic performance of the computational model. The results showed, first, that the computational model described the dynamic characteristics of the taste pathways. Second, the phase diagrams of the pathway structures revealed chaotic characteristics. This result showed that the modelling forms were rational. Third, when stimulated, the model nodes exhibited fast responses. During stimulation, the power spectral density of the model nodes demonstrated 1/ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${f}$ </tex-math></inline-formula> characteristics. The fast-response ability and 1/ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${f}$ </tex-math></inline-formula> characteristics reflected the bionic performance of the computational model. Finally, the computational model was used to identify beer, tea, and apple samples. Compared with other classification methods, the computational model achieved better classification results of 93.33%, 94.00%, and 76.67%, the best specificity of 0.9833, 0.9850, and 0.9417, and the best Kappa coefficients of 0.9180, 0.9261, and 0.7245 for the identification of the beer, tea, and apple samples, respectively. In conclusion, the computational model can effectively identify taste substances.
Published Version
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