Simultaneous quantifying and visualizing moisture, ash and protein distribution in sweet potato [Ipomoea batatas (L.) Lam] by NIR hyperspectral imaging

•NIR hyperspectral imaging was first used to evaluate the quality of sweet potato.•Moisture, ash and protein of sweet potato were well predicted by linear models.•Moisture, ash and protein distribution in sweet potato were visualized by color maps.•CARS was suitable for informative selection to predict moisture.•SPA was suitable for informative selection to predict ash and protein. This study aimed to achieve the rapid evaluation of moisture, ash and protein of sweet potato simultaneously by near-infrared (NIR) hyperspectral imaging (900–1700 nm). Hyperspectral images of 300 samples for each parameter were acquired and the spectra within images were extracted, averaged and preprocessed to relate to the three measured parameters, using partial least squares (PLS) algorithm, respectively, resulting in good performances. Nine, eleven and eleven informative wavelengths were selected to accelerate the prediction of the three parameters, generating a correlation coefficient of prediction (rP) of 0.984, 0.905, 0.935 and root mean square error of prediction (RMSEP) of 0.907%, 0.138%, 0.0941% for moisture, ash and protein, respectively. By transferring the best optimized PLS models to generate color chemical maps, the distributions and variations of the three parameters were visualized. NIR hyperspectral imaging is promising and can be applied to simultaneously evaluate multiple quality parameters of sweet potato.