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Smell classification of wines by the learning vector quantization method

Published: 13 April 2015 Publication History

Abstract

We consider a classification of white wine and red wine by a learning vector quantization method of competitive neural network. First, we measure smell data using metal-oxide semiconductor gas sensors which change smell data into electrical voltages based on oxidation and reduction processes. Two kinds of wines, white wine and red wine, are classified using smell data. Since a smell density of wine is rather thin, we use a bubbling method to make the density level higher. Here, we adopt a mono trap which is a kind of molecular sieves. By this way we obtain smell data of wines of high concentration level. After absorbing process, we take the temperature of a silica tube from a room temperature to 300 degrees Celsius. Using the learning vector quantization method, we classify two kinds of wines. We show that the classification accuracy rate for the white wine is around 97% and that for the red wine is around 83.4%, respectively.

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Cited By

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  • (2024)Recent Advances and Future Perspectives in the E-Nose Technologies Addressed to the Wine IndustrySensors10.3390/s2407229324:7(2293)Online publication date: 4-Apr-2024
  • (2017)Fragrance to vector as scent technology2017 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2017.8258418(4030-4034)Online publication date: Dec-2017

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cover image ACM Conferences
SAC '15: Proceedings of the 30th Annual ACM Symposium on Applied Computing
April 2015
2418 pages
ISBN:9781450331968
DOI:10.1145/2695664
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 13 April 2015

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Author Tags

  1. competitive neural network
  2. electronic nose
  3. learning vector quantization
  4. smell information processing
  5. wine classification

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SAC 2015
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SAC 2015: Symposium on Applied Computing
April 13 - 17, 2015
Salamanca, Spain

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SAC '15 Paper Acceptance Rate 291 of 1,211 submissions, 24%;
Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

View all
  • (2024)Recent Advances and Future Perspectives in the E-Nose Technologies Addressed to the Wine IndustrySensors10.3390/s2407229324:7(2293)Online publication date: 4-Apr-2024
  • (2017)Fragrance to vector as scent technology2017 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2017.8258418(4030-4034)Online publication date: Dec-2017

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