摘要
针对煤质快速在线检测的需求,采用傅里叶变换近红外光谱结合不同的光谱预处理方法,即平滑处理方法、微分方法、多元散射校正方法、标准归一化处理方法分别建立了煤粉样品的水分、灰分和挥发分的偏最小二乘模型,并对模型的检测结果进行了十字交叉验证。结果表明,基于25点平滑处理方法建立的水分偏最小二乘模型较优,基于标准归一化处理方法建立的灰分偏最小二乘模型最佳,基于5点平滑处理方法建立的挥发分偏最小二乘模型精度最高,验证了应用傅里叶变换近红外光谱技术定量分析煤粉指标的可行性。
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 built 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.
出处
《工矿自动化》
北大核心
2013年第8期68-71,共4页
Journal Of Mine Automation
基金
国家自然基金资助项目(41201294)
江苏省农产品物理加工重点实验室开放基金项目(JAPP2012-2)
山西省青年科技基金项目(2009021019-3)
关键词
近红外光谱
煤粉样品
定量检测
预处理方法
偏最小二乘法
near infrared spectrum
coal sample
quantitative detection
preprocessing method
partial least square method
