Moisture and Calorific value， which are two of the most important properties of straw for energy conversion process， were predicted by near infrared spectroscopy (NIRS) technique and LOCAL algorithm. Firstly， the samples were evenly divided into 3 subsets according to the chemical values， named high， mid and low concentration respectively， to build global partial least square regression(PLS) calibrations. Standard errors of cross validation (SECV) of the three calibrations based on subsets were lower than that of calibration based on the whole samples， which suggested that the variation of moisture and calorific value affected the accuracy of NIRS calibrations. Then， LOCAL algorithm was introduced to near infrared spectroscopy analysis for rapid measurement of the moisture content and calorific value of straw samples. By the use of LOCAL algorithm， the prediction accuracy is improved compared to the PLS and MPLS models for both moisture and calorific value of straw. It is therefore concluded that LOCAL algorithm has a broad application prospect in quantitative analysis of straw.