Quantitative characterization of lignocellulosic biomass using surrogate mixtures and multivariate techniques

Krasznai, Daniel J.; Champagne, Pascale; Cunningham, Michael F.

doi:10.1016/j.biortech.2012.01.089

Cited by 22 publications

(17 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The cellulose signals found in the loading plots were the signals at 898 (Β-D-cellulose), 1,030, and 1,050 (intense polysaccharide), and 1,090 cm -1 (cellulose II and amorphous cellulose) [10,22]. These points were perceived in PRIN1 and PRIN2 loading plots; in PRIN1, 898 and 1,090 cm -1 were minimums, while 1,030 and 1,050 cm -1 were maximums, which is an opposite behavior compared with PRIN2.…”

Section: Principal Component Analysis Of the Ft-ir Spectrummentioning

confidence: 99%

Analysis of alkali ultrasonication pretreatment in bioethanol production from cotton gin trash using FT-IR spectroscopy and principal component analysis

Plácido

Capareda

2014

Bioresour. Bioprocess.

View full text Add to dashboard Cite

Background: Cotton gin trash (CGT) is a lignocellulosic residue that can be used in the production of cellulosic ethanol. In a previous research, the sequential use of ultrasonication, liquid hot water, and ligninolytic enzymes was selected as pretreatment for the production of ethanol from CGT. However, an increment in the ethanol production is necessary. To accomplish that, this research evaluated the effect of pretreating CGT using alkaline ultrasonication before a liquid hot water and ligninolytic enzymes pretreatments for ethanol production. Three NaOH concentrations (5%, 10%, and 15%) were employed for the alkaline ultrasonication. Additionally, this work is one of the first applications of Fourier transform infrared (FT-IR) spectrum and principal component analysis (PCA) as fast methodology to identify the differences in the biomass after different types of pretreatments. Results: The three concentrations employed for the alkaline ultrasonication pretreatment produced ethanol yields and cellulose conversions higher than the experiment without NaOH. Furthermore, 15% NaOH concentration achieved twofold increment yield versus the treatment without NaOH. The FT-IR spectrum confirmed modifications in the CGT structure in the different pretreatments. PCA was helpful to determine differences between the pretreated and un-pretreated biomass and to evaluate how the CGT structure changed after each treatment. Conclusions: The combination of alkali ultrasonication hydrolysis, liquid hot water, and ligninolytic enzymes using 15% of NaOH improved 35% the ethanol yield compared with the original treatment. Additionally, we demonstrated the use of PCA to identify the modifications in the biomass structure after different types of pretreatments and conditions.

show abstract

Section: Principal Component Analysis Of the Ft-ir Spectrummentioning

confidence: 99%

Analysis of alkali ultrasonication pretreatment in bioethanol production from cotton gin trash using FT-IR spectroscopy and principal component analysis

Plácido

Capareda

2014

Bioresour. Bioprocess.

View full text Add to dashboard Cite

show abstract

“…Compared to wet chemistry, infrared spectroscopy provides a rapid, simple, and nondestructive method for the classification and determination of biomass composition . Several studies have shown that the compositional variation due to variety, maturity, and process impact could be characterized and/or predicted by near‐infrared (NIR) spectroscopy or mid‐infrared (MIR) spectroscopy . Infrared analysis of lignocellulosic biomass is usually based on spectra data generated from ground raw biomass, which includes the nonstructural and structural fractions .…”

Section: Introductionmentioning

confidence: 99%

“…Infrared spectra can be used to classify plant samples and/or to predict all dependent variables (such as plant composition) at one go using supervised method such as partial least square. The latter methods have been used to determine the biomass chemical composition . However, these methods need large data set to produce a robust model of prediction, which can be used for predicting new sample compositions.…”

Section: Introductionmentioning

confidence: 99%

“…[6][7][8] Several studies have shown that the compositional variation due to variety, maturity, and process impact could be characterized and/or predicted by near-infrared (NIR) spectroscopy [9][10][11][12] or mid-infrared (MIR) spectroscopy. 13,14 Infrared analysis of lignocellulosic biomass is usually based on spectra data generated from ground raw biomass, which includes the nonstructural and structural fractions. 10 Few studies have addressed the contribution of extractives to the variance of the infrared spectra, but extractives have been reported to cause spectral interference that can be eliminated by numerical processing of the spectra.…”

mentioning

confidence: 99%

“…The latter methods have been used to determine the biomass chemical composition. [11][12][13]17 However, these methods need large data set to produce a robust model of prediction, which can be used for predicting new sample compositions. Another possibility is the application of multivariate unsupervised analysis to spectra, such as principal component analysis, which is often used as the first step for data reduction to replace infrared spectra with a few principal components in the subsequent analyses.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Classification of lignocellulosic biomass by weighted‐covariance factor fuzzy C‐means clustering of mid‐infrared and near‐infrared spectra

Rammal

Perrin

Vrabie

et al. 2017

Journal of Chemometrics

View full text Add to dashboard Cite

The analysis of lignocellulosic materials is crucial to optimizing the conversion efficiencies in biorefineries and to studying crop residue input to soil nutrient cycles. Mid-infrared (MIR) and near-infrared (NIR) spectroscopies are rapid, simple, and nondestructive methods for the determination of biomass compositions. However, the analysis of a small set of plant biomass is not generally possible with conventional methods of data processing, such as partial least squares. Additionally, IR spectra do not distribute spherically in the data space. To overcome these problems, we propose a weighted-covariance factor fuzzy C-means clustering method combined with bootstrapping. The algorithm can classify spherical and nonspherical clusters, in contrast to classic fuzzy C-means, which is only adapted to spherical clusters. Bootstrapping enables resampling of the available spectra to generate several datasets on which the classification is performed. This unsupervised clustering methodology was tested to classify a small set of maize roots in soil according to genotype or period of their biodegradation process based on their NIR and MIR spectra. This methodology is applied to determine the optimal pretreatment of IR spectra, to study the contribution of the combination of MIR and NIR spectra and to compare the results on spectral and chemical data. The results show that the best methods of pretreatment are the first-order Savitzky-Golay derivative followed by standard normal variate. The MIR spectra produce a better result than NIR spectra for the initial characterization and for dynamic samples, while MIR spectra acquired on raw samples, without soluble extraction, provided better classification than wet chemistry

show abstract

Expression and characterization of a cellobiohydrolase (CBH7B) from the thermophilic fungus Thielavia terrestris in Pichia pastoris

Woon

Mackeen

Mahadi

et al. 2015

Biotech and App Biochem

View full text Add to dashboard Cite

The gene encoding a cellobiohydrolase 7B (CBH7B) of the thermophilic fungus Thielavia terrestris was identified, subcloned, and expressed in Pichia pastoris. CBH7B encoded 455 amino acid residues with a molecular mass of 51.8 kDa. Domain analysis indicated that CBH7B contains a family 7 glycosyl hydrolase catalytic core but lacks a carbohydrate-binding module. Purified CBH7B exhibited optimum catalytic activity at pH 5.0 and 55 °C with 4-methylumbelliferryl-cellobioside as the substrate and retained 85% of its activity following 24 H incubation at 50 °C. Despite the lack of activity toward microcrystalline substrates, this enzyme worked synergistically with the commercial enzyme cocktail Cellic CTec2 to enhance saccharification by 39% when added to a reaction mixture containing 0.25% alkaline pretreated oil palm empty fruit bunch (OPEFB). Attenuated total reflectance Fourier transform infrared spectroscopy suggested a reduction of lignin and crystalline cellulose in OPEFB samples supplemented with CBH7B. Scanning electron microscopy revealed greater destruction extent of OPEFB strands in samples supplemented with CBH7B as compared with the nonsupplemented control. Therefore, CBH7B has the potential to complement commercial enzymes in hydrolyzing lignocellulosic biomass.

show abstract

Quantitative characterization of lignocellulosic biomass using surrogate mixtures and multivariate techniques

Cited by 22 publications

References 33 publications

Analysis of alkali ultrasonication pretreatment in bioethanol production from cotton gin trash using FT-IR spectroscopy and principal component analysis

Analysis of alkali ultrasonication pretreatment in bioethanol production from cotton gin trash using FT-IR spectroscopy and principal component analysis

Classification of lignocellulosic biomass by weighted‐covariance factor fuzzy C‐means clustering of mid‐infrared and near‐infrared spectra

Expression and characterization of a cellobiohydrolase (CBH7B) from the thermophilic fungus Thielavia terrestris in Pichia pastoris

Contact Info

Product

Resources

About