Quantitative approach of multidimensional interactive sensing for rice quality using electronic tongue sensor array based on information entropy

A novel quantitative approach of multidimensional interactive sensing based on information entropy was developed for the rapid determination of rice quality. Electronic tongue with multi-metal sensor array was employed. Physicochemical indexes including chalkiness, gel consistency, amylose, protein, starch and total metal element content which are the major indicators for rice quality were analyzed. Wavelet packet decomposition and fast Fourier transform were used for the decomposition and transformation of the original voltammetric signal. The square color block diagram and the dial color block diagram were used for the characterization. The multidimensional interaction matrix was constructed by information entropy. CNN model, BpNN model and the federated model (CNN + BpNN) were established to the quantitative prediction for the physicochemical indexes of rice. Compared with CNN and BpNN model, the accuracies of CNN + BpNN model were the highest. The training accuracies and prediction accuracies of CNN + BpNN with MMxI-3 as the input for all physicochemical indexes were 84.3 %similar to 92.0 % and 81.9 %similar to 89.5 % respectively, which were higher than those of other multidimensional interaction matrices as well as the original characteristic matrix as the input. Results indicated that the multidimensional interaction matrix contained more quantitative information in the sensor array for physicochemical components. In conclusion, the combination of the federated model and multidimensional interaction matrix for electronic tongue sensor array could be used as an effective approach for the quantification of rice quality.

Automated summary

A novel quantitative approach based on information entropy was developed for rapid determination of rice quality using an electronic tongue with a multi-metal sensor array. The combination of CNN and BpNN models in a federated framework showed the highest accuracy in predicting physicochemical indexes of rice quality. The multidimensional interaction matrix constructed through information entropy contained more quantitative information to effectively quantify rice quality.