DOI: 10.17586/2226-1494-2016-16-1-30-38


E. S. Khudyakov, E. A. Kochelaev, A. O. Volchek, D. O. Kirsanov, I. E. Jahatspanian

Read the full article 
Article in Russian

For citation: Khudyakov E.S., Kochelaev E.A., Volchek A.O., Kirsanov D.O., Jahatspanian I.E. Application of chemometrics for analysis of bioaerosols by flow-optical method. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2016, vol. 16, no. 1, pp. 30–38.


Subject of Research. The informativity of detection channels for bioaerosol analyzer is investigated. Analyzer operation is based on  flow-optical method. Method. Measurements of fluorescence and the light scattering of separate bioaerosol particles were performed in five and two spectral ranges, correspondingly. The signals of soil dust particles were registered and used as an imitation of background atmospheric particles. For fluorescenceinduction of bioaerosol particles we used light sources: a laser one with a wavelength equal to 266 nm and 365 nm LED source.Main Results. Using chemometric data processing the classification of informative parameters has been performed and three most significant parameters have been chosen which account for 72% of total data variance. Testing has been done using SIMCA and k-NN methods. It has been proved that the use of the original and the reduced sets of three parameters produces comparable accuracy for classification of bioaerosols. Practical Relevance. The possibility of rapid detection and identification of bioaerosol particles of 1-10 microns respirable fraction (hindering in the human respiratory system) by flow-optical method on a background of non-biological particles is demonstrated. The most informative optical spectral ranges for development of compact and inexpensive analyzer are chosen.

Keywords: flow-optical method, bioaerosols, principal component analysis, chemometric, SIMCA, k-NN, informative characteristics

Acknowledgements. This work was partially financially supported by the Government of the Russian Federation (Grant 074-U01).


1. Dutkiewicz J. Bacteria and fungi in organic dust as potential health hazard. Annals of Agriculture and Environmental Medicine, 1997, vol. 4, no. 1, pp. 11–16.
2. Sivaprakasam V., Huston A.L., Scotto C., Eversole J.D. Multiple UV wavelength excitation and fluorescence of bioaerosols. Optics Express, 2004, vol. 12, no. 19, pp. 4457–4466. doi: 10.1364/OPEX.12.004457
3. Pepper I.L., Gerba C.P., Gentry T., Raina M. Environmental Microbiology. 2nd ed. San Diego, Academic Press, 2011, 624 p. doi: 10.1016/B978-0-12-370519-8.50002-9
4. Kikikawa M., Iwasaka Y., Kobayashi F., Maki T. Molecular identification of microorganisms in bioaerosols. Earozoru Kenkyu, 2010, vol. 25, pp. 29–34.
5. Usachev E.V., Pankova A.V., Rafailova E.A., Pyankov O.V., Agranovski I.E. Portable automatic bioaerosol sampling system for rapid on-site detection of targeted airborne microorganisms. Journal of Environmental Monitoring, 2012, vol. 14, no. 10, pp. 2739–2745. doi: 10.1039/c2em30317e
6. Kochelaev E.A., Volchek A.O. Optical recording system for a flow-through optical method of analyzing bioaerosols. Journal of Optical Technology, 2011, vol. 78, no. 6, pp. 365–370.
7. Jeys T.H., Herzog W.D., Hybl J.D., Czerwinski R.N., Sanchez A. Advanced trigger development. Lincoln Laboratory Journal, 2007, vol. 17, no. 1, pp. 29–60.
8. Pan Y-L. Detection and characterization of biological and other organic-carbon aerosol particles in atmosphere using fluorescence. Journal of Quantitative Spectroscopy and Radiative Transfer, 2015, vol. 150, pp. 12–35. doi: 10.1016/j.jqsrt.2014.06.007
9. Burstein E.A., Abornev S.M., Reshetnyak Y.K. Decomposition of protein tryptophan fluorescence spectra into log-normal components. I. Decomposition algorithms. Biophysical Journal, 2001, vol. 81, no. 3, pp. 1699–1709.
10. Reshetnyak Y.K., Burstein E.A. Decomposition of protein tryptophan fluorescence spectra into log-normal components. II. The statistical proof of discreteness of trytophan classes in proteins. Biophysical Journal, 2001, vol. 81, no. 3, pp. 1710–1734.
11. Reshetnyak Y.K., Koshevnik Y., Burstein E.A. Decomposition of protein tryptophan fluorescence spectra into log-normal components. III. Correlation between tryptophan and microenvironment parameters of individual tryptophan residues. Biophysical Journal, 2001, vol. 81, no. 3, pp. 1735–1758.
12. Pan Y.-L., Hill S.C., Pinnick R.G., Huang H., Bottiger J.R., Chang R.K. Fluorescence spectra of atmospheric aerosol particles using one or two excitation wavelengths: comparison of classification schemes employing different emission and scattering results. Optics Express, 2010, vol. 18, no. 12, pp. 12436–12457. doi: 10.1364/OE.18.012436
13. Kochelaev E.A., Volchek A.O. Method for Optical Detection of Fluoroscence and Scattering signals of Aerosol Particles in Stream and Optical System for Realising Said Method. Patent RU2448340, 2012.
14. Esbensen K.H. Multivariate Date Analysis – In Practice. 5th ed. Oslo, Norway, CAMO Process AS, 2002, 598 p.
15. Rodionova O.E., Pomerantsev A.L. Khemometrika v Analiticheskoi Khimii [Chemometrics in Analytical Chemistry]. Moscow, Institute of Chemical Physics RAS Publ., 2006, 61 p.
16. Loo B.W., Cork C.P. Development of high efficiency virtual impactors. Aerosol Science and Technology, 1988, vol. , no. 3, pp. 167–176.
17. Rao N.P., Navanscues J., Fernandez de la Mora J. Aerodynamic focusing of particles in viscouts jets. Journal of Aerosol Science, 1993, vol. 24, no. 7, pp. 879–892. doi: 10.1016/0021-8502(93)90068-K
18. Beebe K.R., Pell R.J., Seasholtz M.B. Chemometrics: A Practical Guide. NY, John Wiley & Sons, 1998, 360 p.
19. Hastie T., Tibshirani R., Friedman J. Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. NY, Springer, 2009, 745 p. doi: 10.1007/978-0-387-84858-7

Copyright 2001-2018 ©
Scientific and Technical Journal
of Information Technologies, Mechanics and Optics.
All rights reserved.