Abstract: The aim of this study was to develop a rapid quality assessment method for Antarctic krill (Euphausia superba) by integrating near-infrared spectroscopy (NIRS) with partial least squares (PLS) regression. Quantitative models were established to predict two critical quality indicators: non-protein nitrogen (NPN) and total volatile base nitrogen (TVB-N) contents. Following spectral acquisition, the key model parameters, including the preprocessing methods, characteristic spectral ranges, and principal factor numbers, were systematically optimized. Model performance was evaluated using the coefficient of determination (R²), root mean square error of calibration (RMSEC), and root mean square error of prediction (RMSEP). For the NPN model, multiplicative scatter correction (MSC) was selected as the optimal preprocessing method, with a characteristic spectral range of 8887.1 cm^-1 to 7774.2 cm^-1. The TVB-N model utilized a combination of MSC and Savitzky-Golay smoothing (SG), with the full spectral band employed for modeling. Both models adopted five principal factors. After optimization and external validation, the optimized NPN model demonstrated a robust performance, with R²=0.9384, RMSEC=0.279, and RMSEP=0.443, whereas the TVB-N model achieved R²=0.8685, RMSEC=3.800, and RMSEP=4.070. These results indicate that both models exhibit high predictive accuracy (R²>0.85) and stability, with the NPN model outperforming the TVB-N model in terms of predictive capability. In conclusion, quantitative analysis models constructed by combining NIRS and PLS can predict the NPN and TVB-N content in Antarctic krill. This approach provides a rapid solution for Antarctic krill quality assessment, addressing the growing demand for the efficient monitoring of Antarctic krill resource utilization.