Non-destructive detection of moisture and fatty acid content in rice using hyperspectral imaging and chemometrics

Abstract

Moisture content (MC) and fatty acid content (FAC) are critical indicators in terms of rice quality. Rapid and non-destructive detection of MC and FAC is crucial for rice pre-storage inspection and storage monitoring. In this study, hyperspectral imaging combined with chemometrics was used to detect MC and FAC in rice non-destructively. Competitive adaptive reweighted sampling (CARS) and sequential projection algorithm (SPA) methods were employed to select significant variables, and partial least squares regression (PLSR) was utilized to establish prediction models of MC and FAC in rice and polished rice. Also, the effect of rice husk on the performance of models was analyzed. Finally, visualization of MC and FAC of rice samples was achieved. The results indicate that the SPA method is superior to the CARS method in selecting significant wavelengths of both MC and FAC. The R P 2 and RMSEPs in the best models for the "rice-moisture", "polished rice-moisture", "rice-fatty acid", and "polished rice-fatty acid" datasets are 0.9650, 0.9567, 0.8573, 0.8436, and 0.0031, 0.0033, 1.6956, 2.0270, respectively. The effect of rice husk on the performance of the FAC model is significant. This study demonstrates that it is feasible to use hyperspectral imaging technology to detect both MC and FAC of rice. • The effect of rice husk on the performance of MC and FAC models was analyzed. • Visualization of MC and FAC distribution in rice samples was achieved. • SPA method is suitable to select significant wavelengths for MC and FAC. • The effect of rice husk on the performance of the FAC model is significant.