This study presents a feature-based domain adaptation model for refining sugar content prediction using eight signature bands extracted from hyperspectral images. The proposed method was evaluated on two fruit species, Ziziphus mauritiana and Syzygium samarangense. Hyperspectral image data consist of hundreds of spectral bands, resulting in large data volumes that are not suitable for rapid detection applications. Eight signature bands were selected through a systematic feature extraction process to address these limitations, reducing data dimensionality and acquisition complexity. However, the neural network models trained on signature-band datasets showed limited accuracy in predicting the sugar content of Ziziphus mauritiana and Syzygium samarangense. A domain adaptation strategy was implemented to overcome this limitation, transferring knowledge from models trained on hyperspectral or multispectral data to models using only the selected signature bands. Experimental results demonstrated the effectiveness of the proposed domain adaptation model in improving sugar content prediction based on signature-band datasets. For Ziziphus mauritiana, the 1D-CNN model trained solely on signature bands achieved an average mean absolute error (MAE) of 1.04 °Brix. By incorporating domain adaptation, the MAE was significantly reduced to 0.71 °Brix. Similarly, for Syzygium samarangense, the average MAE decreased from 0.80 °Brix to 0.66 °Brix after applying domain adaptation. These results indicate that the proposed approach substantially enhances prediction accuracy while reducing data dimensionality, offering an efficient and nondestructive solution for fruit quality assessment.