Identification of the quality for rice with different storage humidity: An electronic nose combined with multiblock feature integration method

Abstract

The ability of traditional attention mechanisms (AMs) to extract useful features from electronic nose (e-nose) data is limited, which affects the performance of the e-nose system for the identification of rice quality. Motivated by this, a nondestructive testing method incorporating an e-nose and multiblock feature integration (MBFI) is proposed to effectively discriminate the quality of rice at different storage humidity. First, gas information for two brands of rice at five storage humidity is acquired using the e-nose system. Second, the feature-mining ability of quality classification models is enhanced by the MBFI module. Finally, compared with the recognition results of multiple AMs, multiple classification models, and ablation analysis, the best identification performance and stability for rice quality are obtained by the MBFI and a residual network18 model. In conclusion, effective identification of rice quality is achieved by the e-nose and MBFI.