Skip to main content
Log in

Statistical Analysis of Photopyroelectric Signals using Histogram and Kernel Density Estimation for differentiation of Maize Seeds

  • ICPPP 18
  • Published:
International Journal of Thermophysics Aims and scope Submit manuscript

Abstract

Considering the necessity of photothermal alternative approaches for characterizing nonhomogeneous materials like maize seeds, the objective of this research work was to analyze statistically the amplitude variations of photopyroelectric signals, by means of nonparametric techniques such as the histogram and the kernel density estimator, and the probability density function of the amplitude variations of two genotypes of maize seeds with different pigmentations and structural components: crystalline and floury. To determine if the probability density function had a known parametric form, the histogram was determined which did not present a known parametric form, so the kernel density estimator using the Gaussian kernel, with an efficiency of 95 % in density estimation, was used to obtain the probability density function. The results obtained indicated that maize seeds could be differentiated in terms of the statistical values for floury and crystalline seeds such as the mean (93.11, 159.21), variance \((1.64\times 10^{3}, 1.48\times 10^{3})\), and standard deviation (40.54, 38.47) obtained from the amplitude variations of photopyroelectric signals in the case of the histogram approach. For the case of the kernel density estimator, seeds can be differentiated in terms of kernel bandwidth or smoothing constant h of 9.85 and 6.09 for floury and crystalline seeds, respectively.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. K. Strzałkowski, Mater Sci. Eng. B 184, 80 (2014)

    Article  Google Scholar 

  2. A. Mami, I. Mellouki, N. Yacoubi, Int. J. Eng. Sci. Innov. Technol. 3(3), 608 (2014)

    Google Scholar 

  3. B.R. Briseño-Tepepa, J.L. Jiménez-Peréz, R. Saavedra, R. González-Ballesteros, E. Suaste, A. Cruz-Orea, Int. J. Thermophys. 29(6), 2200 (2008)

    Article  ADS  Google Scholar 

  4. S. Luterotti, D. Bicanic, K. Kljak, D. Grbesa, E.S.M. Martínez, R. Spruijt, Food Biophys. 6(1), 12 (2011)

    Article  Google Scholar 

  5. C. Hernandez-Aguilar, A. Cruz-Orea, R. Ivanov, A. Dominguez, A. Carballo, I. Moreno, R. Rico, Food Biophys. 6(4), 481 (2011)

    Article  Google Scholar 

  6. A. Rosencwaig, A. Gersho, J. Appl. Phys. 47(1), 64 (1976)

    Article  ADS  Google Scholar 

  7. A. Mandelis, M.M. Zver, J. Appl. Phys. 57(9), 4421 (1985)

    Article  ADS  Google Scholar 

  8. M. Suzuki, K. Miyamoto, Hoshimiya, Jpn. J. Appl. Phys. 44, 6S (2005)

    Google Scholar 

  9. R.R. Molina, C.H. Aguilar, A.D. Pacheco, A. Cruz-Orea, M.A. Canseco, Int. J. Thermophys. 34(8), 1540 (2013)

    Article  ADS  Google Scholar 

  10. J. Yao, L.V. Wang, Laser Photon Rev. 7(5), 758 (2013)

    Article  Google Scholar 

  11. A. Dominguez–Pacheco, C. Hernández –Aguilar, A. Cruz-Orea, E.I. Alemán, E. Martínez-Ortíz, Int. J. Thermophys. 34(8), 1499 (2013)

    Article  ADS  Google Scholar 

  12. G. Parodi, P. Dickerson, J. Cloud, J. Appl. Spectrosc. 67(3), 342 (2013)

    Article  ADS  Google Scholar 

  13. J.T. Alexander, V. Bochko, B. Martinkauppi, S. Saranwong, S. Mantere, Int. J. Spectrosc. (2013). doi:10.1155/2013/341402

  14. R.E. Walpole, R.H. Myers, S.L. Myers, K. Ye, Probability & Statistics for Engineers & Scientist (Pearson Education Inc, Prentice Hall, 2012), pp. 1–4

    MATH  Google Scholar 

  15. R. Ott, M. Lognecker, An Introduction to Statistical Methods and Data Analysis, 6th edn. (International ed.Books/Cole, Belmont, 2010)

    Google Scholar 

  16. I. Horová, Kernel Density Estimation. Encyclopedia of Environmetrics, 3, (2013)

  17. T.T. Soong, Fundamentals of Probability and Statistics for Engineers (Wiley, New York, 2004)

    MATH  Google Scholar 

  18. A.Z. Zambom, R. Dias, Int. Econom. Rev. (IER) 5(1), 20–42 (2013)

    Google Scholar 

  19. B.W. Silverman, Density Estimation for Statistics and Data Analysis, vol. 26 (CRC Press, Boca Raton, 1986)

    Book  MATH  Google Scholar 

  20. W. Zucchini, Applied Smoothing Techniques, Part 1: Kernel Density Estimation (Temple University, Philadephia, 2003)

    Google Scholar 

  21. T. Ledl, Austrian J. Stat. 33(3), 267 (2004)

    Google Scholar 

  22. A. Domínguez-Pacheco, C. Hernández-Aguilar, R. Zepeda-Bautista, E. Martínez-Ortiz, A. Cruz-Orea, Superficies y Vacío 25(2), 92 (2012)

    Google Scholar 

  23. Z.I. Botev, J.F. Grotowski, D.P. Kroese, Ann. Stat. 38(5), 2916–2957 (2010)

    Article  MathSciNet  Google Scholar 

  24. S.J. Sheather, Stat. Sci. 19(4), 588 (2004)

    Article  MathSciNet  Google Scholar 

  25. Y. Zheng, J. Jestes, J. M. Phillips, F. Li, in Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data. ACM, (June, 2013), pp. 433–444

  26. V.C. Raykar, R. Duraiswami, in SDM, 524 (2006). doi:10.1137/1.9781611972764.53

  27. W.J. Da Silva, B.C. Vidal, M.E.Q. Martins, H. Vargas, A.C. Pereira, M. Zerbetto, L.C. Miranda, Nature 362, 417 (1993)

    Article  ADS  Google Scholar 

  28. K.Y. Sastry, L. Froyen, J. Vleugels, E.H. Bentefour, C. Glorieux, Int. J. Thermophys. 25(5), 1611 (2004)

Download references

Acknowledgments

The authors thank the Instituto Politécnico Nacional, through the CONACYT, COFAA, EDI, and SIP Scholarship Projects. One of the authors (A. Cruz-Orea) is grateful for the economic support of CONACYT through Project 241330. Also, we thank Ing. Esther Ayala at the Physics Department of CINVESTAV-IPN for her technical support.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to A. Domínguez-Pacheco.

Additional information

This article is part of the selected papers presented at the 18th International Conference on Photoacoustic and Photothermal Phenomena.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rojas-Lima, J.E., Domínguez-Pacheco, A., Hernández-Aguilar, C. et al. Statistical Analysis of Photopyroelectric Signals using Histogram and Kernel Density Estimation for differentiation of Maize Seeds. Int J Thermophys 37, 98 (2016). https://doi.org/10.1007/s10765-016-2097-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10765-016-2097-2

Keywords

Navigation