Timely monitoring variations of the solid concentration plays a significant role in the stability control of the anaerobic digestion process. In this study, infrared hyperspectral technology coupled with chemometrics methods is applied to detect the amount of solid concentration in which process water hyacinth and rice straw are co-digested. Compared to the traditional way (2540G APHA, 1990) , it is faster and non-destructive. Firstly, the hyperspectral information of fermentation broth is obtained by application of infrared hyperspectral and the spectroscopy data is pretreated by utilizing moving average smoothing (MAS), and then adaptive weighted sampling competition (CARS), random frog (RF) and successive projections algorithm (SPA) are applied to extract characteristic wavelengths. Finally the calibration models of total solid (TS) and volatile solid (VS) are established based on the extracted characteristic wavelengths, partial least square (PLS) and least square-support vector machine (LS-SVM), Which are taken to predict the solid concentration of fermentation broth. The study indicates that SPA-LS-SVM model achieves optimal result, among which the root mean square error prediction (RMSEP) and correlation efficient (R) of the total solid concentration are respectively 0.005 8 and 0.841; the root mean square error prediction and correlation efficient of the volatile solid concentration are respectively 0.004 1 and 0.874. The study shows that it is feasible to utilize infrared hyperspectral combined with chemometrics methods for prediction of the solid concentration of the fermentation broth, and it can provide a theoretic and practical basis for setting up a spectral sensor to detect the solid concentration of anaerobic digestion process.