Effects of sample preparation on NIR spectroscopic estimation of chemical properties of Eucalyptus urophylla S.T. Blake wood

Hein, Paulo Ricardo Gherardi; Lima, José Tarcísio; Chaix, Gilles

doi:10.1515/hf.2010.011

Cited by 34 publications

(11 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They concluded that size reduction improved the stability of spectra, and consequently, the predictive diagnostics of models developed. Hein et al 16 also had a similar conclusion, but they added that the difference between lumber and milled wood was more significant than the difference between particle sizes of milled wood.…”

Section: Introductionmentioning

confidence: 87%

Non-Destructive Prediction of the Properties of Forest Biomass for Chemical and Bioenergy Applications Using near Infrared Spectroscopy

Via

Fasina

Eckhardt

2015

Journal of Near Infrared Spectroscopy

View full text Add to dashboard Cite

Forest biomass will play a key role as a feedstock for bioproducts as the bioeconomy develops. Rapid assessment of this heterogeneous resource will help determine its suitability as feedstock for specific applications, aid in feedstock improvement programmes and enable better process control that will optimise the biorefinery process. In this study, near infrared spectroscopy coupled with partial least-squares regression was used to predict important chemical and thermal reactivity properties of biomass made up of needles, twigs, branches, bark and wood of Pinus taeda (loblolly pine). Models developed with the raw spectra for property prediction used between three and eight factors to yield R2 values ranging from a low of 0.34 for higher heat values to a high of 0.92 for volatile matter. Pretreating the raw spectra with first derivatives improved the fit statistics for all properties (i.e. min 0.57, max 0.92; with two or three factors). The best-performing models were for extractives, lignin, glucose, cellulose, volatile matter and fixed carbon ( R2 ≥ 0.80, residual predictive deviation/ratio of performance to deviation ≥1.5). This study provided the capacity to predict multiple chemical and thermal/energy traits from a single spectrum across an array of materials that differ considerably in chemistry type and distribution. Models developed should be able to rapidly predict the studied properties of similar biomass types. This will be useful in rapidly allocating feedstocks that optimise biomass conversion technologies.

show abstract

Section: Introductionmentioning

confidence: 87%

Non-Destructive Prediction of the Properties of Forest Biomass for Chemical and Bioenergy Applications Using near Infrared Spectroscopy

Via

Fasina

Eckhardt

2015

Journal of Near Infrared Spectroscopy

View full text Add to dashboard Cite

show abstract

“…The influence of particle size on the NIR spectra is not consensual: some authors stated that the particle size influences the prediction error (the R 2 in lignin prediction of coniferous biomass diminished from 0.96-0.99 to 0.90 in 80 and 20-40 mesh, respectively) [20], whereas others reported that particle size effects are not important, and coarse powder produces calibration as good as fine powder, without any specific variation of R 2 [21]. Furthermore, it is known that spectra pre-processing, such as the use of derivatives, can minimize the effect of sample preparation [22].…”

Section: Methodsmentioning

confidence: 99%

Measuring the chemical composition of waterlogged decayed wood by near infrared spectroscopy

Pecoraro¹,

Pizzo²,

Alves³

et al. 2015

Microchemical Journal

View full text Add to dashboard Cite

“…trees coming from the same experimental plantation were previously evaluated for wood density 27 and chemical composition. 28…”

Section: Methodsmentioning

confidence: 99%

Predicting Microfibril Angle in Eucalyptus Wood from Different Wood Faces and Surface Qualities Using near Infrared Spectra

Hein

Clair

Brancheriau

et al. 2010

Journal of Near Infrared Spectroscopy

Self Cite

View full text Add to dashboard Cite

all rights reserved Microfibril angle (Mfa) is a property of the cell wall of wood fibres, which is made up of millions of strands of cellulose called microfibrils. 1 this elementary wood trait represents the orientation of crystalline cellulose in the cell wall with respect to the stem axis. 2 It is of particular interest for breeding programmes 3,4 since Mfa has major effects on two key properties of wood: its stiffness and longitudinal shrinkage. 5 among all available techniques, only X-ray diffractometry (Xrd) provides quick Mfa measurements for a large number of samples; 6,7 however, sample preparation is often time-consuming. Xrd has been largely used because of the crystalline arrangement of cellulose microfibrils in the wood cell wall; it allows a study of not only its organisation (such as Mfa) but also its apparent crystal size 8 or its mechanical state. 9 numerous papers have proposed near infrared (nIr) spectroscopy to determine Mfa (table 1). nIr spectroscopy is a rapid method for the determination of many chemical properties which have been successfully related to physico-mechanical properties of wood. 26 one of its main advantages is the possibility of estimating a range of wood traits from the same nIr spectra. to explain the results of these established nIr-based calibrations for Mfa, a common assumption is that the supposed correlation that exists between a cIrad-perSySt department-production and processing of tropical Woods, 73 rue Jean-françois Breton ta B-40/16, b laboratoire de Mécanique et Génie civil, cnrS, université Montpellier 2, place e. Bataillon, cc 048, 34095 Montpellier, cedex 5, france c cIrad-BIoS department-Genetic diversity and Breeding of forest Species, 73 rue Jean-françois Breton ta B-40/16, 34398 Montpellier, cedex 5, franceThe microfibril angle (MFA) of crystalline cellulose in the wood cell wall along the stem axis has major effects on stiffness and longitudinal shrinkage of wood and is of key importance to timber quality. The aims of this study were: (1) to develop partial least square (PLS) regression models for microfibril angle (measured on tangential sections by X-ray diffraction) based on NIR spectra measured on tangential and on radial surfaces; (2) to develop PLS regression models for MFA based on radial NIR spectra collected from wood surfaces of different quality; and (3) to verify the reliability of these PLS-R models by external validations. T values were recorded by X-ray diffraction on tangential sections while NIR spectra were taken on tangential and radial wood surfaces. PLS-R calibrations for MFA based on tangential NIR spectra were better (r 2 p = 0.72) than those using radial NIR spectra (r 2 p = 0.64). The key role of the chemical components and the effect of surface quality of wood on NIR spectroscopy calibrations are discussed. Considering the differences between experimental conditions, these findings showed the potential of the NIR-based models for predicting MFA in Eucalyptus wood, even using spectra taken from different wood faces and surface qua...

show abstract

Effects of sample preparation on NIR spectroscopic estimation of chemical properties of Eucalyptus urophylla S.T. Blake wood

Cited by 34 publications

References 43 publications

Non-Destructive Prediction of the Properties of Forest Biomass for Chemical and Bioenergy Applications Using near Infrared Spectroscopy

Non-Destructive Prediction of the Properties of Forest Biomass for Chemical and Bioenergy Applications Using near Infrared Spectroscopy

Measuring the chemical composition of waterlogged decayed wood by near infrared spectroscopy

Predicting Microfibril Angle in Eucalyptus Wood from Different Wood Faces and Surface Qualities Using near Infrared Spectra

Contact Info

Product

Resources

About