In order to improve the accuracy of on-line monitoring of ethanol solid-state fermentation process, We carried out the fast quantitative detection of ethanol solid state fermentation process parameters based on Fourier transform near infrared spectroscopy (FT-NIRS). The synergic interval partial least squares (siPLS) method was taken to select the optimal wavelength intervals from the standard normal variate transformation (SNV) preprocessing spectra. Characteristic wavelength variables were extracted from the optimal wavelength intervals by genetic algorithm (GA), competitive adaptive reweighted sampling (CARS) and iteratively retaining informative variables (IRIV). The partial least squares (PLS) forecast model of solid state fermentation process parameters (the level of ethanol and glucose) was established. The characteristic wavelength variables of these parameters were selected by different methods. The results show that compared with GA and CARS methods, IRIV method can select the fewest wavelength variables and the number of characteristic variables associated with ethanol and glucose is 43 and 40, respectively. In IRIV-siPLS model, root mean square error of prediction (RMSEP) is 0.2511 and correlation coefficient of prediction Rp is 0.9934 for ethanol, RMSEP is 0.1730 and Rp is 0.9926 for glucose in prediction set, and the prediction accuracy of the results is also higher than those of other methods. Based on the result, on-line detection of key process parameters in the process of solid-state fermentation is feasible with the near infrared spectral technology. IRIV is an effective method to select characteristic wavelength from the near infrared spectra and improve precision of the prediction model.