A Fast Pearson Graph Convolutional Network Combined With Electronic Nose to Identify the Origin of Rice

The quality of rice produced in different origins is different, and the gas reflects the external sensory information of rice. Based on the electronic nose (e-nose) instrument, the gas information of rice from different origins is obtained. An effective feature processing method is a key issue to improve the detection performance of e-nose. In this work, a fast pearson graph convolutional network (FPGCN) is proposed to identify the features extracted by the e-nose sensors and realize the origin tracking of rice. Based on the pearson correlation coefficient (PCC) value, the correlation between the features is quantified to construct the graph Laplacian matrix of graph convolutional network (GCN). The Chebyshev polynomial is introduced to reduce the computational complexity and parameters of GCN, and combine the binary tree method to speed up the pooling calculation. A multi-layer structure of FPGCN is designed to achieve the gas identification of rice. Compared with the traditional feature processing method, the FPGCN has a better classification result of 98.28%, the best F-1-score is 0.9829, and the best Kappa coefficient is 0.9799. In conclusion, the FPGCN provides an effective theoretical method to improve the detection performance of e-nose and a new technology to track the rice quality.

Automated summary

A fast Pearson graph convolutional network (FPGCN) was proposed to enhance the detection performance of e-nose and realize the origin tracking of rice, achieving a good classification result.