The application of near infrared spectrum technology in rapid detection of Monascus biomass in solid-state fermentation was studied in this paper. Four batches of 80 samples′s spectra were collected,and glucosamine method was used to estimate the biomass. In order to reduce the complexity of the model and improve the prediction performance, the application of the genetic algorithm (GA) to selected wavelenths region was studied,and partial least squares regression was constructed for the prediction of biomass value in solid state fermentation of Monascus with effective wavelengths selected by GA. To illustrate the feasibility of GA to optimize spectral variables,partial least squares regression (PLSR) was constructed with full-spectrum and the wavelengths were selected by the correlation coefficient method,respectively. The prediction ability of the three models were comparatively analyzed,and the correlation between the spectral bands information selected by GA algorithm and the corresponding absorption generated by hydrogen groups of bacteria′s composition was explained. The results showed that GA could reduce the complexity of the model and improve the model′s prediction performance,with Rc=0.998 3,RMSECV=3.580 2,Rp=0.993 1,RMSEP=3.643 7,data points participate in modeling decreasing from the original 1 457 to 585,and the model predict-tion accuracy is improved by 11.55% compared with that of the full spectrum′s PLS model. The result showed that the PLS model built by using near infrared spectroscopy combined with GA could realize the rapid detection of biomass of Monascus in solid state fermentation. The method provided the technical foundation to further realize online fermentation process optimization control.