FT-Raman, FT-infrared and NIR spectroscopic characterization of oxygen-delignified kraft pulp treated with hydrogen peroxide under acidic and alkaline conditions

Wojciak, A.; Kasprzyk, H; Sikorska, Ewa; Krawczyk, Alina; Sikorski, Marek; Wesełucha–Birczyńska, Aleksandra

doi:10.1016/j.vibspec.2014.01.007

Cited by 19 publications

(9 citation statements)

References 27 publications

(48 reference statements)

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“…The band at approximately 1923 nm is attributable to –OH and –C = O groups 25 . The signal at around 2108 nm is ascribed to the combination of O–H and C–H stretching vibrations 26 . The absorption peak at approximately 2272 nm is assigned to the combination bands of O–H and C–O 8 .…”

Section: Resultsmentioning

confidence: 99%

“…The signal approximately 1725 nm, which appears in the shared characteristic wavelengths for hemicellulose, cellulose and lignin, may relate to the C–H stretching (1st overtone) of –CH 2 21 . The common characteristic wavelengths of 1898 nm and approximately 1927 nm for cellulose and lignin are ascribed to C = O stretching (2nd overtone) of –CO 2 H 21 and the combination of O–H stretching and deformation vibrations 26 , respectively. The shared wavelength around 1996 nm for hemicellulose and lignin may correspond to the combination of O–H stretching and C = O stretching (2nd overtone) 22 .…”

Section: Resultsmentioning

confidence: 99%

“…The shared wavelength around 1996 nm for hemicellulose and lignin may correspond to the combination of O–H stretching and C = O stretching (2nd overtone) 22 . The signal around 2100 nm is connected with the combination of O–H and C–H stretching vibrations for hemicellulose, cellulose and lignin 26 . Wavelengths approximately 2280 nm and 2322 nm of hemicellulose, cellulose and lignin belong to C–H stretching and deformation group frequencies 21 .…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Determination of Hemicellulose, Cellulose and Lignin in Moso Bamboo by Near Infrared Spectroscopy

Liu

Sun

Zhou

et al. 2015

Sci Rep

134

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The contents of hemicellulose, cellulose and lignin are important for moso bamboo processing in biomass energy industry. The feasibility of using near infrared (NIR) spectroscopy for rapid determination of hemicellulose, cellulose and lignin was investigated in this study. Initially, the linear relationship between bamboo components and their NIR spectroscopy was established. Subsequently, successive projections algorithm (SPA) was used to detect characteristic wavelengths for establishing the convenient models. For hemicellulose, cellulose and lignin, 22, 22 and 20 characteristic wavelengths were obtained, respectively. Nonlinear determination models were subsequently built by an artificial neural network (ANN) and a least-squares support vector machine (LS-SVM) based on characteristic wavelengths. The LS-SVM models for predicting hemicellulose, cellulose and lignin all obtained excellent results with high determination coefficients of 0.921, 0.909 and 0.892 respectively. These results demonstrated that NIR spectroscopy combined with SPA-LS-SVM is a useful, nondestructive tool for the determinations of hemicellulose, cellulose and lignin in moso bamboo.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Determination of Hemicellulose, Cellulose and Lignin in Moso Bamboo by Near Infrared Spectroscopy

Liu

Sun

Zhou

et al. 2015

Sci Rep

134

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show abstract

“…Fourier-transform Raman spectroscopy has been the most commonly used instrumental configuration for the analysis of biomass (Agarwal and Atalla, 1993 ; Sene et al, 1994 ; Agarwal and Ralph, 1997 ; Ona et al, 1997 ; Takayama et al, 1997 ; Kacurikova et al, 1998 ; Ona et al, 1998a ; Ona et al, 1998b , c ; Ona et al, 2000 ; Schenzel and Fischer, 2001 ; Sivakesava et al, 2001a ; Sivakesava et al, 2001b ; Kihara et al, 2002 ; Proniewicz et al, 2002 ; Agarwal et al, 2003 ; Ona et al, 2003 ; Cao et al, 2004 ; Vester et al, 2004 ; Agarwal and Kawai, 2005 ; Schenzel et al, 2005 ; Keown et al, 2007 ; Schulz and Baranska, 2007 ; Agarwal and Ralph, 2008 ; Keown et al, 2008 ; Schenzel et al, 2009 ; Agarwal and Atalla, 2010 ; Agarwal et al, 2010 ; Larsen and Barsberg, 2010 ; Agarwal, 2011 ; Agarwal et al, 2011 ; Chundawat et al, 2011 ; Larsen and Barsberg, 2011 ; Sun et al, 2012 ; Agarwal et al, 2013 ; Kim et al, 2013 ; Lupoi et al, 2014a ; Wójciak et al, 2014 ; Lupoi et al, 2015 ). A recent study surveyed three high-throughput vibrational spectrometers (NIR, FTIR, and FT-Raman) to evaluate which was best suited for developing PLS models for predicting lignin S/G ratios (Lupoi et al, 2014a ).…”

Section: Fourier-transform Raman Spectroscopymentioning

confidence: 99%

Evaluating Lignocellulosic Biomass, Its Derivatives, and Downstream Products with Raman Spectroscopy

Lupoi

Gjersing

Davis

2015

Front. Bioeng. Biotechnol.

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The creation of fuels, chemicals, and materials from plants can aid in replacing products fabricated from non-renewable energy sources. Before using biomass in downstream applications, it must be characterized to assess chemical traits, such as cellulose, lignin, or lignin monomer content, or the sugars released following an acid or enzymatic hydrolysis. The measurement of these traits allows researchers to gage the recalcitrance of the plants and develop efficient deconstruction strategies to maximize yields. Standard methods for assessing biomass phenotypes often have experimental protocols that limit their use for screening sizeable numbers of plant species. Raman spectroscopy, a non-destructive, non-invasive vibrational spectroscopy technique, is capable of providing qualitative, structural information and quantitative measurements. Applications of Raman spectroscopy have aided in alleviating the constraints of standard methods by coupling spectral data with multivariate analysis to construct models capable of predicting analytes. Hydrolysis and fermentation products, such as glucose and ethanol, can be quantified off-, at-, or on-line. Raman imaging has enabled researchers to develop a visual understanding of reactions, such as different pretreatment strategies, in real-time, while also providing integral chemical information. This review provides an overview of what Raman spectroscopy is, and how it has been applied to the analysis of whole lignocellulosic biomass, its derivatives, and downstream process monitoring.

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“…The noticeable peak at approximately 5200 cm -1 has been attributed to O-H stretching and C=O stretching (2 nd overtone) for Cel/Hem [17] . The signal at around 4743 cm -1 has been ascribed to the combination band of O-H stretching and C-O stretching for Hem [18,19] . The peak at 4386 cm -1 has been proved to belong to C-H stretching and CH 2 deformation [17] .…”

Section: Discussionmentioning

confidence: 99%

Comparison and rapid prediction of lignocellulose and organic elements of a wide variety of rice straw based on near infrared spectroscopy

Diallo

Yang

Shen

et al. 2019

International Journal of Agricultural and Biological Engineering

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Rice straw is a major kind of biomass that can be utilized as lignocellulosic materials and renewable energy. Rapid prediction of the lignocellulose (cellulose, hemicellulose, and lignin) and organic elements (carbon, hydrogen, nitrogen, and sulfur) of rice straw would help to decipher its growth mechanisms and thereby improve its sustainable usages. In this study, 364 rice straw samples featuring different rice subspecies (japonica and indica), growing seasons (early-, middle-, and late-season), and growing environments (irrigated and rainfed) were collected, the differences among which were examined by multivariate analysis of variance. Statistic results showed that the cellulose content exhibited significant differences among different growing seasons at a significant level (p < 0.01), and the contents of cellulose and nitrogen had significant differences between different growing environments (p < 0.01). Near infrared reflectance spectroscopy (NIRS) models for predicting the lignocellulosic and organic elements were developed based on two algorithms including partial least squares (PLS) and competitive adaptive reweighted sampling-partial least squares (CARS-PLS). Modeling results showed that most CARS-PLS models are of higher accuracy than the PLS models, possibly because the CARS-PLS models selected optimal combinations of wavenumbers, which might have enhanced the signal of chemical bonds and thereby improved the predictive efficiency. As a major contributor to the applications of rice straw, the nitrogen content was predicted precisely by the CARS-PLS model. Generally, the CARS-PLS models efficiently quantified the lignocellulose and organic elements of a wide variety of rice straw. The acceptable accuracy of the models allowed their practical applications.

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FT-Raman, FT-infrared and NIR spectroscopic characterization of oxygen-delignified kraft pulp treated with hydrogen peroxide under acidic and alkaline conditions

Cited by 19 publications

References 27 publications

Determination of Hemicellulose, Cellulose and Lignin in Moso Bamboo by Near Infrared Spectroscopy

Determination of Hemicellulose, Cellulose and Lignin in Moso Bamboo by Near Infrared Spectroscopy

Evaluating Lignocellulosic Biomass, Its Derivatives, and Downstream Products with Raman Spectroscopy

Comparison and rapid prediction of lignocellulose and organic elements of a wide variety of rice straw based on near infrared spectroscopy

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