###### Abstract  
In this study, a fast and non-destructive method was proposed to analyze rapeseed quality parameters with the help of NIR hyperspectral imaging spectroscopy and chemometrics. Hyperspectral images were acquired in the reflectance mode. Meanwhile, the region of interest was extracted from each image by the regional growth algorithm. The kernel partial least square regression was used to build prediction models for crude protein content, oil content, erucic acid content, and glucosinolate content of rapeseed. The results showed that the correlation coefficients were 0.9461, 0.9503, 0.9572, and 0.9335, whereas the root mean square errors of prediction were 0.5514%, 0.5680%, 2.8113%, and 10.3209 umol/g for crude protein content, oil content, erucic acid content, and glucosinolate content, respectively. It demonstrated that NIR hyperspectral imaging is a promising tool to determine rapeseed quality parameters in a rapid and non-invasive manner. rapeseed; NIR hyperspectral imaging; quality parameters; kernel partial least square regression Article  
A Rapid and Nondestructive Detection Method for Rapeseed Quality Using NIR Hyperspectral Imaging Spectroscopy and Chemometrics  
A. 2023134  
Near-infrared hyperspectral imaging (NIR-HSI) was used to conduct quantitative and qualitative analysis due to its fast, non-destructive detection, and free use of chemical reagents [9; 10]. Combined with conventional near-infrared spectroscopy with imaging techniques to acquire both spectral and spatial information from an object, NIR-HSI can overcome the disadvantage of point detection and provide images for visual classification [11; 12; 13]. Meanwhile, the spectral data provide the internal quality of the sample. The image information can be used to detect the external quality of the sample [14]. NIR-HSI is a three-dimensional image block containing a large amount of spectral information with the wavelength, two spatial dimensions (x and y), and one spectral coordinate (\(\lambda\)) [15; 16; 17]. Usually, dimensionality reduction and feature extraction were used to extract the most important information for solving practical challenges and building calibration models based on the selected wavelengths [18]. NIR-HSI has been widely applied in the quality and safety of various agricultural products. The contents of amylose and amylopectin in different varieties of sorghum were predicted by the cascade forest model. The residual predictive deviations (RPD) were 4.7622 and 5.5889, respectively [19]. Feature wavelengths were extracted by the uninformative variable elimination, and the partial least squares regression (PLSR) model was built for the moisture of maize [20]. An attention mechanism was used to identify the geographical origins of Coix seeds [21]. The convolutional neural network (CNN) models were established to discriminate three varieties of soybeans. The results showed that the performance of the model using pixel spectra from 60 soybeans was comparable to a model using average spectra from 810 soybeans. Only one soybean of one variety was misjudged, and the rest were predicted correctly [22]. Meanwhile, there was considerable progress in the detection of single seeds with the help of HSI, such as the total lipid content of single cocoa beans [23], oleic acid content, and the linoleic acid content of a single soybean [24]. In summary, HSI was widely used in the detection of quality parameters, origin traceability, variety identification, and single seeds of agricultural products. Recently, it has been reported that NIR-HSI was successfully used to detect mycotoxins and pesticide residues in agricultural products. One-dimensional CNN (1D-CNN) was employed to detect aflatoxin on the surface of one peanut and one corn by detecting aflatoxin-containing pixels in a rapid, comprehensive, and non-destructive manner. The classification result showed that the accuracy was greater than 90%, which was of great significance for the early warning of mycotoxins in oilseeds [25].