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.
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