Determination of Eucalyptus Globulus Wood Extractives Content by near Infrared-Based Partial Least Squares Regression Models: Comparison between Extraction Procedures

2012 ◽  
Vol 20 (2) ◽  
pp. 275-285 ◽  
Author(s):  
Ana M.M. Alves ◽  
Rita F.S. Simões ◽  
Claudia A. Santos ◽  
Brad M. Potts ◽  
José Rodrigues ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Eucalyptus Globulus ◽  
Regression Models ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Least Squares Regression ◽  
Wood Extractives ◽  
Models Comparison

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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