Two hundred and twenty-two straw samples, consisting of 170 rice straw samples and 50 wheat straw samples, were collected from 24 provinces of China. Near infrared spectroscopy (NIRS)was applied to build quantitative models for calorific value of straw combining the use of principal component regression (PCR), partial least square regression (PLS)and modified partial least square regression (MPLS). Different scatter correction methods and derivative treatments were adopted to help improve the accuracy of NIRS models. A total of 54 NIRS models were obtained and independent validations were conducted using the same validation set of samples. A statistical comparison of independent validation results was then introduced to evaluate whether the models perform significantly. Bias and bias corrected standard error of prediction (SEP(C)), which are the mean and the standard deviation of the prediction residuals respectively, were compared by the proposed statistical procedures. It was concluded that near infrared spectroscopy was able to predict the calorific value of straw samples rapidly and accurately, with resulting SEP(C)s between 134 and 178 J·g-1; statistical comparison of biases and SEP(C)s was a reasonable and efficient way to compare spectral pre-processing methods, and select NIRS models predicting calorific value of straw.