NIR spectroscopy is widely used in chemical analysis, agricultural science, food safety, and other fields, but its high dimensionality and data redundancy bring analytical challenges. This study aims to compare the performance of different wavelength selection methods in NIR spectral datasets with different dimensionalities to provide a reference for researchers. The wavelength selection methods in this study were classified into four categories according to their principles, which are partial least squares (PLS) parameter-based methods, intelligent optimization algorithms (IOA)-based methods, model population analysis (MPA)-based methods and wavelength interval selection (WIS) methods. The performance of the models was compared in terms of R