论文信息 - Identification of green tea grade using different feature of response signal from E-nose sensors

Identification of green tea grade using different feature of response signal from E-nose sensors

Detection of tea grade by a human taste panel is affected by external factors and usually inaccurate, but it might be promising to use an electronic nose (E-nose). In this paper an investigation has been made to determine the grade of different tea samples using an E-nose. Feature vectors of the teas with different quality grade (Labeled: T120, T600, T800, T1200 and T1800) were extracted from the E-nose response signals, and the data were processed by using the principle components analysis (PCA) and linear discriminant analysis (LDA). Using the average and integrated value of feature vectors, 100% correct classification by LDA was achieved for five different tea samples with different qualities. The results indicated that the E-nose was capable of discriminating different grades of green teas.

[1] Andrew Fisher,et al. The classification of tea according to region of origin using pattern recognition techniques and trace metal data , 2003 .

[2] Matteo Falasconi,et al. The novel EOS835 electronic nose and data analysis for evaluating coffee ripening , 2005 .

[3] Suranjan Panigrahi,et al. Neural-network-integrated electronic nose system for identification of spoiled beef , 2006 .

[4] Tiina Rajamäki,et al. Application of an electronic nose for quality assessment of modified atmosphere packaged poultry meat , 2006 .

[5] Eric Chanie,et al. Shelf life determination by electronic nose: application to milk , 2005 .

[6] E. Llobet,et al. Discrimination between different samples of olive oil using variable selection techniques and modified fuzzy artmap neural networks , 2005, IEEE Sensors Journal.

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

[8] Antonella Macagnano,et al. Multisensor for fish quality determination , 2004 .

[9] Evor L. Hines,et al. Electronic nose based tea quality standardization , 2003, Neural Networks.

[10] Manuel Aleixandre,et al. Electronic nose for wine discrimination , 2006 .