Rapid assessment of coniferous biomass lignin–carbohydrates with near-infrared spectroscopy

Jiang, Wei; Han, Guangting; Via, Brian K.; Tu, Maobing; Liu, Wei; Fasina, Oladiran

doi:10.1007/s00226-013-0590-3

Cited by 35 publications

(29 citation statements)

References 24 publications

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“…In predicting cellulose content, He and Hu (2013) found that R 2 P and RPD were over 0.91 and 5, respectively, for wood meal from various wood species. Meanwhile, Jiang et al (2013) found that the cellulose content of coniferous wood species possessed R 2 P and RPD values of 0.93 and 2.22, respectively. Lower values were found for the hemicellulose content, with R 2 P = 0.84 and RPD = 0.93.…”

Section: Resultsmentioning

confidence: 98%

“…NIRS is based on the selective absorption of light by chemical compounds (Baillères et al 2002), and it can be used to estimate wood chemical properties (Tsuchikawa 2007;Tsuchikawa and Schwanninger 2011). As a fast and reliable method for determining chemical components in wood, NIRS has been successfully applied in various wood species (Raymond and Schimleck 2002;Poke and Raymond 2006;Hou and Li 2010;He and Hu 2013;Jiang et al 2013;Via et al 2014); it has also been used to determine the physico-chemical properties of wood pulp (Fardim et al 2002;Zhang et al 2011). Several studies related to the use of NIRS analysis have been done in China to estimate the lignin content and to determine the kappa number of pulp and pulping yield (Zhang et al 2011) of Acacia wood.…”

Section: Introductionmentioning

confidence: 96%

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Near infrared (NIR) spectroscopy for estimating the chemical composition of (Acacia mangium Willd.) wood

Karlinasari

Sabed

Wistara

et al. 2014

J Indian Acad Wood Sci

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Research on wood technological properties using near infrared (NIR) spectroscopy has shown promising results. The aim of this study was to evaluate the efficiency of NIR spectroscopy for estimating chemical properties of mangium wood (Acacia mangium). NIR spectra were obtained from 150 wood meal samples of mangium trees that were 5-7-years-old. A multivariate data analysis method of partial least squares was used to develop calibration regression models for predicting chemical properties based on NIR spectra. The results showed a good relationship between values derived from laboratory analyses and those predicted by NIR spectroscopy for a-cellulose and hemicellulose content. The calibration models had high values for the coefficient of determination (R 2 [ 0.80) and the ratio of performance to deviation (RPD [ 2.0). Meanwhile, lignin and extractive content were poorly predicted; calibration validation revealed R 2 \ 0.60 and RPD = 1.0. This study indicated that NIR spectroscopy analysis on wood meal of A. mangium could be reliably used to predict a-cellulose and hemicellulose.

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Section: Resultsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 96%

Near infrared (NIR) spectroscopy for estimating the chemical composition of (Acacia mangium Willd.) wood

Karlinasari

Sabed

Wistara

et al. 2014

J Indian Acad Wood Sci

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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: 97%

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

Pecoraro¹,

Pizzo²,

Alves³

et al. 2015

Microchemical Journal

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“…Other applications of NIR spectroscopy coupled with multivariate analysis for determining total lignin content extend to Acacia [259,273], aspen [276], E. globulus [264], larch [259], poplar [259], and southern [261] and Turkish pine [270], to name a few.…”

Section: Near-infrared Spectroscopymentioning

confidence: 99%

Recent innovations in analytical methods for the qualitative and quantitative assessment of lignin

Lupoi

Singh

Parthasarathi

et al. 2015

Renewable and Sustainable Energy Reviews

296

188

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a b s t r a c tAs the attraction of creating biofuels and bio-based chemicals from lignocellulosic biomass has increased, researchers have been challenged with developing a better understanding of lignin structure, quantity and potential uses. Lignin has frequently been considered a waste-product from the deconstruction of plant cell walls, in attempts to isolate polysaccharides that can be hydrolyzed and fermented into fuel or other valuable commodities. In order to develop useful applications for lignin, accurate analytical instrumentation and methodologies are required to qualitatively and quantitatively assess, for example, what the structure of lignin looks like or how much lignin comprises a specific feedstock's cellular composition. During the past decade, various diverse strategies have been employed to elucidate the structure and composition of lignin. These techniques include using two-dimensional nuclear magnetic resonance to resolve overlapping spectral data, measuring biomass with vibrational spectroscopy to enable modeling of lignin content or monomeric ratios, methods to probe and quantify the linkages between lignin and polysaccharides, or refinements of established methods to provide higher throughput analyses, less use of consumables, etc. This review seeks to provide a comprehensive overview of many of the advancements achieved in evaluating key lignin attributes. Emphasis is placed on research endeavored in the last decade.

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Rapid assessment of coniferous biomass lignin–carbohydrates with near-infrared spectroscopy

Cited by 35 publications

References 24 publications

Near infrared (NIR) spectroscopy for estimating the chemical composition of (Acacia mangium Willd.) wood

Near infrared (NIR) spectroscopy for estimating the chemical composition of (Acacia mangium Willd.) wood

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

Recent innovations in analytical methods for the qualitative and quantitative assessment of lignin

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