Journal of Analytical Chemistry

, Volume 74, Issue 7, pp 686–692 | Cite as

Application of Moving Average Filter for the Quantitative Analysis of the NIR Spectra

  • Amneh A. Al-MbaideenEmail author


This paper investigates the use of the moving average (MA) filter for the quantitative analysis of the near-infrared (NIR) spectra. The performance of the MA filter is mainly dependent on the right choice of the filter length. The paper also introduces a new technique for the determination of the appropriate length of the MA filter. The use of MA filters for the quantitative analysis of the NIR spectra has not been previously evaluated in the field of chemometrics. The effect of using several types of MA filters on the performance of the partial least squares regression (PLS) model has been studied and evaluated. The efficiency of the model was validated using different mixtures composing of glucose, urea and triacetin dissolved in a phosphate buffer solution. The results demonstrate that the use of a small filter length reduces the signal to noise ratio of the spectra, while the use of a larger filter length introduces a distortion and some information gets lost. In this paper, the exponential MA (EWMA) filter has been used specifically for the quantitative analysis of the NIR spectra for the first time. Its performance has been evaluated and compared to other types of MA and Savitzky- Golay filters. The results show that the standard error of prediction decreases from 35.6 mg/dL when 8 PLS factors are used for the PLS model to 22.4 mg/dL when 9 PLS factors are used for the EWMA−PLS model.


moving average filter exponential moving average filter time domain smoothing NIR glucose 



The authors declare no conflict of interest.


  1. 1.
    Rinnan, A., van den Berg, F., and Engelsen, S.B., TrAC, Trends Anal. Chem., 2009, vol. 28, no. 10, p. 1201.CrossRefGoogle Scholar
  2. 2.
    Smith, S., The Scientist and Engineer’s Guide to Digital Signal Processing, San Diego: California Technical Publishing, 1999.Google Scholar
  3. 3.
    O’Haver, T., Pragmatic Introduction to Signal Processing: Applications in Scientific Measurement. Accessed May 16, 2016.Google Scholar
  4. 4.
    Savitzky, A. and Golay, M.J.E., Smoothing and differentiation of data by simplified least squares procedures, Perkin-Elmer Tech. News, 1964, vol. 36, no. 8, p. 1627.Google Scholar
  5. 5.
    Mark, H. and Workman, J., Spectroscopy (Springfield, OR, U. S.), 2003, vol. 18, no. 10, p. 32.Google Scholar
  6. 6.
    Olinger, J.M. and Griffiths, P.R., Microchim. Acta, 1988, vol. 94, no. 6, p. 105.CrossRefGoogle Scholar
  7. 7.
    Bowen, M. and Smith, R., Proc. R. Soc. A, 2005, vol. 461, p. 1975.CrossRefGoogle Scholar
  8. 8.
    Wabomba, M.J., Small, G.W., and Arnold, M., Anal. Chim Acta, 2003, vol. 490, nos. 1–2, p. 325.CrossRefGoogle Scholar
  9. 9.
    Chen, H., Song, Q., Tang, G., Feng, Q., and Lin, L., ISRN Spectrosc., 2013, vol. 2013, p. 1.CrossRefGoogle Scholar
  10. 10.
    Azami, H., Mohammadi, K., and Bozorgtabar, B., J. Signal Inform. Proc., 2012, vol. 3, p. 39.CrossRefGoogle Scholar
  11. 11.
    Guiñón, L., Ortega, E., García-Antón, J., and Pérez-Herranz, V., in Proc. Int. Conf. on Engineering Education, ICEE, Valencia, Spain, 2007.Google Scholar
  12. 12.
    Bovik, C.A. and Acton, T.S., The Essential Guide to Image Processing, New York: Academic, 2009, 2nd ed., p. 225.Google Scholar
  13. 13.
    Kramer, R., Chemometric Techniques for Quantitative Analysis, New York: Marcel Dekker, 1998.CrossRefGoogle Scholar
  14. 14.
    Tham, M.T., Dealing with measurement noise. Moving average filter, in Chemical Engineering and Advanced Materials, University of Newcastle upon Tyne, 1998. Accessed September 20, 2016.Google Scholar
  15. 15.
    Forbes, J., Ordonez, M., and Anun, M., Improving the dynamic response of power factor correctors using simple digital filters: moving average filter comparative evaluation, in IEEE Energy Conversion Congress and Exposition, 2013, p. 4814.Google Scholar
  16. 16.
    Burns, D.A. and Ciurczak, E.W., Handbook of Near-Infrared Analysis, Boca Raton: CRC, 2008, 3rd ed.Google Scholar
  17. 17.
    Areepong, Y., Int. J. Appl. Phys. Math., 2012, vol. 2, no. 5, p. 372.CrossRefGoogle Scholar
  18. 18.
    Areepong, Y., Int. J. Appl. Phys. Math., 2012, vol. 80, no. 3, p. 331.Google Scholar
  19. 19.
    Hatchett, R., Wade, B.B., and Anderson, K., J. Agric. Resour. Econ., 2010, vol. 35, no. 1, p. 118.Google Scholar
  20. 20.
    Areepong, Y., Int. J. Math. Comput. Sci., 2013, vol. 7, no. 8, p. 1283.Google Scholar
  21. 21.
    Geladi, P. and Kowalski, B., Anal. Chim. Acta, 1986, vol. 185, no. 346, p. 1.CrossRefGoogle Scholar
  22. 22.
    Borror, C.M., Montgomery, D.C., and Runger, G.C., J. Qual. Technol., 1999, vol. 31, no. 3, p. 309.CrossRefGoogle Scholar
  23. 23.
    Freijedo, F., Doval-Gandoy, J., Lopez, O., and Cabaleiro, J., Harmonic identification methods based on moving average filters for active power filters, in IEEE Industry Applications Society Annual Meeting IAS, 2008, p. 1.Google Scholar
  24. 24.
    Lyandres, V. and Briskin, S., in Circuits and Systems: Proc. of the 35th Midwest Symposium, 1992, vol. 1, p. 616.Google Scholar
  25. 25.
    Paatero, J.V. and Lund, P.D., Wind Energy, 2005, vol. 8, p. 421.CrossRefGoogle Scholar
  26. 26.
    Ham, F., Kostanic, I., Cohn, G., and Gooch, B., IEEE Trans. Biomed. Eng., 1997, vol. 44, no. 6, p. 475.CrossRefGoogle Scholar
  27. 27.
    Small, G., Arnold, M., and Marquadt, L., Anal. Chem., 1993, vol. 65, no. 22, p. 3279.CrossRefGoogle Scholar
  28. 28.
    Arnold, M. and Small, G., Anal. Chem., 1990, vol. 62, no. 14, p. 1457.CrossRefGoogle Scholar
  29. 29.
    Mattu, M.J., Small, G., and Arnold, M., Anal. Chem., 1997, vol. 69, no. 22, p. 4695.CrossRefGoogle Scholar
  30. 30.
    Ham, F., Cohenz, G., Kostanic, I., and Gooch, B.R., Physiol. Meas., 1996, vol. 17, no. 1, p. 1.CrossRefGoogle Scholar
  31. 31.
    Al-Odienat, A. and Al-Mbaideen, A., Int. J. Innovative Comput., Inform. Control, 2015, vol. 11, no. 2, p. 691.Google Scholar
  32. 32.
    Shome, S.K., Vadali, S., Datta, U., Sen, S., and Mukherjee, A., Int. J. Signal Process., Image Process. Pattern Recognit., 2012, vol. 5, no. 3, p. 75.Google Scholar
  33. 33.
    Reyes, P., Reviriego, P., Ruano, O., and Maestro, J.A., in Proc. of the RADECS 2006 Conference, Athens, Greece, 2006.Google Scholar
  34. 34.
    Hunter, J.S., J. Qual. Technol., 1986, vol. 18, no. 4, p. 203.CrossRefGoogle Scholar
  35. 35.
    Stone, D., Can. J. Chem., 1995, vol. 73, no. 10, p. 1573.CrossRefGoogle Scholar
  36. 36.
    Clarke, W., Cox, D., Gonder-Frederik, L., Carter, W., and Pohl, S.L., Diabetes Care, 1987, vol. 10, no. 5, p. 622.CrossRefGoogle Scholar
  37. 37.
    American Diabetes Association Consensus Statement. Self-monitoring of blood glucose. Am. Diabetes Assoc., Diabetes Care, 1994, vol. 17, p. 81.Google Scholar

Copyright information

© Pleiades Publishing, Ltd. 2019

Authors and Affiliations

  1. 1.Mutah University, Electrical Engineering DepartmentMutahJordan

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