Spectral reflectance of lettuce leaves in growing status was measured using the ASD FieldSpec3, and logarithmic transformation was also obtained. By variable selection, it was found that the linear relationships between dry-basis moisture content of lettuce leaves and spectral reflectance data in 725 nm,1075 nm,1272 nm, 1450nm, 1640 nm and 1958 nm were very notable. In order to overcome the impact of multicollinearity, quantitative analysis models of dry-blade’s moisture content have been established respectively with methods of multiple linear regression analysis, principal component regression analysis, partial least squares regression analysis and PLS-neural network analysis. The result showed that the correlation coefficient R of measured and predictive values from the four algorithms were 0.4850, 0.8992, 0.9174 and 0.9470 respectively, which showed better predictive performance of the model based on PLS-neural network analysis than the others.