| 1. |
- Börjesson, T., et al.
(author)
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Electronic nose for odor classification of grains
- 1996
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In: Cereal Chemistry. - : American Association of Cereal Chemists. - 0009-0352 .- 1943-3638. ; 73:4, s. 457-461
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Journal article (peer-reviewed)abstract
- An electronic nose was used to classify grain samples based on their smell and to predict the degree of moldy/musty odor. A total of 235 samples of wheat, barley and oats, which had been odor classified by at least two grain inspectors, were used. Headspace samples from heated grain were pumped through chambers containing metal oxide semiconductor field effect transistor (MOSFET) sensors, SnO2 semiconductors and an infrared detector monitoring CO2. The sensor signals were evaluated with a pattern-recognition software program based on artificial neural networks. The samples were divided into either the four classes moldy/musty, acid/sour, burnt, or normal or the two classes good and bad according to the inspectors descriptions. They were also assigned a score describing their intensity of moldy/musty odor. The electronic nose correctly classified approximate to 75% of the samples when using the four-class system and approximate to 90% when using the two-class system. These values exceeded the corresponding percentages of agreement between two grain inspectors classifying the grain.
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| 2. |
- Mandenius, C. F., et al.
(author)
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Predicting fermentability of wood hydrolyzates with responses from electronic noses
- 1999
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In: Biotechnology progress (Print). - New York, NY, United States : AIChE. - 8756-7938 .- 1520-6033. ; 15:4, s. 617-621
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Journal article (peer-reviewed)abstract
- 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%.
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