Herein, a hyperspectral imaging system in the 380-1030 nm range was used to rapidly determine the moisture content of scallops in different dehydration periods. Mean spectral values of scallops were extracted from hyperspectral images. Only eight optimal wavelengths were selected using the regression coefficient method. Spectra of full wavebands and selected wavelengths were used as independent variables for modeling. Partial least squares regression (PLSR) and least-squares support vector machines (LSSVM) were employed to establish multispectral calibration models to correlate spectral features with moisture content. The best results, with correlation coefficients of prediction (Rp), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) of 0.9673, 3.5584%, and 3.7150, respectively, were achieved using the optimal wavelength-based PLSR model. To visualize moisture content in scallops, a visualization map was generated using the selected wavelength-based PLSR model. These results highlight the potential of hyperspectral imaging for non-destructive prediction of moisture content in scallops. (C) 2017 Elsevier Ltd. All rights reserved.