FT–Raman Investigation of Milled-Wood Lignins: Softwood, Hardwood, and Chemically Modified Black Spruce Lignins

Agarwal, Umesh P.; McSweeny, James D.; Ralph, Sally A.

doi:10.1080/02773813.2011.562338

Cited by 174 publications

(163 citation statements)

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“…This explains why the Raman crystallinity of bleached aspen, unlike those of spruce and pine, is still somewhat higher (Table 4). Hardwood lignin and hemicelluloses are known to have contributions at or near band positions used in the calculation of Raman crystallinity (Agarwal et al 1999;Agarwal and Ralph 1997). In any case, for hardwoods, our findings point out the necessity of further investigation so that the roles of wood fluorescence, lignin, and hemicelluloses can be better understood.…”

Section: Other Samplesmentioning

confidence: 74%

Cellulose I crystallinity determination using FT–Raman spectroscopy: univariate and multivariate methods

2010

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Two new methods based on FT-Raman spectroscopy, one simple, based on band intensity ratio, and the other using a partial least squares (PLS) regression model, are proposed to determine cellulose I crystallinity. In the simple method, crystallinity in cellulose I samples was determined based on univariate regression that was first developed using the Raman band intensity ratio of the 380 and 1,096 cm -1 bands. For calibration purposes, 80.5% crystalline and 120-min milled (0% crystalline) Whatman CC31 and six cellulose mixtures produced with crystallinities in the range 10.9-64% were used. When intensity ratios were plotted against crystallinities of the calibration set samples, the plot showed a linear correlation (coefficient of determination R 2 = 0.992). Average standard error calculated from replicate Raman acquisitions indicated that the cellulose Raman crystallinity model was reliable. Crystallinities of the cellulose mixtures samples were also calculated from X-ray diffractograms using the amorphous contribution subtraction (Segal) method and it was found that the Raman model was better. Additionally, using both Raman and X-ray techniques, sample crystallinities were determined from partially crystalline cellulose samples that were generated by grinding Whatman CC31 in a vibratory mill. The two techniques showed significant differences. In the second approach, successful Raman PLS regression models for crystallinity, covering the 0-80.5% range, were generated from the ten calibration set Raman spectra. Both univariateRaman and WAXS determined crystallinities were used as references. The calibration models had strong relationships between determined and predicted crystallinity values (R 2 = 0.998 and 0.984, for univariateRaman and WAXS referenced models, respectively). Compared to WAXS, univariate-Raman referenced model was found to be better (root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) values of 6.1 and 7.9% vs. 1.8 and 3.3%, respectively). It was concluded that either of the two Raman methods could be used for cellulose I crystallinity determination in cellulose samples.

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Section: Other Samplesmentioning

confidence: 74%

Cellulose I crystallinity determination using FT–Raman spectroscopy: univariate and multivariate methods

2010

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“…In accordance with the lignin staining data, the lignin content in interfascicular fibers (based on peak intensities for the lignin aromatic stretching at 1,597 cm 21 ) of ccr1-6 and ccr1-6 ProSNBE:CCR1 lines was reduced drastically when compared with the wild type. Another difference was observed for the peak at 1,657 cm 21 , which is assigned mainly to coniferyl alcohol (C=C stretching of coniferyl alcohol and C=O stretching of coniferaldehyde; Agarwal et al, 2011). Whereas the wild type showed a high intensity for this peak in vessels, xylary fibers, and interfascicular fibers, it was absent in all regions of ccr1-6.…”

Section: Prosnbe Confers Vessel-specific Expression In Both the Protomentioning

confidence: 90%

Vessel-Specific Reintroduction of CINNAMOYL-COA REDUCTASE1 (CCR1) in Dwarfed ccr1 Mutants Restores Vessel and Xylary Fiber Integrity and Increases Biomass

et al. 2017

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Lignocellulosic biomass is recalcitrant toward deconstruction into simple sugars due to the presence of lignin. To render lignocellulosic biomass a suitable feedstock for the bio-based economy, plants can be engineered to have decreased amounts of lignin. However, engineered plants with the lowest amounts of lignin exhibit collapsed vessels and yield penalties. Previous efforts were not able to fully overcome this phenotype without settling in sugar yield upon saccharification. Here, we reintroduced CINNAMOYL-COENZYME A REDUCTASE1 (CCR1) expression specifically in the protoxylem and metaxylem vessel cells of Arabidopsis (Arabidopsis thaliana) ccr1 mutants. The resulting ccr1 ProSNBE:CCR1 lines had overcome the vascular collapse and had a total stem biomass yield that was increased up to 59% as compared with the wild type. Raman analysis showed that monolignols synthesized in the vessels also contribute to the lignification of neighboring xylary fibers. The cell wall composition and metabolome of ccr1 ProSNBE:CCR1 still exhibited many similarities to those of ccr1 mutants, regardless of their yield increase. In contrast to a recent report, the yield penalty of ccr1 mutants was not caused by ferulic acid accumulation but was (largely) the consequence of collapsed vessels. Finally, ccr1 ProSNBE:CCR1 plants had a 4-fold increase in total sugar yield when compared with wild-type plants.

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“…Considering that the Raman spectra of lignins (syringyl, guaiacyl, and coumaryl) are complex, significant advances have been made to interpret them. This has come about as a result of studying, with a number of approaches, not only lignins (native [18], milled wood [18,42,43], and residual lignins [37,[44][45][46][47]) and their models (deuterated and normal dehydrogenation polymer [DHP] lignins [48] and a large number of lignin model compounds [38,49]) but also chemic ally modified (e.g., bleaching, hydrogenation, and acetylation) lignins and lignocellulosics [10,39,43,50,51]. An additional tool in the aid to interpretation has been the theoretical cal culations on lignin models wherein some degree of preliminary work has been done [52,53], but a lot more remains.…”

Section: Wood Componentsmentioning

confidence: 99%

Raman Spectroscopic Characterization of Wood and Pulp Fibers

Agarwal

2008

Characterization of Lignocellulosic Materials

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This chapter reviews applications of Raman spectroscopy in the field of wood and pulp fibers. Most of the literature examined was published between 1998 and 2006. In addi tion to introduction, this chapter contains sections on wood and components, mechanical pulp, chemical pulp, modified/treated wood, cellulose I crystallinity of wood fibers, and the "self-absorption" phenomenon in near-infrared (IR) Fourier-transform (FT)-Raman spectroscopy. When needed, the sections are further categorized into various subsections. For example, the section on wood and components contains a variety of topics ranging from Raman band assignments to molecular changes during tensile deformation. From this review it will become clear that Raman spectroscopy has become an essential analytical technique in the field of wood and pulp fibers. IntroductionTechniques that can provide information on the chemical constitution of wood and pulp fibers and on processing-related changes of these materials are of great interest. Raman spectroscopy [1] is one such technique that provides fundamental knowledge on a molecu lar level and does not require chemical treatment of the sample (contrary to the case, for example, in fluorescence and electron microscopy). It has become an important analyt ical technique for nondestructive, qualitative, and quantitative analysis of materials. The technique is useful in a number of areas, including analytical measurements, mechan istic studies, and structural determinations. Studying a sample is very simple and usually involves sampling an area of interest by directing a laser excitation beam and analyzing the collected molecularly specific scattered light [2]. Although initially, obtaining good-quality Raman data required operator skill and training, this has started to change as a result of the commercial availability of compact, easy-to-use, integrated instruments at reasonable cost.Nevertheless, although the specificity of Raman spectroscopy is very high, its sensitivity is somewhat poor. Considering that only a small number of the incident laser photons are inelastically scattered, the detection of analytes present in very low concentrations is limited. To overcome this problem, special Raman signal-enhancing techniques can be applied. The two most prominent approaches are the resonance Raman effect [3] and the

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FT–Raman Investigation of Milled-Wood Lignins: Softwood, Hardwood, and Chemically Modified Black Spruce Lignins

Cited by 174 publications

References 40 publications

Cellulose I crystallinity determination using FT–Raman spectroscopy: univariate and multivariate methods

Cellulose I crystallinity determination using FT–Raman spectroscopy: univariate and multivariate methods

Vessel-Specific Reintroduction of CINNAMOYL-COA REDUCTASE1 (CCR1) in Dwarfed ccr1 Mutants Restores Vessel and Xylary Fiber Integrity and Increases Biomass

Raman Spectroscopic Characterization of Wood and Pulp Fibers

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