Compositional analysis of biomass feedstocks by near infrared reflectance spectroscopy

Sanderson, Matt A.; Agblevor, Foster A.; Collins, Michael; Johnson, David K.

doi:10.1016/s0961-9534(96)00039-6

Cited by 115 publications

(112 citation statements)

References 7 publications

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“…Switchgrass ethanol yield is improved with greater carbohydrate fractions, fewer lignin constituents, and less metals [9,34]. The major fermentable sugars in hydrolyzed grass biomass are glucose and xylose, while arabinose, galactose, and mannose contribute significantly less [1,7,10,13,27,35]. Therefore, simple conversion calculations can be used to predict ethanol yield using the hexose (glucose, mannose, and galactose) and pentose (xylose and arabinose) sugars from hydrolyzed switchgrass biomass cell walls [6,35].…”

Section: Introductionmentioning

confidence: 99%

Switchgrass Biofuel Production on Reclaimed Surface Mines: II. Feedstock Quality and Theoretical Ethanol Production

et al. 2015

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Switchgrass (Panicum virgatum L.), a warmseason perennial grass, is an important bioenergy crop candidate because it produces high biomass yields on marginal lands and on reclaimed surface mined sites. In companion studies, dry matter (DM) yields for Cave-inRock, Shawnee, and Carthage cultivars varied from 4.2 to 13.0 Mg ha −1 averaged over 6 years at the reclaimed Hampshire site, and fertilization increased yields of Cave-in-Rock at Black Castle and Coal Mac sites from 0.3 to 2 Mg ha −1 during the first 3 years. The objective of these experiments was to compare the impacts of cultivar and soil amendments on biomass quality and theoretical ethanol production of switchgrass grown on surface mines with differing soil characteristics. Biomass quality was determined for fiber, ash, lignin, digestibility, and carbohydrate contents via near-infrared reflectance spectroscopy, and carbohydrates were used to calculate theoretical ethanol yield (TEY; L Mg −1 ) and multiplied by biomass yield to calculate theoretical ethanol production (TEP; L ha −1 ). Cultivars at the Hampshire site did not differ in TEY and ranged from 426 to 457 L Mg −1 . Theoretical ethanol production from Cave-in-Rock at Hampshire was 7350 L ha −1 , which was higher than other cultivars because of its greater biomass production. This TEP was higher than in other studies which predicted 4000 to 5000 L ha −1 . At the Black Castle and Coal Mac sites, fertilizer applications slightly affected biomass quality of switchgrass and TEY, but provided greater TEP as a function of increased yield. Similar to other findings, total switchgrass biomass production has more impact than compositional differences on TEP, so maximizing biomass production is critical for maximizing potential biofuel production. With appropriate soil substrates, fertilization, planning, and management, large areas of reclaimed surface mines can be converted to switchgrass stands to produce high biomass quality and yields to support a bioethanol industry.

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Section: Introductionmentioning

confidence: 99%

Switchgrass Biofuel Production on Reclaimed Surface Mines: II. Feedstock Quality and Theoretical Ethanol Production

et al. 2015

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“…Rapid compositional analysis methods based on near-infrared (NIR) reflectance spectroscopy combined with multivariate statistics are well-established and widely used in agriculture [13][14][15]. Rapid compositional analysis methods have been developed for a number of different potential bioenergy feedstocks [16][17][18]. The goal of this work was to develop an NIR calibration model for sorghum as a rapid analysis tool for sorghum researchers.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Calibration Models for Sorghum Composition using Near-Infrared Spectroscopy

Wolfrum¹,

Payne²,

Stefaniak³

et al. 2013

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We have developed calibration models based on near-infrared (NIR) spectroscopy coupled with multivariate statistics to predict compositional properties relevant to cellulosic biofuels production for a variety of sorghum cultivars. A robust calibration population was developed in an iterative fashion. The quality of models developed using the same sample geometry on two different types of NIR spectrometers and two different sample geometries on the same spectrometer did not vary greatly.iv List of Acronyms

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“…This technology has been used for the analysis of biomass feedstock properties. Sanderson et al [3] used the NIRS to determine the chemical compositions of several woody and herbaceous feedstocks, such as ethanol extractives, ash, and lignin. Labbé et al [4] found the orthogonal signal correction-(OSC-) treated kernel PLS method achieved highest coefficient of correlation and lowest root-mean square of error (RMSE) for the prediction of ash and char content of three types of woody biomass (red oak, yellow poplar, hickory) and three herbaceous biomasses (switch grass, corn stover, sugarcane bagasse).…”

Section: Introductionmentioning

confidence: 99%

Characterization of Biochar Properties Affected by Different Pyrolysis Temperatures Using Visible-Near-Infrared Spectroscopy

Yang

Sheng

2012

ISRN Spectroscopy

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Rapid characterization of biochar for energy and ecological purpose utilization is fundamental. In this work, visible and nearinfrared (vis-NIR) spectroscopy was used to measure ash, volatile matter, fixed carbon contents, and calorific value of three types of biochar produced from pine wood, cedar wood, and cotton stalk, respectively. The vis-NIR spectroscopy was also used to discriminate biochar feedstock types and pyrolysis temperature. Prediction result shows that partial least squares (PLS) regression calibrating the spectra to the values of biochar properties achieved very good or excellent performance with coefficient of determination (R 2 ) of 0.86∼0.91 and residual prediction deviation (RPD) of 2.58∼3.32 for ash, volatile matter, and fixed carbon, and good prediction with R 2 of 0.81 and RPD of 2.30 for calorific value. Linear discrimination analysis (LDA) of the principal components (PCs) produced from PCA of wavelength matrix shows that three types of biochar can be successfully discriminated with 95.2% accuracy. The classification of biochar with different pyrolysis temperatures can be conducted with 69% accuracy for all three types and 100% accuracy for single type of cotton stalk. This experiment suggests that the vis-NIR spectroscopy is promising as an alternative of traditionally quantitative and qualitative analysis of biochar properties.

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Compositional analysis of biomass feedstocks by near infrared reflectance spectroscopy

Cited by 115 publications

References 7 publications

Switchgrass Biofuel Production on Reclaimed Surface Mines: II. Feedstock Quality and Theoretical Ethanol Production

Switchgrass Biofuel Production on Reclaimed Surface Mines: II. Feedstock Quality and Theoretical Ethanol Production

Multivariate Calibration Models for Sorghum Composition using Near-Infrared Spectroscopy

Characterization of Biochar Properties Affected by Different Pyrolysis Temperatures Using Visible-Near-Infrared Spectroscopy

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