PROTOTYPE OF ELECTRONIC NOSE BASED ON GAS SENSORS ARRAY AND BACK PROPAGATION NEURAL NETWORK FOR TEA CLASSIFICATION

Abstract

We have developed an electronic nose based on metal oxide gas sensor array and back-propagation neural network for tea classification. The sensor array consists of six Tagushi Gas Sensor (TGS) type devices. To recognize the pattern formed by the six sensors we used six neurons in the input layer. Since we only want to classify four tea samples, we used two neurons in the output layer. The four tea samples (different tea flavors) were purchased from local super store in Yogyakarta, namely, black tea, green tea, vanilla tea and jasmine tea. Under the relatively similar conditions, we measured each sample of tea as a function of time. Prior to the exposure of tea samples, the sensor array was tested with air ambient. Then the electronic nose was trained by using one set of four tea samples without pre-processing step. By using all data sets, the electronic nose is able to recognize the pattern for almost 80%. This result prove that our electronic nose is capable of discriminating between the flavors of tea samples. For further investigation, the performance of this system should be compared with the data sets with pre-processing.

Keywords : Odor, Tea flavor, Metal oxide gas sensor, Sensor array, Back Propagation Neural network