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

Choi, Kyung-Mee

  • Received : 20100100
  • Accepted : 20100100
  • Published : 2010.02.28

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

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. https://doi.org/10.1364/OE.16.001056
  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. https://doi.org/10.1016/j.apergo.2006.01.003
  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. https://doi.org/10.1007/BF02294245
  9. Morawski, R. Z. (2006). Spectrophotometric applications of digital signal processing, Measurement Science Technology, 17, 117-144. https://doi.org/10.1088/0957-0233/17/9/R01
  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.