Applications of near-infrared spectroscopy (NIRS) in biomass energy conversion processes: A review

Škvařil, Jan; Kyprianidis, Konstantinos; Dahlquist, Erik

doi:10.1080/05704928.2017.1289471

Cited by 57 publications

(25 citation statements)

References 189 publications

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“…Its use for the prediction of biomass composition in a biorefinery context originated later (Sanderson et al, 1996;Hames et al, 2003). An overview of the technique and a comprehensive review of its use in biomass conversion processes was recently published (Skvaril et al, 2017). Rapid analysis using NIR is considered a secondary analytical method, because NIR spectra are correlated with primary biomass compositional analysis data using multivariate calibration algorithms to produce a calibration equation.…”

Section: Nir Spectroscopy For Structural Componentsmentioning

confidence: 99%

High Throughput Screening Technologies in Biomass Characterization

et al. 2018

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Biomass analysis is a slow and tedious process and not solely due to the long generation time for most plant species. Screening large numbers of plant variants for various geno-, pheno-, and chemo-types, whether naturally occurring or engineered in the lab, has multiple challenges. Plant cell walls are complex, heterogeneous networks that are difficult to deconstruct and analyze. Macroheterogeneity from tissue types, age, and environmental factors makes representative sampling a challenge and natural variability generates a significant range in data. Using high throughput (HTP) methodologies allows for large sample sets and replicates to be examined, narrowing in on more precise data for various analyses. This review provides a comprehensive survey of high throughput screening as applied to biomass characterization, from compositional analysis of cell walls by NIR, NMR, mass spectrometry, and wet chemistry to functional screening of changes in recalcitrance via HTP thermochemical pretreatment coupled to enzyme hydrolysis and microscale fermentation. The advancements and development of most high-throughput methods have been achieved through utilization of state-of-the art equipment and robotics, rapid detection methods, as well as reduction in sample size and preparation procedures. The computational analysis of the large amount of data generated using high throughput analytical techniques has recently become more sophisticated, faster and economically viable, enabling a more comprehensive understanding of biomass genomics, structure, composition, and properties. Therefore, methodology for analyzing large datasets generated by the various analytical techniques is also covered.

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Section: Nir Spectroscopy For Structural Componentsmentioning

confidence: 99%

High Throughput Screening Technologies in Biomass Characterization

et al. 2018

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“…By contrast, the non-destructive spectroscopy techniques provides analytical information without damaging the sample and in most of the cases, in addition, sample treatment is not required. Vibrational techniques such as IR spectroscopy are amongst the non-destructive spectroscopy techniques that have attracted more interest in the last years [15,16]. Additionally, infrared spectroscopy techniques are fast, accurate, and low-cost analytical methods with high potential in biomass composition analysis [17,18].…”

Section: Introductionmentioning

confidence: 99%

Determination of the Lignocellulosic Components of Olive Tree Pruning Biomass by Near Infrared Spectroscopy

et al. 2019

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The determination of chemical composition of lignocellulose biomass by wet chemistry analysis is labor-intensive, expensive, and time consuming. Near infrared (NIR) spectroscopy coupled with multivariate calibration offers a rapid and no-destructive alternative method. The objective of this work is to develop a NIR calibration model for olive tree lignocellulosic biomass as a rapid tool and alternative method for chemical characterization of olive tree pruning over current wet methods. In this study, 79 milled olive tree pruning samples were analyzed for extractives, lignin, cellulose, hemicellulose, and ash content. These samples were scanned by reflectance diffuse near infrared techniques and a predictive model based on partial least squares (PLS) multivariate calibration method was developed. Five parameters were calibrated: Lignin, cellulose, hemicellulose, ash, and extractives. NIR models obtained were able to predict main components composition with R2cv values over 0.5, except for lignin which showed lowest prediction accuracy.

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“…Near-infrared (NIR) spectroscopy is a reliable alternative to traditional methods for efficiently evaluating chemical components in a non-destructive manner [ 5 ]. The NIR spectral region (800–2500 nm) is dominated by the bands related to the overtones and combination of fundamental vibrations (e.g., N–H, C–H, O–H and S–H), which are the foundation for analysis of lignocellulose components in biomass [ 6 ]. Hodge et al [ 7 ] developed global NIR models for rapid determination of cell wall components in pine wood which gave high correlation coefficients with independent validation of 0.97 for lignin and 0.82 for cellulose.…”

Section: Introductionmentioning

confidence: 99%

Nondestructive and rapid determination of lignocellulose components of biofuel pellet using online hyperspectral imaging system

Feng

Liu

et al. 2018

Biotechnol Biofuels

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BackgroundIn the pursuit of sources of energy, biofuel pellet is emerging as a promising resource because of its easy storage and transport, and lower pollution to the environment. The composition of biomass has important implication for energy conversion processing strategies. Current standard chemical methods for biomass composition are laborious, time-consuming, and unsuitable for high-throughput analysis. Therefore, a reliable and efficient method is needed for determining lignocellulose composition in biomass and so to accelerate biomass utilization. Here, near-infrared hyperspectral imaging (900–1700 nm) together with chemometrics was used to determine the lignocellulose components in different types of biofuel pellets. Partial least-squares regression and principal component multiple linear regression models based on whole wavelengths and optimal wavelengths were employed and compared for predicting lignocellulose composition.ResultsOut of 216 wavelengths, 20, 10 and 17 were selected by the successive projections algorithm for cellulose, hemicellulose and lignin, respectively. Three simple and satisfactory prediction models were constructed, with coefficients of determination of 0.92, 0.84 and 0.71 for cellulose, hemicellulose and lignin, respectively. The relative parameter distributions were quantitatively visualized through prediction maps by transferring the optimal models to all pixels on the hyperspectral image.ConclusionsHence, the overall results indicated that hyperspectral imaging combined with chemometrics offers a non-destructive and low-cost method for determining biomass lignocellulose components, which would help in developing a simple multispectral imaging instrument for biofuel pellets online measurement and improving the production management.Electronic supplementary materialThe online version of this article (10.1186/s13068-018-1090-3) contains supplementary material, which is available to authorized users.

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Applications of near-infrared spectroscopy (NIRS) in biomass energy conversion processes: A review

Cited by 57 publications

References 189 publications

High Throughput Screening Technologies in Biomass Characterization

High Throughput Screening Technologies in Biomass Characterization

Determination of the Lignocellulosic Components of Olive Tree Pruning Biomass by Near Infrared Spectroscopy

Nondestructive and rapid determination of lignocellulose components of biofuel pellet using online hyperspectral imaging system

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