Classification identification of corn juices based on taste sensor array

In order to identify corn juices with different flavor quickly and evaluate the conformance of the same corn juices, a taste sensor array including 12 sensors was built. The taste sensor array was tested with sweet, salty, sour, bitter and umami tastes as the evaluation of its ability to distinguish 5 basic tastes. Principal component analysis and Probabilistic neural networks were used for analyzing the effect to distinguish basic tastes based on the sensor array. The array allowed a successful recognition of the basic tastes. The taste recognition capability was further tested in the identification of corn juices. A total of 9 commercial corn juices from different brands were analyzed. Cluster analysis showed that taste characteristics from the same corn juices were similar, and aggregated as a cluster. Dimensionality reduction was achieved by Principal component analysis. The previous three principal components were applied as inputs of probabilistic neural networks. The taste sensor array showed good identification of corn juices with identification accuracy of 95.06%.