Gray wolf optimizer(GWO) algorithm, which is based on swarm intelligence, is easy to implement due to its few parameters and simple structure. However, to our knowledge, few studies used GWO for the spectral analysis. In this study, the GWO was introduced into the variable selection of NIR spectra.Taking corn dataset as an example,the performance,numbers of iterationsnumbers of wolves and efficiency of GWO algorithm were investigated.Based on this,a partial least squares(PLS) model was established to determine the protein,fat,moisture and starch contents in corn samples.Results showed that GWO algorithm was very efficient.With optimized parameters,the retention variable numbers of GWO algorithm for protein,fat,moisture and starch were 19,19,14 and 34,respectively.Compared with root mean square error of prediction(RMSEP) values of the full wavelength PLS model for the four components,those of the GWO-PLS model decreased from 0.245 8,0.122 4,0.339 8 and 1.105 8 to 0.147 7,0.080 1,0.176 2 and 0.739 8,with their decreasing percentages of 40%,35%,48% and 33%,respectively.Meanwhile,the correlation coefficients were increased accordingly.Therefore,GWO algorithm could improve the prediction accuracy of the PLS model apparently with high efficiency and fewer selected variables.It is a promising method for variable selection of NIR spectroscopy.