Multi-class support vector machine for quality estimation of black tea using electronic nose

Electronic nose (e-nose) is a machine olfaction system that has shown significant possibilities as an improved alternative of human taster as olfactory perceptions vary from person to person. In contrast, electronic noses also detect smells with their sensors, but in addition describe those using electronic signals. An efficient e-nose system should analyze and recognize these electronic signals accurately. For this it requires a robust pattern classifier that can perform well on unseen data. This research work shows the efficient prediction of black tea quality using machine learning algorithm with e-nose. This paper investigates the potential of three different types of multi-class support vector machine (SVM) to build taster-specific computational models. Experimental results show that all the three models offer more than 97% accuracies to predict the considerable variation in tea quality.

[1]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[2]  Calogero Pace,et al.  On the Use of the SVM Approach in Analyzing an Electronic Nose , 2007, 7th International Conference on Hybrid Intelligent Systems (HIS 2007).

[3]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[4]  Meiying Ye,et al.  Least Square Support Vector Machines in Combination with Principal Component Analysis for Electronic Nose Data Classification , 2009, 2009 Second International Symposium on Information Science and Engineering.

[5]  Manuela O’Connell,et al.  A practical approach for fish freshness determinations using a portable electronic nose , 2001 .

[6]  Dustin Boswell,et al.  Introduction to Support Vector Machines , 2002 .

[7]  Harris Drucker,et al.  A Case Study in Handwritten Digit Recognition , 1994 .

[8]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[9]  John Platt,et al.  Large Margin DAG's for Multiclass Classification , 1999 .

[10]  Xiaodong Wang,et al.  Gas quantitative analysis with support vector machine , 2009, 2009 Chinese Control and Decision Conference.

[11]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[12]  Nabarun Bhattacharyya,et al.  Electronic Nose for Black Tea Classification and Correlation of Measurements With “Tea Taster” Marks , 2008, IEEE Transactions on Instrumentation and Measurement.

[13]  Matteo Pardo,et al.  Coffee analysis with an electronic nose , 2002, IEEE Trans. Instrum. Meas..

[14]  Calogero Pace,et al.  Least Square Regression Method for Estimating Gas Concentration in an Electronic Nose System , 2009, Sensors.

[15]  K. R. Kashwan,et al.  Tea quality prediction using a tin oxide-based electronic nose: an artificial intelligence approach , 2003 .

[16]  Ekachai Phaisangittisagul,et al.  Improving Sensor Subset Selection of Machine Olfaction Using Multi-class SVM , 2010, 2010 Third International Conference on Knowledge Discovery and Data Mining.

[17]  J. W. Arnold,et al.  Electronic nose analysis of volatile compounds from poultry meat samples, fresh and after refrigerated storage , 2002 .

[18]  Ekachai Phaisangittisagul.  Approximating Sensors' Responses of Odor Mixture on Machine Olfaction , 2009, 2009 International Conference on Artificial Intelligence and Computational Intelligence.

[19]  Bipan Tudu,et al.  Electronic nose for black tea quality evaluation by an incremental RBF network , 2009 .

[20]  M. C. Horrillo,et al.  Identification of typical wine aromas by means of an electronic nose , 2004, Proceedings of IEEE Sensors, 2004..

[21]  M. Pardo,et al.  Classification of electronic nose data with support vector machines , 2005 .