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Predicting fermentability of wood hydrolyzates with responses from electronic noses
Dept. of Phys. and Msrmt. Technology, Linköping University.
Dept. of Phys. and Msrmt. Technology, Linköping University.
Dept. of Phys. and Msrmt. Technology, Linköping University.
Dept. of Chem. Reaction Engineering, Chalmers University of Technology.ORCID iD: 0000-0003-4887-2433
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1999 (English)In: Biotechnology progress (Print), ISSN 8756-7938, E-ISSN 1520-6033, Vol. 15, no 4, p. 617-621Article in journal (Refereed) Published
Sustainable development
In my opinion, the content of this publication falls within the area of sustainable development.
Abstract [en]

The fermentability of lignocellulose hydrolyzates have been predicted from the responses of a combination of chemical gas sensors. The hydrolyzates were prepared by dilute-acid hydrolysis of wood from pine, aspen, birch, and spruce. The volatile emission from the hydrolyzates before fermentation was measured, and the sensor array response pattern was compared with the observed fermentability of the hydrolyzates, i.e. with the final ethanol concentration after fermentation and the maximum specific ethanol production rate. Two concentration parameters in the hydrolyzates, furfural and the sum of furfural and 5-(hydroxymethyl)furfural (HMF), were also predicted from the responses. The sensors used were metal oxide semiconductor field effect transistors (MOSFET), tin oxide semiconductor devices, and conductive polymer sensors configured in two sensor arrays. The sensor array response pattern was analyzed by principal component analysis and artificial neural networks. Predictions from artificial neural networks deviated from measured values with less than 15%.The fermentability of lignocellulose hydrolyzates have been predicted from the responses of a combination of chemical gas sensors. The hydrolyzates were prepared by dilute-acid hydrolysis of wood from pine, aspen, birch, and spruce. The volatile emission from the hydrolyzates before fermentation was measured, and the sensor array response pattern was compared with the observed fermentability of the hydrolyzates, i.e. with the final ethanol concentration after fermentation and the maximum specific ethanol production rate. Two concentration parameters in the hydrolyzates, furfural and the sum of furfural and 5-(hydroxymethyl)furfural (HMF), were also predicted from the responses. The sensors used were metal oxide semiconductor field effect transistors (MOSFET), tin oxide semiconductor devices, and conductive polymer sensors configured in two sensor arrays. The sensor array response pattern was analyzed by principal component analysis and artificial neural networks. Predictions from artificial neural networks deviated from measured values with less than 15%.

Place, publisher, year, edition, pages
New York, NY, United States: AIChE , 1999. Vol. 15, no 4, p. 617-621
Keywords [en]
5 hydroxymethylfurfural, alcohol, fermentation, furfural, lignocellulose, principal component analysis, sensor, Cellulose, Composition, Ethanol, Fermentation, Furfural, Hydrolysis, Neural networks, Organic acids, Electronic noses, Lignocellulose, Principal component analysis, Wood hydrolyzates, Chemical sensors
National Category
Industrial Biotechnology
Research subject
Resource Recovery
Identifiers
URN: urn:nbn:se:hb:diva-14827DOI: 10.1021/bp990059dScopus ID: 2-s2.0-0032868540ISBN: 87567938 (ISSN) OAI: oai:DiVA.org:hb-14827DiVA, id: diva2:1236487
Available from: 2018-08-02 Created: 2018-08-02 Last updated: 2018-08-08Bibliographically approved

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Taherzadeh, Mohammad J

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