Electronic nose for odor classification of grains
1996 (English)In: Cereal Chemistry, ISSN 0009-0352, E-ISSN 1943-3638, Vol. 73, no 4, 457-461 p.Article in journal (Refereed) Published
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.
Place, publisher, year, edition, pages
American Association of Cereal Chemists , 1996. Vol. 73, no 4, 457-461 p.
Food Science Chemical Sciences
IdentifiersURN: urn:nbn:se:oru:diva-52291ISI: A1996UY84400009ScopusID: 2-s2.0-0029956767OAI: oai:DiVA.org:oru-52291DiVA: diva2:971455