Rapid Compositional Analysis of Sawdust using Sparse Method and Near Infrared Spectroscopy
This paper proposes to measure the components of sawdust by combining a new sparse method with near infrared(NIR)spectroscopy technology.The spectroscopic data of sawdust samples are acquired by the means of Fourier transform near-infrared(FT-NIR)spectrometer.Wavelet filter is used to remove undesired noises from the spectroscopic data,and multivariate statistical methods,such as principal component regression(PCR),partial least squares regression(PLS)and least absolute shrinkage and selection operator(LASSO)are used to model the relationship between the spectroscopic data and sawdust composition.The constructed model is then tested on a set of new samples.Compared with PCR and PLS,it is shown that LASSO,a sparse method,is capable of constructing a sparse model with stronger ability in interpretation while retaining good modeling accuracy.
near infrared spectroscopy LASSO sparse method
Wang Changyue Yao Yan Liu Huijun Wang Jingjun
College of Metrology & Measurement Engineering,China Jiliang University,Hangzhou,310000
国际会议
长沙
英文
4487-4492
2014-05-31（万方平台首次上网日期，不代表论文的发表时间）