DOI QR코드

DOI QR Code

고차원 스펙트라 데이터 분석을 위한 Adjusted Direct Orthogonal Signal Correction 기법

Adjusted Direct Orthogonal Signal Correction For High-Dimensional Spectral Data

  • 김신영 (고려대학교 산업경영공학과) ;
  • 김성범 (고려대학교 산업경영공학과)
  • Kim, Sin-Young (School of Industrial Management Engineering, Korea University) ;
  • Kim, Seoung-Bum (School of Industrial Management Engineering, Korea University)
  • 투고 : 2011.08.31
  • 심사 : 2011.11.13
  • 발행 : 2011.12.01

초록

Modeling and analysis of high-dimensional spectral data provide an opportunity to uncover inherent patterns in various information-rich data. Orthogonal signal correction (OSC) a preprocessing technique has been widely used to remove unwanted variations of spectral data that do not contribute to prediction or classification. In the present study we propose a novel OSC algorithm called adjusted direct OSC to improve visualization and the ability of classification. Experimental results with real mass spectral data from condom lubricants demonstrate the effectiveness of the proposed approach.

키워드

참고문헌

  1. Fearn, T. (2000), On orthogonal signal correction, Chemometrics and Intelligent Laboratory Systems, 50, 47-52. https://doi.org/10.1016/S0169-7439(99)00045-3
  2. Jang, W. and Chang, W. (2006), A wavelet based feature selection method to improve classification of large signal-type data, Journal of the Korea Institute of Industrial Engineers, 32, 133-140.
  3. Jung, Y. S., Lee, W. I., and Lee, W. M. (2004), Instrumental analysis, basic and experiment, Info-Tech Corea, Seoul, Korea.
  4. Kher, A., Mulholland, M., Green, M., and Reedy, B. (2006), Forensic classification of ballpoint pen inks using high performance liquid chromatography and infrared spectroscopy with principal components analysis and linear discriminant analysis, Vibrational Spectroscopy, 40, 270-277. https://doi.org/10.1016/j.vibspec.2005.11.002
  5. Kim, S. B., Chen, V. C. P., Park, Y., Ziegler, T. R., and Jones, D. P. (2008), Controlling the false discovery rate for feature selection in high-resolution NMR spectra, Statistical Analysis and Data Mining, 1, 57-66. https://doi.org/10.1002/sam.10005
  6. Kutner, M. H., Nachtsheim, C. J., Neter, J., and Li, W. (2005), Applied Linear Statistical Models(5th edition), McGraw-Hill/Irwin, New York, USA.
  7. Luypaert, J., Heuerding, S., Massart, D. L., and Vander, Y. V. (2006), Direct orthogonal signal correction as data pretreatment in the classification of clinical lots of creams from near infrared spectroscopy data, Analytica Chimica Acta, 582, 191-189
  8. Park, Y., Kim, S. B., Wang, B, Blanco, R. A., Le, N.-A., Wu, S., Accardi, C. J., Alexander, R. W., Ziegler, T. R., and Jones, D. P. (2009), Individual variation in macronutrient regulation measured by proton magnetic resonance spectroscopy of human plasma, AmJ Physiol Regul Integr Comp Physiol, 297, 202-209. https://doi.org/10.1152/ajpregu.90757.2008
  9. Saferstein, R. (2005), An Introduction to Forensic Science, Hanrimwon, Seoul, Korea.
  10. Saiz-Abajo, M. J., Gonzalez-Saiz, J. M., and Pizarro, C. (2005), Orthogonal signal correction applied to the classification of wine and molasses vinegar samples by near-infrared spectroscopy. Feasibility study for the detection and quantification of adulterated vinegar samples, Anal Bioanal. Chem, 382(2), 412-420. https://doi.org/10.1007/s00216-005-3148-x
  11. Shmueli, G., Patel, N. R., and Bruce, P. C. (2007), Data Mining for Business Intelligence, Wiley, Hoboken, NJ
  12. Svensson, O., Kourti, T., and MacGregor, J. F. (2002), An investigation of orthognal signal correction algorithms and their characteristic, Journal of Chemometrics, 16, 176-188. https://doi.org/10.1002/cem.700
  13. Tan, P. N., Steinbach, M., and Kumar, V. (2007), Introduction to datamining, Infinity books, Seoul, Korea.
  14. Trygg, J. and Wold, S. (2002), Orthogonal projection to latent structures( O-PLS), Journal of Chemometrics, 16, 119-128. https://doi.org/10.1002/cem.695
  15. Westerhuis, J. A., Jong, S., and Smilde, A. K. (2001), Direct orthogonal signal correction, Chemometrics and Intelligent Laboratory Systems, 56, 13-25. https://doi.org/10.1016/S0169-7439(01)00102-2
  16. Witten, I. H., Frank, E., and Hall, M. A. (2011), Data Mining, Morgan Kaufmann, Burlington, MA.
  17. Wold, S., Antti, H., Lindgren, F., and Ohman, J. (1998), Orthogonal signal correction of near-infrared spectra, Chemometrics and Intelligent Laboratory Systems, 44, 175-185. https://doi.org/10.1016/S0169-7439(98)00109-9
  18. Zhu, D., Ji, B., Meng, C., Shi, B., Tu, Z., and Qing, Z. (2007), The application of direct orthogonal signal correction for linear and non-linear multivariate calibration, Chemometrics and Intelligent Laboratory Systems, 90, 108-115.
  19. Zijlstra, W. G., Buursma, A., and Assendelft, O. W. (2000), Visible and Near Infrared Absorption Spectra of Human and Animal Hemoglobin, Determination and Application, VSP, Amsterdam, The Netherlands.