Objective  To quickly and non-destructively detect polysaccharide content in Pleurotus eryngii using hyperspectral imaging technology.

Method  Hyperspectral images of P. eryngii in the visible and near infrared (390-1050 nm) regions were acquired using the hyperspectral imaging system. Polysaccharide contents in corresponding P. eryngii samples were measured by phenol sulfuric acid method. The binary mask image was constructed by the method of band operation and threshold segmentation to separate the sample area from the background area. Principal component analysis (PCA) was used to process the original hyperspectral images, and two principal component images (PC1, PC2) representing 99% information of the original image were obtained. Seven characteristic wavelengths of 554.4, 772.8, 811.4, 819.1, 855.6, 986.3, and 1 019.5 nm were selected using the successive projection algorithm (SPA). Texture and color data were extracted from two principal component images and seven characteristic wavelength images, and spectral data were also extracted from seven characteristic wavelength images. Using partial least squares regression (PLSR) models were established based on the correlations of different image features and polysaccharide contents in P. eryngii samples

Result  According to the determination coefficient of the calibration set (R_c²), the best model is the one based on characteristic spectra, characteristic wavelength images and principal component images with R_c²=0.954 and RMSE_C=0.341. According to the determination coefficient of the prediction set (R_p²), the best model is the one based on characteristic spectra and characteristic wavelength images with R_p²=0.868 and RMSE_P=0.539.

Conclusion  This study provides references for fast and non-destructive detection of polysaccharide content in Pleurotus eryngii.