Fibre morphological characteristics of Kraft pulps of Acacia melanoxylon estimated by NIR-PLS-R models

article

Pereira, H.
Santos, A.J.A.
Anjos, O.

PEREIRA, H. ; SANTOS, A.J.A. ; ANJOS, O. (2016) - Fibre morphological characteristics of Kraft pulps of Acacia melanoxylon estimated by NIR-PLS-R models. Materials. 9:8. doi:10.3390/ma9010008 .

doi:10.3390/ma9010008

Fibre morphological characteristics of Kraft pulps of Acacia melanoxylon estimated by NIR-PLS-R models

Acacia melanoxylon
Kraft pulps
Fibre length and width
NIR

2016-02-18T15:59:54Z
2016-02-18T15:59:54Z
2016

In this paper, the morphological properties of fiber length (weighted in length) and of fiber width of unbleached Kraft pulp of Acacia melanoxylon were determined using TECHPAP Morfir equipment (Techpap SAS, Grenoble, France), and were used in the calibration development of Near Infrared (NIR) partial least squares regression (PLS-R) models based on the spectral data obtained for the wood. It is the first time that fiber length and width of pulp were predicted with NIR spectral data of the initial woodmeal, with high accuracy and precision, and with ratios of performance to deviation (RPD) fulfilling the requirements for screening in breeding programs. The selected models for fiber length and fiber width used the second derivative and first derivative + multiplicative scatter correction (2ndDer and 1stDer + MSC) pre-processed spectra, respectively, in the wavenumber ranges from 7506 to 5440 cm 1. The statistical parameters of cross-validation (RMSECV (root mean square error of cross-validation) of 0.009 mm and 0.39 m) and validation (RMSEP (root mean square error of prediction) of 0.007 mm and 0.36 m) with RPDTS (ratios of performance to deviation of test set) values of 3.9 and 3.3, respectively, confirmed that the models are robust and well qualified for prediction. This modeling approach shows a high potential to be used for tree breeding and improvement programs, providing a rapid screening for desired fiber morphological properties of pulp prediction.

openAccess
http://creativecommons.org/licenses/by/4.0/

eng