［Objective ］ Addressing the heavy metal lead pollution in oysters using near -infrared spectroscopy technology.［Methods ］ This study proposed the use of near -infrared reflectance spectroscopy combined with pattern recognition for detecting Pb contamination.Initially,spectral data of healthy mussels and Pb -contaminated mussels in the range of 950~1 700 nm were collected.The wavelength selection algorithm of variable importance analysis based on the random variable combination (VIAVC ) was utilized to reduce the dimensionality,and selected the optimal subset of wavelengths.Considering the detection of healthy mussels and Pb -contaminated mussels as an imbalanced classification problem,the gravitational fixed radius nearest neighbor (GFRNN ) method based on universal gravity was explored for identifying Pb contamination in mussels.［Results］ The experimental results demonstrated that the proposed VIAVC -GFRNN method outperformed traditional algorithms such as K -nearest neighbor,fixed radius nearest neighbor,and support vector machine algorithms in detecting Pb contamination,while remaining unaffected by the imbalance ratio.The area under the receiver operation curve value of the VIAVC -GFRNN model reached 0.988 6,with a detection accuracy and geometric mean of 99.17%.［Conclusion ］ Near -infrared spectroscopy combined with pattern recognition methods has great potential for detecting Pd pollution in mussels.