Research of quantitative detection of coal samples based on near infrared spectrum

In view of requirement of quick and online detection for coal quality, the paper uses Fourier transform near infrared spectrum to separately build partial least square models of water, ash and volatile combining with different spectrum preprocessing methods, namely smooth processing method, differential method, multiplicative signal correction method and standard normal variate method, and makes decussation verification for detecting result of the models. The result shows that the partial least square model of water bulit by 25 points smooth processing method is better, the partial least square model of ash built by standard normal variate method is the best, precision of the partial least square model of volatile built by 5 points smooth processing method is the highest, which validates feasibility of applying Fourier transform near infrared spectrum technology to analyze coal indexes.