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Data set of green extraction of valuable chemicals from lignocellulosic biomass using microwave method.

ABSTRACT: Lignocellulosic biomass is a promising alternative for the replacement of limited fossil resources to produce various chemical compounds, such as 5-hydroxymethylfurfural, furfural, vanillin, vanillic acid, ferulic acid, syringaldehyde, and 4-aminobenzoic acid. However, the complex biomass structure is a limitation to making effective use of this naturally found feedstock. This research presents a data set of different compounds obtained directly from forest residues, with special emphasis on achieving effective utilization of the biomass. The extraction method and the catalyst are considered as the two main factors in this valorization process.

SUBMITTER: Osorio-Gonzalez CS

PROVIDER: S-EPMC6731349 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Data set of green extraction of valuable chemicals from lignocellulosic biomass using microwave method.

Osorio-González Carlos S CS Hegde Krishnamoorthy K Brar Satinder K SK Kermanshahipour Azadeh A Avalos-Ramírez Antonio A

Data in brief 20190822

Lignocellulosic biomass is a promising alternative for the replacement of limited fossil resources to produce various chemical compounds, such as 5-hydroxymethylfurfural, furfural, vanillin, vanillic acid, ferulic acid, syringaldehyde, and 4-aminobenzoic acid. However, the complex biomass structure is a limitation to making effective use of this naturally found feedstock. This research presents a data set of different compounds obtained directly from forest residues, with special emphasis on ach ...[more]

PMID: 31516937

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Project description:BackgroundSolvent-free microwave-assisted extraction (MAE) is a green extraction method capable of boosting the yield and quality profile of hemp essential oil when compared with other conventional extraction techniques. During this process, two by-products are produced, namely the aqueous residue containing bioactive phenolics and the residual deterpenated biomass, which can be used for further extraction and purification of phytocannabinoids. To date, the hemp industry has not utilized these products, although they can be valuable for the food, cosmetic, nutraceutical and pharmaceutical market.ResultsThis study assessed and optimized the variables affecting MAE efficiency, namely microwave irradiation power, extraction time and added water, which were studied using a central composite design approach, and results were used to optimize the extraction process for recovering three valuable fractions: essential oil, polyphenols and phytocannabinoids. The products obtained using the optimized conditions were characterized in terms of yield, chemical profile and antioxidant potential. Moreover, the by-products obtained during the optimized run were further analyzed in terms of their biological activity using both enzymatic and non-enzymatic assays. The aqueous residue demonstrated a powerful α-glucosidase inhibition, a good activity in terms of superoxide radical scavenging activity, a modest efficacy in terms of inhibition of advanced glycation end products formation and no activity in terms of lipase inhibition. The residual deterpenated biomass did not possess significant biological activity.ConclusionThis work demonstrated valorization of industrial hemp essential oil and its by-products, obtained by a sustainable and eco-friendly extraction method, through an almost waste-free approach. Cannabinoids as well as other valuable bioactive compounds such as glycosidic flavones may be recovered from the residues of the essential oil extraction, representing interesting substances in the pharmaceutical, cosmetic and nutraceutical fields. © 2022 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Project description:High-throughput analysis of biomass is necessary to ensure consistent and uniform feedstocks for agricultural and bioenergy applications and is needed to inform genomics and systems biology models. Pyrolysis followed by mass spectrometry such as molecular beam mass spectrometry (py-MBMS) analyses are becoming increasingly popular for the rapid analysis of biomass cell wall composition and typically require the use of different data analysis tools depending on the need and application. Here, the authors report the py-MBMS analysis of several types of lignocellulosic biomass to gain an understanding of spectral patterns and variation with associated biomass composition and use machine learning approaches to classify, differentiate, and predict biomass types on the basis of py-MBMS spectra. Py-MBMS spectra were also corrected for instrumental variance using generalized linear modeling (GLM) based on the use of select ions relative abundances as spike-in controls. Machine learning classification algorithms e.g., random forest, k-nearest neighbor, decision tree, Gaussian Naïve Bayes, gradient boosting, and multilayer perceptron classifiers were used. The k-nearest neighbors (k-NN) classifier generally performed the best for classifications using raw spectral data, and the decision tree classifier performed the worst. After normalization of spectra to account for instrumental variance, all the classifiers had comparable and generally acceptable performance for predicting the biomass types, although the k-NN and decision tree classifiers were not as accurate for prediction of specific sample types. Gaussian Naïve Bayes (GNB) and extreme gradient boosting (XGB) classifiers performed better than the k-NN and the decision tree classifiers for the prediction of biomass mixtures. The data analysis workflow reported here could be applied and extended for comparison of biomass samples of varying types, species, phenotypes, and/or genotypes or subjected to different treatments, environments, etc. to further elucidate the sources of spectral variance, patterns, and to infer compositional information based on spectral analysis, particularly for analysis of data without a priori knowledge of the feedstock composition or identity.

Project description:Background: Lignocellulosic biomass has many functionalities that hold huge potential for material, energy or chemistry applications. To support advanced applications, the biomass must be milled into ultrafine powder to increase reactivity. This milling unit operation needs to be fully mastered to deliver high-quality standard end-products. Here we studied the relationship between the characteristics of the starting lignocellulosic plant material and the properties of the resulting ultrafine powder in different ball-mill process routes. Methods: Two lignocellulosic biomasses (pine bark and wheat straw) with contrasted compositional and mechanical properties were milled using three ball-mill set-ups delivering different balances of impact force and attrition force. The resulting powders were analysed for particle characteristics (size, agglomeration extent, shape) and powder flow properties (compressibility, cohesion) using a dynamic powder rheometer. Results: Pine bark is more amenable to a fast particle size reduction than the fibrous wheat straw. The resulting pine bark powders appear less compressible but much more cohesive than the straw powders due to particle shape, density and composition factors. The mill set-up working by attrition as dominant mechanical force (vibratory ball mill) produced a mix of large, elongated particles and higher amounts of fines as it acts mainly by erosion, the resulting powder being more prone to agglomerate due to the abundance of fines. The mill set-up working by impact as dominant mechanical force (rotary ball mill) produced more evenly distributed particle sizes and shapes. The resulting powder is less prone to agglomerate due to a preferential fragmentation mechanism. Conclusions: The attrition-dominant mill yields powders with dispersed particle sizes and shapes and the poorest flow properties, while the impact-dominant mill yields more agglomeration-prone powders. The mill set-up working with impact and attrition as concomitant mechanical forces (stirred ball mill) produces powders with better reactivity and flow properties compared to rotary and vibratory mills.

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Data set of green extraction of valuable chemicals from lignocellulosic biomass using microwave method.

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Data set of green extraction of valuable chemicals from lignocellulosic biomass using microwave method.

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