Color is an important quality indicator for agricultural products. The internal color of potatoes directly affects the sensory quality of their processed products. The rapid and non-destructive digital detection of the internal color of potatoes, as well as the quick classification of yellow-core and white-core potatoes, is of significant importance for advancing China's staple potato industry and the rapid rise of the prepared food industry. This study aims to achieve rapid, non-destructive digital characterization of the internal color of potatoes. It utilizes two self-developed, portable, multi-quality non-destructive detection devices based on a mini-spectrometer and a discrete spectral sensor: one is a laboratory-based, portable multi-quality non-destructive detection device for potatoes, and the other is a handheld, multi-quality non-destructive detection device for potatoes. A total of 209 potato samples from 26 different varieties, grown in various regions, were selected. Continuous spectral and discrete spectral data were collected, and the internal color parameters L^*，a^* and b^* of the potato samples were measured using a colorimeter. First, based on the L^*，a^* and b^* values measured by the colorimeter, an SVM classification model was established to distinguish between yellow-core and white-core potatoes. The threshold plane for distinguishing between yellow- and white-core potatoes was determined, which will serve as the basis for the next step in the non-destructive and rapid identification of yellow- and white-core potatoes. Secondly, based on the continuous spectral data collected by the portable continuous spectral device from the 209 potato samples, SNV preprocessing combined with the Random Frog Jump (RF) algorithm was used to select 200 characteristic wavelengths to establish a PLSR (Partial Least Squares Regression) prediction model for the L^*，a^* and b^* parameters of potatoes. The root mean square errors (RMSE) for the validation set of L^*，a^* and b^* were 1.278 8, 0.081 6, and 1.407 1, respectively. Similarly, for the discrete spectral data collected by the handheld spectral device, after SNV preprocessing, PLSR prediction models for the L^*，a^* and b^* parameters of potatoes were also established. The RMSE for the validation set of L^*，a^* and b^* were 1.278 8, 0.081 6, and 1.407 1, respectively. The results showed that the prediction models for the internal color parameters of potatoes established with both devices can meet the demand for rapid, non-destructive, digital detection of potato internal color in the field. Finally, 52 potato samples from 26 varieties, which were not involved in model training, were selected for external validation of the L^*，a^* and b^* predicted values using both devices. For the portable continuous spectral device, the maximum absolute residuals for L^*，a^* and b^* were 2.617 3, 0.141 3, and 2.779 1, respectively, and the mean residuals were 0.857 7, 0.049 0, and 0.697 2, respectively. For the handheld discrete spectral device, the maximum absolute residuals for L^*，a^* and b^* were 3.262 8, 0.203 4, and 3.519 5, respectively, and the mean residuals were 1.093 0, 0.066 7, and 1.268 8, respectively. Based on the L^*，a^* and b^* predicted values from both devices, rapid non-destructive identification of yellow and white-core potatoes was performed using the previously established classification threshold plane. The classification accuracy for yellow and white-core potatoes was 92.31% for the portable continuous spectral device and 86.54% for the handheld discrete spectral device. This technology enables the rapid, non-destructive, and real-time digital detection of potato internal color, as well as the quick classification of yellow- and white-core potatoes in the field. It provides technical support for the entire potato industry chain, including planting, processing, and sales.