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http://dx.doi.org/10.5351/KJAS.2010.23.1.179

Clustering Red Wines Using a Miniature Spectrometer of Filter-Array with a Cypress RGB Light Source  

Choi, Kyung-Mee (College of Science and Technology, Hongik University)
Publication Information
The Korean Journal of Applied Statistics / v.23, no.1, 2010 , pp. 179-187 More about this Journal
Abstract
Miniature spectrometers can be applied for various purposes in wide areas. This paper shows how a wellmade spectrometer on-a-chip of a low performance and low-cost filter-array can be used for recognizing types of red wine. Light spectra are processed through a filter-array of a spectrometer after they have passed through the wine in the cuvettes. Without recovering the original target spectrum, pattern recognition methods are introduced to detect the types of wine. A wavelength cross-correlation turns out to be a good distance metric among spectra because it captures their simultaneous movements and it is affine invariant. Consequently, a well-designed spectrometer is reliability in terms of its repeatability.
Keywords
Spectrometer; cross-correlation; pattern recognition;
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  • Reference
1 Chang, C. C. and Lee, H. N. (2008). On the estimation of target spectrum for filter-array based spectrometers, Optical Express, 16, 1056-1061.   DOI
2 Choi, K. and Jun, C. (2007). A systematic approach to the Kansei factors of tactile sense regarding the surface roughness, Applied Ergonomics, 38, 53-63.   DOI   ScienceOn
3 Duda, R. O., Hart, P. E. and Stork, D. G. (2001). Pattern Classification, Wiley & Sons, New York.
4 Hastie, T., Tibshirani, R. and Friedman, J. (2001). The Elements of Statistical Learning: Data Mining, Inference and Prediction, Springer-Verlag, New York.
5 Johnson, R. A. and Wichern, D. W. (2007). Applied Multivariate Statistical Analysis, Prentice Hall, New York.
6 Krzanowski, W. J. (2000). Principles of Multivariate Analysis: A User's Perspective, Oxford University Press, Oxford.
7 Milligan, G. W. and Cooper, M. C.(1985). An examination of procedures for determining the number of clusters in a data set, Psychometrika, 50, 159-179.   DOI
8 Morawski, R. Z. (2006). Spectrophotometric applications of digital signal processing, Measurement Science Technology, 17, 117-144.   DOI   ScienceOn
9 Peebles, P. Z. (2000). Probability, Random Variables and Random Signal Principles, McGraw-Hill, New York.
10 Rencher, A. C. (2002). Methods of Multivariate Analysis, Wiley Series in Probability and Statistics, New York.
11 Box, G. E. P., Jenkins, G. M. and Reinsel, G. (1994). Time Series Analysis: Forecasting and Control, Wiley Series in Probability and Statistics, San Francisco.