Determination of tea quality by using a neural network based electronic nose
UNSPECIFIED (2003) Determination of tea quality by using a neural network based electronic nose. In: International Joint Conference on Neural Networks, JUL 20-24, 2003, PORTLAND, OR.Full text not available from this repository.
In this paper we have used a metal oxide sensor based electronic nose (EN) to analyse five tea samples with different qualities, namely, Drier Month, Drier Month again over fired, Well Fermented normal fired. in oven, Well Fermented over fired in oven, and Under Fermented normal fired in oven. The flavour of tea is determined mainly by its taste and smell, which is generated by hundreds of Volatile Organic Compounds (VOCs) and Non Volatile Organic Compounds present in tea. These VOCs are present in different ratios and determine the quality of the tea. For example Assamica (Sri Lanka and Assam Tea) and Assamica Sinesis (Darjeeling and Japanese Tea) are two different species of tea giving different flavour notes. Tea flavour is traditionally measured through the use of a combination of conventional analytical instrumentation and human organoleptic profiling panels. These methods are expensive in terms of time and labour and also inaccurate because of a lack of either sensitivity or quantitative information. In this paper an investigation has been made to determine the flavours of different tea samples using an EN and to explore the possibility of replacing existing analytical and profiling panel methods. The technique uses an array of 4 metal oxide sensors (MOS), each of, which has an electrical resistance that has partial sensitivity to the headspace of tea. The signals from the sensor array are then conditioned by suitable interface circuitry. The data were processed using Principal Components Analysis (PCA), Fuzzy C Means algorithm (FCM). We also explored the use of a Self Organizing Map (SOM) method along with a Radial Basis Function network (RBF) and a Probabilistic Neural Network (PNN) classifier. Using FCM and SOM feature extraction techniques along with RBF neural network we achieved 100% correct classification for the five different tea samples with different qualities. These results prove that our EN is capable of discriminating between the flavours of teas manufactured under different processing conditions, viz. over-fermented, over-fired, under fermented etc.
|Item Type:||Conference Item (UNSPECIFIED)|
|Subjects:||Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
T Technology > TK Electrical engineering. Electronics Nuclear engineering
|Series Name:||IEEE International Joint Conference on Neural Networks (IJCNN)|
|Journal or Publication Title:||PROCEEDINGS OF THE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS 2003, VOLS 1-4|
|Number of Pages:||6|
|Page Range:||pp. 404-409|
|Title of Event:||International Joint Conference on Neural Networks|
|Location of Event:||PORTLAND, OR|
|Date(s) of Event:||JUL 20-24, 2003|
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