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Clustering Red Wines Using a Miniature Spectrometer of Filter-Array with a Cypress RGB Light Source
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 Title & Authors
Clustering Red Wines Using a Miniature Spectrometer of Filter-Array with a Cypress RGB Light Source
Choi, Kyung-Mee;
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 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;
 Language
English
 Cited by
 References
1.
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.

2.
Chang, C. C. and Lee, H. N. (2008). On the estimation of target spectrum for filter-array based spectrometers, Optical Express, 16, 1056-1061. crossref(new window)

3.
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. crossref(new window)

4.
Duda, R. O., Hart, P. E. and Stork, D. G. (2001). Pattern Classification, Wiley & Sons, New York.

5.
Hastie, T., Tibshirani, R. and Friedman, J. (2001). The Elements of Statistical Learning: Data Mining, Inference and Prediction, Springer-Verlag, New York.

6.
Johnson, R. A. and Wichern, D. W. (2007). Applied Multivariate Statistical Analysis, Prentice Hall, New York.

7.
Krzanowski, W. J. (2000). Principles of Multivariate Analysis: A User's Perspective, Oxford University Press, Oxford.

8.
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. crossref(new window)

9.
Morawski, R. Z. (2006). Spectrophotometric applications of digital signal processing, Measurement Science Technology, 17, 117-144. crossref(new window)

10.
Peebles, P. Z. (2000). Probability, Random Variables and Random Signal Principles, McGraw-Hill, New York.

11.
Rencher, A. C. (2002). Methods of Multivariate Analysis, Wiley Series in Probability and Statistics, New York.