Origin traceability of rice based on an electronic nose coupled with a feature reduction strategy

Effective information processing technology is one of the keys to improving detection accuracy. In this study, a feature reduction strategy is proposed for reducing the dimension of electronic nose (e-nose) sensor features, in combination with multiclassifiers to identify the origin of rice. Firstly, the time domain and time-frequency domain features were extracted from the detection data. Secondly, the kernel principal component analysis and kernel entropy component analysis (KECA) were introduced to reduce the dimension of the fusion features to obtain the kernel principal components (KPCs) and kernel entropy components (KECs). Finally, global discriminant analysis (GDA) was proposed in order to reduce the dimension of the KPCs and KECs to obtain the final features, respectively. The results indicated that the KECA-GDA achieved the dimensionality reduction of fusion features, effectively, the good classification accuracy of 97% and 93.29%, F-1-scores of 0.9697 and 0.9410, and Kappa coefficients of 0.9648 and 0.9210 were obtained by means of the random forest (RF) method in uncooked and cooked rice, respectively. This study shows that KECA-GDA-RF can be used as an effective tool in tracing the origin of rice. Moreover, it can provide a useful processing technique to improve the measurement accuracy of an e-nose.

Automated summary

In this study, a feature reduction strategy was proposed to enhance the detection accuracy of rice origin. By combining dimensionality reduction and multi-classifiers, the classification accuracy reached as high as 97%.