Pre-Processing Influence on Near-Infrared Spectra for Determination of Volatile in Lignite Coal Samples

2014 ◽  
Vol 898 ◽  
pp. 831-834 ◽  
Author(s):  
Xiao Li Yang ◽  
Fan Wang
Keyword(s):  
Wavelet Transform ◽  
Least Squares ◽  
Discrete Wavelet Transform ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Discrete Wavelet ◽  
Least Squares Regression ◽  
Lignite Coal

We studied volatile determination in lignite coal samples using near-infrared (NIR) spectra. Firstly, spectra were pre-processed to eliminate useless information. Then, determination model was constructed by partial least squares regression. To study the influence of pre-processing on determination of volatile for NIR analysis of lignite coal samples, we applied four techniques to pre-process spectra, including normalization, standardization, centralization, derivative and discrete wavelet transform. Comparison of the mean absolute percentage error (MAPE) and root mean square error of prediction (RMSEP) of the models show that the models constructed with spectra pre-processed by discrete wavelet transform gave the best results. Through parameters optimization, the results show that discrete wavelet transform and partial least squares regression can obtain satisfactory performance for moisture and volatile determination in coal samples.

Determination of carbon fraction and nitrogen concentration in tree foliage by near infrared reflectances: a comparison of statistical methods

1996 ◽  
Vol 26 (4) ◽  
pp. 590-600 ◽  
Author(s):  
Katherine L. Bolster ◽  
Mary E. Martin ◽  
John D. Aber
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Data Sets ◽  
Independent Data ◽  
Least Squares Regression ◽  
Near Infrared Reflectance ◽  
Regression Methods

Further evaluation of near infrared reflectance spectroscopy as a method for the determination of nitrogen, lignin, and cellulose concentrations in dry, ground, temperate forest woody foliage is presented. A comparison is made between two regression methods, stepwise multiple linear regression and partial least squares regression. The partial least squares method showed consistently lower standard error of calibration and higher R2 values with first and second difference equations. The first difference partial least squares regression equation resulted in standard errors of calibration of 0.106%, with an R2 of 0.97 for nitrogen, 1.613% with an R2 of 0.88 for lignin, and 2.103% with an R2 of 0.89 for cellulose. The four most highly correlated wavelengths in the near infrared region, and the chemical bonds represented, are shown for each constituent and both regression methods. Generalizability of both methods for prediction of protein, lignin, and cellulose concentrations on independent data sets is discussed. Prediction accuracy for independent data sets and species from other sites was increased using partial least squares regression, but was poor for sample sets containing tissue types or laboratory-measured concentration ranges beyond those of the calibration set.

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