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Journal of Analytical Chemistry

, Volume 73, Issue 12, pp 1195–1201 | Cite as

Recognition of Model Analyte Mixtures in the Presence of Blood Plasma Using a Mixture of Fluorophores (“Fluorescent Tongue”)

  • N. N. Divyanin
  • E. A. Rukosueva
  • A. V. Garmash
  • M. K. BeklemishevEmail author
ARTICLES
  • 22 Downloads

Abstract

The paper is devoted to the development of a version of the fingerprint method based on the effect of analytes on the shape of the fluorescence spectrum of a fluorophore mixture. The model analytes were medicinal substances (amikacin, sulfamethoxazole, pyracetam, and chloramphenicol) and binary to quaternary mixtures of these substances in equal concentrations. The mixtures were recognized using a fluorophore whose fluorescence was quenched to different extent by different model analytes (CdSe/CdS/ZnS quantum dots, Schiff base prepared from o-phthalic dialdehyde and polyethyleneimine, and also rhodamine B and fluorescein immobilized on silica nanoparticles to increase the degree of quenching – Rhod/SiO2 and Fluor/SiO2). The fluorophores were used as mixtures (“fluorescent reagents”) containing from one to four fluorophores. The classes of analytes were distinguished by calculating Mahalanobis distances on score plots of the principal component analysis method. It was found that it was more difficult to distinguish analyte mixtures in the presence of blood plasma than in a buffer solution and that the best fluorescent reagent was the mixture of all four fluorophores, which ensured the subdivision of 15 mixtures of 2–4 model analytes into seven classes. An alternative fingerprint method based on the use of UV absorption spectra allowed distinguishing of only five classes.

Keywords:

fluorescence quenching fluorophore mixture qualitative analysis principal component analysis CdSe quantum dots rhodamine B fluorescein Schiff base amikacin sulfamethoxazole pyracetam chloramphenicol 

Notes

ACKNOWLEDGMENTS

This work was supported by the Russian Science Foundation (project no. 14-23-00012).

REFERENCES

  1. 1.
    Bajoub, A., Medina-Rodriguez, S., Gómez-Romero, M., Ajal, E.A., Bagur-González, M.G., Fernández-Gutiérrez, A., and Carrasco-Pancorbo, A., Food Chem., 2017, vol. 215, p. 245.CrossRefGoogle Scholar
  2. 2.
    Zil’berg, R.A., Yarkaeva, Yu.A., Maksyutova, E.I., Sidel’nikov, A.V., and Maistrenko, V.N., J. Anal. Chem., 2017, vol. 72, no. 4, p. 402.CrossRefGoogle Scholar
  3. 3.
    Ntakatsane, M.P., Liu, X.M., and Zhou, P., J. Dairy Sci., 2013, vol. 96, p. 2130.CrossRefGoogle Scholar
  4. 4.
    Xie, P., Chen, S., Liang, Y.-Z., Wang, X., Tian, R., and Upton, R., J. Chromatogr. A, 2006, vol. 1112, p. 171.CrossRefGoogle Scholar
  5. 5.
    Brunelle, E., Huynh, C., Le, A.M., Halámková, L., Agudelo, J., and Halámek, J., Anal. Chem., 2016, vol. 88, p. 2413.CrossRefGoogle Scholar
  6. 6.
    Madhuri, S., Vengadesan, N., Aruna, P., Koteeswaran, D., Venkatesan, P., and Ganesan, S., Photochem. Photobiol., 2003, vol. 78, p. 197.CrossRefGoogle Scholar
  7. 7.
    Goodacre, R., Timmins, E.M., Burton, R., Kaderbhai, N., Woodward, A.M., Kell, D.B., and Rooney, P.J., Microbiology, 1998, vol. 144, p. 1157.CrossRefGoogle Scholar
  8. 8.
    Yonis Talpur, M., Kara, H., Sherazi, S.T.H., Ayyildiz, H.F., Topkafa, M., Nur Arslan, F., Naz, S., Durmaz, F., and Sirajuddin, Talanta, 2014, vol. 129, p. 473.CrossRefGoogle Scholar
  9. 9.
    Sergiel, I., Pohl, P., Biesaga, M., and Mironczyk, A., Food Chem., 2014, vol. 145, p. 319.CrossRefGoogle Scholar
  10. 10.
    Fang, X., Zhang, X., Feng, C., Tang, Y., and Ding, Z., J. Chin. Cereals Oils Assoc., 2015, vol. 9. http://en.cnki.com.cn/Article_en/CJFDTOTAL-ZLYX201509024.htm. Accessed April 16, 2018.Google Scholar
  11. 11.
    Miranda, O.R., You, C.-C., Phillips, R., Kim, I.-B., Ghosh, P.S., Bunz, U.H.F., and Rotello, V.M., J. Am. Chem. Soc., 2007, vol. 129, p. 9856.CrossRefGoogle Scholar
  12. 12.
    Han, J., Wang, B., Bender, M., Seehafer, K., and Bunz, U.H.F., Analyst, 2017, vol. 142, p. 537.CrossRefGoogle Scholar
  13. 13.
    Wu, Y., Tan, Y., Wu, J., Chen, S., Chen, Y.Z., Zhou, X., Jiang, Y., and Tan, C., ACS Appl. Mater. Interfaces, 2015, vol. 7, no. 12, p. 6882.CrossRefGoogle Scholar
  14. 14.
    Han, J., Bender, M., Seehafer, K., and Bunz, U.H.F., Angew. Chem., 2016, vol. 55, p. 7689.CrossRefGoogle Scholar
  15. 15.
    Han, J., Wang, B., Bender, M., Kushida, S., Seehafer, K., and Bunz, U.H.F., ACS Appl. Mater. Interfaces, 2017, vol. 9, no. 1, p. 790.CrossRefGoogle Scholar
  16. 16.
    Divyanin, N.N., Razina, A.V., Rukosueva, E.A., Garmash, A.V., and Beklemishev, M.K., Microchem. J., 2017, vol. 135, p. 48.CrossRefGoogle Scholar
  17. 17.
    Vasilenko, D.V., Maslov, A.I., Erina, N.D., Kuz’mina, N.I., and Aduev, M.S., Prikl. Inf. Aspekty Med., 2010, vol. 13, no. 1, p. 3.Google Scholar
  18. 18.
    Jolliffe, I., Principal Component Analysis, New York: Springer, 2002, 2nd ed.Google Scholar
  19. 19.
    Electronic Noses and Tongues in Food Science, Rodríguez-Méndez, M.L., Ed., New York: Academic, 2016.Google Scholar

Copyright information

© Pleiades Publishing, Inc. 2018

Authors and Affiliations

  • N. N. Divyanin
    • 1
  • E. A. Rukosueva
    • 1
  • A. V. Garmash
    • 1
  • M. K. Beklemishev
    • 1
    Email author
  1. 1.Department of Chemistry, Moscow State UniversityMoscowRussia

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