Features extraction from electronic nose employing genetic algorithm for black tea quality estimation

Electronic nose has wide application in discriminations among food and beverage samples. Electronic nose is an array of gas sensor classifies samples based on their aroma profile. In this work this artificial sensory system is used to classify black tea using the features extracted from sensor response. Gaussian windowing function called `kernel´ are used to extract information from the transient response and those are optimized by GA. The number of features being considered for classification was reduced considerably as well as classification performance is much improved than classification by directly using the sensor responses.