Skip to main content
SpringerLink
Log in

Method for Processing Fluorescence Decay Kinetic Curves Using Data Mining Algorithms

  • Published:
Journal of Applied Spectroscopy Aims and scope

A method is proposed for processing large data sets of fluorescence decay kinetic curves using data mining algorithms to determine the parameters of biophysical and optical processes in molecular systems. The idea of this method involves breaking the initial set of fluorescence decay curves into clusters in terms of some degree of similarity, finding medoids of the clusters, applying a dimensionality reduction method to the data and imaging the experimental data in two- and three-dimensional space, and analyzing the decay curves of the medoids using analytic or simulation models. The applicability of the method is examined for the example of analyzing sets of data representing systems of fluorophores. This method requires substantially less time and calculations of the analytic approximation functions, while the accuracy of the estimated parameters is higher than in the classical approach.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. J. R. Lakowicz, Principles of Fluorescence Spectroscopy, 3rd edn., Springer, New York (2006).

  2. A. H. Clayton, J. Biosci., 43, No. 3, 463–469 (2018).

    Article  Google Scholar 

  3. S. Shashkova and M. C. Leake, Biosci. Rep., 37, No. 4 (2017); https://doi.org/10.1042/BSR20170031.

  4. D. Phillips, Proc. Math. Phys. Eng. Sci., 472, No. 2190, 1–20 (2016).

    Article  Google Scholar 

  5. J. P. Angelo, S.-J. Chen, M. Ochoa, U. Sunar, S. Gioux, and X. Intes, J. Biomed. Opt., 24, No. 7, 071602, 1–20 (2018).

  6. E. Wientjes, J. Philippi, J. W. Borst, and H. van Amerongen, Biochim. Biophys. Acta Bioenerg., 1858, No. 3, 259–265 (2017).

    Article  Google Scholar 

  7. A. Boreham, R. Brodwolf, K. Walker, R. Haag, and U. Alexiev, Molecules, 22, No. 1, Е17 (1–18) (2017).

  8. Y. Engelborghs and A. J. W. G. Visser (Eds.), Fluorescence Spectroscopy and Microscopy: Methods and Protocols. Methods in Molecular Biology, 1076, Springer Science+Business Media, LLC (2014).

  9. M. M. Yatskou, Computer Simulation of Energy Relaxation and Transport in Organized Porphyrin Systems, Wageningen (2001).

  10. M. M. Yatskou, V. V. Apanasovich, R. B. M. Koehorst, A. van Hoek, and T. J. Schaafsma, J. Appl. Spectrosc., 70, 372–377 (2003).

    Google Scholar 

  11. I. V. Stanishevskii and S. M. Arabei, Proc. Conf. “Quantum Electronics,” November 18–22, 2019, RIVSh, Minsk (2019), pp. 64–66.

  12. N. N. Yatskov, Data Mining, a Textbook [in Russian], BGU, Minsk (2014).

    Google Scholar 

  13. N. N. Yatskov, V. V. Skakun, and V. V. Apanasovich, Applied Problems in Optics, Information, Electronics, and Solid State Physics [in Russian], NIU “Int. Prikl. Fiz. Problem im. A. N. Sevchenko,” GGU (2019), pp. 125–127.

  14. M. Bramer, Principles of Data Mining, 2nd edn., Springer, London (2013).

  15. C. C. Aggarwal, Data Mining: The Textbook, Springer, eBook (2015).

  16. N. N. Yatskov and V. V. Apanasovich, Proc. Sci.-Tech. Conf. “Quantum Electronics,” November 18–22, 2019, RIVSh, Minsk (2019), pp. 282–283.

  17. I. D. Mandel′, Cluster Analysis [in Russian], Finansy i Statistika, Moscow (1988).

  18. M. B. Lagutin, Intutitive Mathematical Statistics, a Textbook [in Russian], BINOM, Moscow, Laboratoriya Znanii (2007).

  19. I. T. Jolliffie, Principal Component Analysis, 2nd end., Springer, New York (2002).

  20. A. Hyvaerinen, J. Karhunen, and O. Erkki, Independent Component Analysis, New York, John Wiley & Sons, Inc. (2001).

    Book  Google Scholar 

  21. Y. Saeys, I. Inza, and P. Larranaga, Bioinformatics, 23, 2507–2517 (2007).

    Article  Google Scholar 

  22. A. V. Volkov, N. N. Yatskov, and V. V. Grinev, Vestn. BGU, Matematika, Informatika, No. 1, 77–89 (2019).

  23. V. Shapaval, J. Brandenburg, J. Blomqvist, V. Tafintseva, V. Passoth, M. Sandgren, and A. Kohler, Biotechnol. Biofuels, 12, 140 (1–12) (2019).

  24. C. Colabella, L. Corte, L. Roscini, V. Shapaval, A. Kohler, V. Tafintseva, C. Tascini, and G. Cardinali, PLoS One, 12, No. 12, e0188104 (1–20) (2017).

  25. V. V. Apanasovich and O. M. Tikhonenko, Digital Modeling of Stochastic Systems [in Russian], Universitetskoe, Minsk (1986).

  26. T. A. Roelofs, C. H. Lee, and A. R. Holzwarth, Biophys. J., 61, No. 5, 1147–1163 (1992).

    Article  Google Scholar 

  27. A. V. Digris, E. G. Novikov, V. V. Skakun, and V. V. Apanasovich, Method. Mol. Biol., 1076, 257–277 (2014).

    Article  Google Scholar 

  28. V. V. Apanasovich, E. G. Novikov, N. N. Yatskov, R. B. M. Koehorst, T. J. Schaafsma, and A. van Hoek, J. Appl. Spectrosc., 66, 613–616 (1999).

    Article  ADS  Google Scholar 

  29. V. V. Apanasovich, E. G. Novikov, and N. N. Yatskov, J. Appl. Spectrosc., 67, 42–851 (2000).

    Article  Google Scholar 

  30. N. N. Yatskov and E.V. Lisitsa, Data Mining: Methods for Laboratory Work [in Russian], BGU, Minsk (2019).

Download references

Author information

Authors and Affiliations

  1. Belarusian State University, 220030, Minsk, Belarus

    M. M. Yatskou & V. V. Skakun

  2. Institute of Information Technology and Business Administration, 220004, Minsk, Belarus

    V. V. Apanasovich

Authors
  1. M. M. Yatskou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. V. Skakun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. V. V. Apanasovich
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. M. Yatskou.

Additional information

Translated from Zhurnal Prikladnoi Spektroskopii, Vol. 87, No. 2, pp. 322–333, March–April, 2020.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yatskou, M.M., Skakun, V.V. & Apanasovich, V.V. Method for Processing Fluorescence Decay Kinetic Curves Using Data Mining Algorithms. J Appl Spectrosc 87, 333–344 (2020). https://doi.org/10.1007/s10812-020-01004-3

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10812-020-01004-3

Keywords

Search

Navigation