Prediction of blackwood Kraft pulps yields with wood NIR–PLSR models

The objective of this chapter is to provide a concise overview of lignin composition and structure in different species and materials (wood, barks and nonwood plants). It includes a brief review on the lignin precursors and their polymerization as well as of the analytical tools used for lignin characterization from wet chemical to spectroscopic methods. Wood of gymnosperms is characterized by high lignin content (25-35%) and a HG-type of lignin with more guaiacyl (G) units and a small portion of p-hydroxyphenyl (H) units. Wood of angiosperms has a lignin content of 15-28%, with a GS-lignin having different proportions of syringyl (S) units. Nonwoody monocotyledon species have different lignin content (9-20%) and a HGS type of lignin, characterized by a high proportion of H units. Bark lignin content ranges from 13 to 43% and is of HGS-type with species-specific composition and different in the bark components, phloem and cork. Lignin composition and macromolecular structure are key issues to understand the properties of lignocellulosic materials and to design a lignin-based pathway within biomass biorefineries. The available information on lignin composition is still limited to a few species and plant components. This is certainly an area where more research is needed.

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“…melanoxylon ranged from 21.0 to 28.2% [120,121], whereas in A. mangium the value was 28% [122]. [122].…”

Section: Acaciamentioning

confidence: 99%

Compositional Variability of Lignin in Biomass

Lourenço¹,

Pereira²

2018

Lignin - Trends and Applications

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100

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“…The pulped samples were disintegrated, washed, and screened. Under these conditions the pulp yields ranged from 47.0% to 58.2% [ 15 , 35 ], as related to the variation of heartwood proportion in different wood discs [ 39 ].…”

Section: Methodsmentioning

confidence: 99%

Fibre Morphological Characteristics of Kraft Pulps of Acacia melanoxylon Estimated by NIR-PLS-R Models

2015

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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 Morfi® 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 μm) and validation (RMSEP (root mean square error of prediction) of 0.007 mm and 0.36 μm) 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.

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“…2). Thus, multivariate statistical methods are required to create calibration models for the chemical composition and/or physical properties of wood (Schwanninger et al 2011) Some studies utilizing NIR spectral data to create multivariate models for predicting yield (or mass loss) or different chemical components of wood have employed specific wavenumber ranges or bands (Esteves and Pereira 2008;Jiang et al 2014;McLean et al 2014;Pulkka et al 2016;Santos et al 2016). In such cases, for example, for modeling the lignin content, the wavenumber range from 5,400 cm -1 to 6,900 cm -1 or parts of it have been selected.…”

Section: Multivariate Modelsmentioning

confidence: 99%

Characterization of pre-treatments on wood chips prior to delignification by near infrared spectroscopy

Louhelainen

Lehto

Klosinska

et al. 2017

BioRes

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A near infrared (NIR) spectroscopy-based method for predicting yields and lignin contents of differently pre-treated silver/white birch (Betula pendula/B. pubescens) and Scots pine (Pinus sylvestris) chips was developed. The approach was to create multivariate calibration models from the NIR data by the partial least squares (PLS) method. Both parameters are important factors when adjusting adequate conditions for pre-treatments either with hot-water (HW) as such and slightly acidified HW (collectively referred to as autohydrolysis) or dilute alkaline aqueous solutions prior to alkaline pulping. Pre-treatment conditions were varied with respect to temperature (130 °C and 150 °C) and treatment time (from 30 min to 120 min). In the case of alkaline pre-treatments, the NaOH charge was 1% to 8% NaOH on wood dry solids (DS). The yields varied in the range 81.2% to 99.3% (in autohydrolyses) and 83.5% to 97.9% (in alkaline pre-treatments). High correlation coefficients and low prediction errors in relation to conventional yield and lignin content data clearly indicated the suitability of NIR spectroscopy combined with the multivariate modeling as an effective and fast tool for this purpose. This technique also showed promising possibilities for developing practical process control methods to follow such pre-treatments.

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Prediction of blackwood Kraft pulps yields with wood NIR–PLSR models

Cited by 8 publications

References 32 publications

Compositional Variability of Lignin in Biomass

Compositional Variability of Lignin in Biomass

Fibre Morphological Characteristics of Kraft Pulps of Acacia melanoxylon Estimated by NIR-PLS-R Models

Characterization of pre-treatments on wood chips prior to delignification by near infrared spectroscopy

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