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Sliding Window-based DCT Features for Tea Quality Prediction Using Electronic Tongue

Authors:

Santanu Ghorai,

Rajib Bandyopadhyay,

Nabarun BhattacharyyaAuthors Info & Claims

PerMIn '15: Proceedings of the 2nd International Conference on Perception and Machine Intelligence

Pages 230 - 236

https://doi.org/10.1145/2708463.2709032

Published: 26 February 2015 Publication History

Abstract

The electronic tongue (ET) system is a multi-electrode system where each electrode generates a specific electronic response in presence of different chemical substances in the sample. The efficiency of an ET system mostly depends on the discriminating power of the electronic signature generated by the electrode array. In this work, a sliding window approach is used to extract discrete cosine transform (DCT) coefficients from the ET response and the corresponding energy for different position of window are used as the features of the ET response. The efficacy of the proposed method is verified on three types of ET data sets to predict four different quality of black tea using two kernel classifiers, namely support vector machine (SVM) and vector valued regularized kernel function approximation (VVRKFA).

References

[1]

Ivarsson, P., Holmin, S., Hojer, N. E., Krantz- Rulcker, C., and Winquist, F. 2001. Discrimination of tea by means of a voltammetric electronic tongue and different applied waveforms. Sensor Actuat. B-Chem., 76, 1-3, 449--454. DOI=10.1016/S0925-4005(01)005834.

[2]

Valsov, Y., Legin, A., Rudnitskya, A., and Bychkov, E. 1996. Cross-sensitivity of chalcogenide glass sensors in solutions of heavy metal ions. Sensor Actuat. B-Chem., 34, 1-3, 456--461.

[3]

Ivarsson, P., Krantz-Rulcker, C., Winquist, F., and Lundstrom, I. 2005. A voltammetric electronic tongue. Chem. Sens., 30, 1, i258-i259.

[4]

Scampicchio, M., Benedetti, S., Brunetti, B., and Mannino, S. 2006. Amperometric electronic tongue for the evaluation of the tea astringency. Electroanal., 18, 17, 1643--1648.

[5]

Legin, A., Rudnitskaya, A., Valsov, Y., Natale, D., Mazzone, C., and D'Amico, A. 1999. Application of electronic tongue for quantitative analysis of mineral water and wine. Electroanalysis, 11, 10-11, 814--820.

[6]

Lowton, B., A., and Pethig, R. 1993. Determining the fat content of milk and cream using AC conductivity measurements. Meas. Sci. Technol., 4, 1, 38--41.

[7]

Scozzari, A., Acito, N., and Corsini, G. 2007 A novel method based on voltammetry for the qualitative analysis of water. IEEE Trans. Instrum. Meas., 56, 6, 2688--2697.

[8]

Ciosek, P., Brzozka, Z., and Wroblewski, W. 2006. Electronic tongue for flow-through analysis of beverages. Sensor Actuat. B-Chem., 118, 1-2, 454--460.

[9]

Ivarsson, P., Krantz-Rulcker, C., Winquist, F., and Lundstrom, I. 2005. A voltammetric electronic tongue. Chem. Sens, 30, 1, i258-i259.

[10]

Haddad, R., Carmel, and L., and Harel, D. 2007. A feature extraction algorithm for multi-peak Signals in electronic noses. Sensor Actuat. B-Chem., 120, 2, 462--472.

[11]

Ding, H., Ge, H., and Liu, J. 2005. High performance of gas identification by wavelet transform-based fast feature extraction from temperature modulated semiconductor gas sensors. Sensor Actuat. B-Chem, 107, 2, 749--755.

[12]

Scozarri, A., Acito, N., and Corsini, G. 2007. A novel method based on voltammetry for the qualitative analysis of water. IEEE Trans. Instrum. Meas., 56, 6, 2688--2697.

[13]

Valle, S., Li, w., and Qin, S. J. 2012. Selection of the number of principal components: the variance of the reconstruction error criterion with a comparison to other methods. Ind. Eng. Chem. Res, 38, 11, 4389--4401.

[14]

Saha, P., Ghorai, S., Tudu, B., Bandyopadhyay, R., and Bhattacharyya, N. 2014. A novel method of black tea quality prediction using electronic tongue Signals. IEEE Trans. Instrum. Meas, 63, 10, 2472--2479, DOI=10.1109/TIM.2014.2310615.

[15]

Signes, M. T., Garcia, J. M., and Mora, H. 2009. Improvement of the discrete cosine transform calculation by means of a recursive method. Mathematical and Computer Modelling, 50, 5-6, 750--764.

Digital Library

[16]

Karwal, V., Sherlock, B. G., and Kakad, Y. P. 2013 Independently updating the DCT and DST for shifting windowed data. Journal of the Franklin Institute, 350, 6, 1345--1357.

[17]

Vapnik, V. N. 1998. The Nature of Statistical Learning Theory, John Willy & Sons, Inc., New York.

Digital Library

[18]

Burges, C. J. C. 1998. A tutorial on support vector machines for pattern recognition. Data Min. and Knowl. Disc., 2, 2, 121--167.

Digital Library

[19]

Hsu, C. -W., and Lin, C. -J. 2002. A comparison of methods for multi-class support vector machines. IEEE Trans. N. Nets., 13, 2, 415--425.

Digital Library

[20]

Saha, P., Ghorai, S., Tudu, B. Bandyopadhyay, R., and Bhattacharyya, N. 2012. Multi-class support vector machine for quality estimation of black tea using electronic nose. In Proceedings of the ICST-2012, Kolkata, India. 571--576.

[21]

Ghorai, S., Mukherjee, A., and Dutta, P. K. 2010. Discriminant analysis for fast multiclass data classification through regularized kernel function approximation. IEEE Trans. N. Nets, 21, 6, 1020--1029.

Digital Library

[22]

Ghorai, S., Mukherjee, A., and Dutta, P. K. 2012. Advances in Proximal Kernel Classifiers, LAP LAMBERT Academic Publishing, Germany.

[23]

Ghorai, S., Mukherjee, A., Gangadaran, M., and Dutta, P. K. 2013. Automatic Defect Detection on Hot Rolled Flat Steel Products. IEEE Trans. Instrum. Meas, 62, 3 (2013), 612--621.

[24]

Palit, M., Tudu, B., Dutta, P. K., Dutta, A., Jana, A., Roy, J. K., Bhattacharyya, N., Bandyopadhyay, R., and Chatterjee, A. 2010. Classification of black tea taste and correlation with tea taster's mark using voltammetric electronic tongue. IEEE Trans. Instrum. Meas, 59, 8, 2230--2239.

[25]

Rao, K. R., Yip, P., and Britanak, V. 2007. Discrete Cosine Transform: Algorithms, Advantages, Applications, Academic Press, Inc., Orlando, Flo., USA.

Digital Library

[26]

Chang, C. C., and Lin, C. J. 2001. LIBSVM: A library for support vector machines, Tech. Report, DOI=http://www.csie.ntu.edu.tw/~cjlin/libsvm.

[27]

Mitchell, T. M. 1997. Machine Learning, The McGRaw Hill Companies, Inc., Singapore, Ch. 5, 148.

Cited By

Ghosh DSaha PGhorai STudu BBandyopadhyay RBhattacharyya N(2024)Noisy Electronic Tongue Signal Prediction for Tea Quality Estimation Using AutoencoderProceedings of Third International Conference on Advanced Computing and Applications10.1007/978-981-97-4799-3_20(267-279)Online publication date: 23-Dec-2024
https://doi.org/10.1007/978-981-97-4799-3_20
Kaushal PMudhalwadkar R(2020)Stationary wavelet singular entropy based electronic tongue for classification of milkTransactions of the Institute of Measurement and Control10.1177/014233121989389542:4(870-879)Online publication date: 7-Jan-2020
https://doi.org/10.1177/0142331219893895
Saha PGhorai STudu BBandyopadhyay RBhattacharyya N(2019)Tea Quality Prediction by Sparse Modeling of Electronic Tongue SignalsIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2018.287728468:9(3046-3053)Online publication date: Sep-2019
https://doi.org/10.1109/TIM.2018.2877284
Show More Cited By

Index Terms

Sliding Window-based DCT Features for Tea Quality Prediction Using Electronic Tongue
1. Computing methodologies
  1. Machine learning

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Information

Published In

cover image ACM Other conferences

PerMIn '15: Proceedings of the 2nd International Conference on Perception and Machine Intelligence

February 2015

269 pages

ISBN:9781450320023

DOI:10.1145/2708463

General Chair:
Sankar K. Pal
Indian Statistical Institute, Kolkata, India
,
Program Chairs:
Malay K. Kundu
Indian Statistical Institute, Kolkata, India
,
Santanu Chaudhury
Indian Institute of Technology, Delhi, India
,
Soma Mitra
CDAC, Kolkata, India
,
Debasis Mazumdar
CDAC, Kolkata, India

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Dept. of Science and Techn., Government of India: Department of Science and Technology, Government of India

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 February 2015

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PerMIn '15

PerMIn '15: 2nd International Conference on Perception and Machine Intelligence

February 26 - 27, 2015

West Bengal, Kolkata, India

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Cited By

Ghosh DSaha PGhorai STudu BBandyopadhyay RBhattacharyya N(2024)Noisy Electronic Tongue Signal Prediction for Tea Quality Estimation Using AutoencoderProceedings of Third International Conference on Advanced Computing and Applications10.1007/978-981-97-4799-3_20(267-279)Online publication date: 23-Dec-2024
https://doi.org/10.1007/978-981-97-4799-3_20
Kaushal PMudhalwadkar R(2020)Stationary wavelet singular entropy based electronic tongue for classification of milkTransactions of the Institute of Measurement and Control10.1177/014233121989389542:4(870-879)Online publication date: 7-Jan-2020
https://doi.org/10.1177/0142331219893895
Saha PGhorai STudu BBandyopadhyay RBhattacharyya N(2019)Tea Quality Prediction by Sparse Modeling of Electronic Tongue SignalsIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2018.287728468:9(3046-3053)Online publication date: Sep-2019
https://doi.org/10.1109/TIM.2018.2877284
Saha PGhorai STudu BBandyopadhyay RBhattacharyya N(2016)Tea Quality Prediction by Autoregressive Modeling of Electronic Tongue SignalsIEEE Sensors Journal10.1109/JSEN.2016.254497916:11(4470-4477)Online publication date: Jun-2016
https://doi.org/10.1109/JSEN.2016.2544979

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