Taste sensation evaluation for an electronic tongue based on an optimized computational model of taste pathways

Abstract

Abstract Effective evaluation of taste sensation can be achieved by analyzing electronic tongue (e-tongue) data. Research on evaluation of the taste sensation of an e-tongue by nerve conduction mechanisms is limited, which obviously affects performance evaluation of e-tongues. Therefore, in this paper, a method for evaluating the taste sensation of an e-tongue based on human taste conduction mechanisms, the computational model of taste pathways (CMTP), is proposed. However, the limited physiological basis of the CMTP parameters influences the evaluation results. To achieve excellent evaluation performance, a parameter optimization algorithm with Hebbian and habituation learning rules is used to optimize the CMTP parameters. The effectiveness of the optimized results is demonstrated by improvement in the dynamic characteristics of the CMTP. Next, the optimized CMTP (OCMTP) is used for pattern recognition and sweetness evaluation of four taste substances. The results showed, first, that the dynamic characteristics (including 1/f characteristics and synchronization) of the OCMTP are improved, and the bionics of the OCMTP is enhanced. The optimized results are effective. Second, compared with the recognition results for the four taste substances by the unoptimized CMTP (UCMTP), signal preprocessing methods and multiclass classification models, the best classification accuracy (95.38%), the best kappa coefficient (93.83%) and the best F 1-score (96.10%) are achieved by the OCMTP. Finally, compared with the sweetness evaluation results of the UCMTP, signal preprocessing methods and multiple evaluation models, the best evaluation performance, including a root-mean-square error of 0.1643 and R 2 of 0.9785, is obtained using the OCMTP. In conclusion, effective evaluation of taste sensation can be achieved by the OCMTP and an e-tongue.