Abstract: Chicken meat was widely favored by consumers for its excellent texture and reasonable price. However, the frequent occurrence of white striping (WS) myopathy significantly impacted its quality and marketability. In this study, the comprehensive myopathy index (CMI) was developed as a quantitative predictor of WS myopathy using multiple physicochemical parameters, including pH, shear force, cooking loss, drip loss, and color. In combination with hyperspectral imaging (HSI), a CMI prediction model was developed to achieve rapid and non-destructive identification of WS myopathy. To address the limitation of insufficient samples, a regression generative adversarial network (RGAN) was proposed. Spectral data and the corresponding CMI values were generated simultaneously. And the generated data were highly consistent with the real data in spectral features, t-distributed stochastic neighbor embedding (t-SNE), and CMI values. The comparison of models showed that the convolutional neural network regression (CNNR) model achieved the best performance (R_p² of 0.835, RMSEP of 0.057) when 300 generated samples were added. This result confirmed the effectiveness of RGAN in improving modeling accuracy and generalization. In conclusion, the proposed CMI prediction method integrating HSI with RGAN enabled accurate quantification of WS myopathy in chicken breast. It also provided an effective solution for intelligent detection under limited sample conditions.