Classification of beverages using electronic nose and machine vision systems

Mazlina Mamat, Salina Abdul Samad

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

In this work, the classification of beverages was conducted using three approaches: by using the electronic nose alone, by using the machine vision alone and by using the combination of electronic nose and machine vision. A total of two hundred and twenty eight beverages from fifteen different brands were used in this classification problem. A supervised Support Vector Machine was used to classify beverages according to their brands. Results show that by using the electronic nose alone and the machine vision alone were able to respectively classify 73.7% and 92.9% of the beverages correctly. When combining the electronic nose and the machine vision, the classification accuracy increased to 96.6%. Based on the results, it can be concluded that the combination of the electronic nose and machine vision is able to extract more information from the sample, hence improving the classification accuracy.

Original languageEnglish
Title of host publication2012 Conference Handbook - Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2012
Publication statusPublished - 2012
Event2012 4th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2012 - Hollywood, CA
Duration: 3 Dec 20126 Dec 2012

Other

Other2012 4th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2012
CityHollywood, CA
Period3/12/126/12/12

Fingerprint

Beverages
Computer vision
Support vector machines
Electronic nose

ASJC Scopus subject areas

  • Information Systems

Cite this

Mamat, M., & Abdul Samad, S. (2012). Classification of beverages using electronic nose and machine vision systems. In 2012 Conference Handbook - Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2012 [6411842]

Classification of beverages using electronic nose and machine vision systems. / Mamat, Mazlina; Abdul Samad, Salina.

2012 Conference Handbook - Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2012. 2012. 6411842.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Mamat, M & Abdul Samad, S 2012, Classification of beverages using electronic nose and machine vision systems. in 2012 Conference Handbook - Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2012., 6411842, 2012 4th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2012, Hollywood, CA, 3/12/12.
Mamat M, Abdul Samad S. Classification of beverages using electronic nose and machine vision systems. In 2012 Conference Handbook - Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2012. 2012. 6411842
Mamat, Mazlina ; Abdul Samad, Salina. / Classification of beverages using electronic nose and machine vision systems. 2012 Conference Handbook - Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2012. 2012.
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