Incremental FCM Technique for Black Tea Quality Evaluation Using an Electronic Nose

Peer review under responsibility of Fuzzy Information and Engineering Branch of the Operations Research Society of China.

Abstract

A novel incremental algorithm based on fuzzy-c-means (FCM) method is proposed and implemented to effectively cluster data obtained from an electronic nose for black tea quality evaluation. The algorithm segregates data generated with the electronic nose from different batches of black tea into clusters with similar features, without requiring to access previously collected data. This feature of appending information exclusively from fresh data points entitles the algorithm to overcome catastrophic interference phenomenon common to conventional pattern recognition techniques.

Keywords:

• Electronic nose
• Black tea quality
• Taster scores
• Gas sensors
• Fuzzy clustering
• Incremental learning
• Fuzzy c-means

Notes

Peer review under responsibility of Fuzzy Information and Engineering Branch of the Operations Research Society of China.