Near-Infrared Spectroscopy Coupled with a Neighborhood Rough Set Algorithm for Identifying the Storage Status of Paddy

Rapid and non-destructive identification of the suitable storage status of paddy during storage is crucial for controlling the quality of stored grains, which can provide high-quality raw grains for rice processing. Near-infrared (NIR) spectroscopy combined with neighborhood rough set (NRS) and multiple classification methods were used to identify the different storage statuses of paddy. The NIR data were collected in the range of 1000–1800 nm, and three storage statuses from suitable storage to severely unsuitable storage were divided using the measured fatty acid value of paddy. The spectral features were selected using NRS, successive projection algorithm and variable combination population analysis methods. Random forest (RF), extreme learning machine, and soft independent modeling of class analogy classifiers coupled with spectral features were used to establish classification models to distinguish the different storage statuses of paddy. The comparison results indicated that the optimal wavelengths selected by NRS combined with the RF classifier to construct the NRS-RF series models led to satisfactory identification results, with high correct classification rates of 96.31% and 93.68% in the calibration and test sets, respectively; the indicators of sensitivity and specificity ranged from 0.93 to 0.99. Therefore, the combination of NIR technology with NRS and RF algorithms for identifying the storage status of paddy was feasible, as this would be more helpful for rapidly evaluating the changes of stored paddy quality. The proposed method from this study is expected to provide support for the development of non-destructive equipment for the accurate detection of the quality of stored paddy.