Skip to main content
SpringerLink
Log in

Expansion of the Capabilities of Chromatography-Mass Spectrometry Due to the Numerical Decomposition of the Signal with the Mutual Superposition of Mass Spectra

  • Conference paper
  • First Online:
Data Stream Mining & Processing (DSMP 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1158))

Included in the following conference series:

  • 404 Accesses

Abstract

Numerical methods for expanding the field of applicability of chromatography-mass spectrometry in the case of poorly separated signals are considered. We found that the existence of additive noise in the initial mixed mass spectrum gives rise to the noise component of the weight coefficients of its components with an undetermined probability distribution law. The power of the generated noise is higher than the power of the additive noise of the output signal. It is shown that the condition of orthogonality of the components of the mixed mass spectrum makes it possible to determine their weight coefficients with a relative error of less than 4% when the ratio of the power of additive noise to the power of the useful signal is not more than three times. The main result of the work, which is new compared to the one published earlier, is that for real mass spectra it was shown that decomposition of a linear combination of orthogonal functions by the optimal linear associative memory (OLAM) method gives a satisfactory result even if the noise level is three times higher than the useful signal level. The area of satisfactory application of OLAM for the decomposition of non-orthogonal functions, on the contrary, is limited by the condition that the signal level exceeds the noise level by about 2.5 times.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Alford-Stevens, A., Budde, W., Bellar, T.: Interlaboratory study on determination of polychlorinated biphenyls in environmentally contaminated sediments. Anal. Chem. 57, 2452–2457 (1985). https://doi.org/10.1021/ac00290a007

    Article  Google Scholar 

  2. Ayris, S., Currado, S., Smith, D., Harrad, S.: GC/MS procedures for the determination of PCBS in environmental matrices. Chemosphere 35(5), 905–917 (2016). https://doi.org/10.1016/S0045-6535(97)00187-2

    Article  Google Scholar 

  3. Cicone, A.: Iterative filtering as a direct method for the decomposition of nonstationary signals. Numer. Algorithms 1–17 (2020). https://doi.org/10.1007/s11075-019-00838-z

  4. Cicone, A., Liu, J., Zhou, H.: Adaptive local iterative filtering for signal decomposition and instantaneous frequency analysis. Appl. Comput. Harmon. Anal. 41(2), 384–411 (2016). https://doi.org/10.1016/j.acha.2016.03.001d

    Article  MathSciNet  MATH  Google Scholar 

  5. Cicone, A., Liu, J., Zhou, H.: Hyperspectral chemical plume detection algorithms based on multidimensional iterative filtering decomposition. Phil. Trans. R. Soc. A 374, art. no. 20160871 (2016). https://doi.org/10.1098/rspa.2016.0871

  6. Coombes, K., Baggerly, K., Morris, J.: Pre-processing mass spectrometry data. In: Dubitzky, W., Granzow, M., Berrar, D. (eds.) Fundamentals of Data Mining in Genomics and Proteomics, pp. 79–102. Springer, Boston (2007). https://doi.org/10.1007/978-0-387-47509-7_4

  7. Donato, F., Magoni, M., Bergonzi, R., et al.: Exposure to polychlorinated biphenyls in residents near a chemical factory in Italy: the food chain as main source of contamination. Chemosphere 64(9), 1562–1572 (2006). https://doi.org/10.1021/es025809l

    Article  Google Scholar 

  8. Dragomiretskiy, K., Zosso, D.: Variational mode decomposition. IEEE Trans. Signal Process. 62(3), 531–544 (2014). https://doi.org/10.1109/TSP.2013.2288675

    Article  MathSciNet  MATH  Google Scholar 

  9. Gomara, B., Herrero, I., Pacepavicius, G., et al.: Occurrence of co-planar polybrominated/chlorinated biphenyls (PXBS), polybrominated diphenyl ethers (PBDES) and polychlorinated biphenyls (PCBS) in breast milk of women from Spain. Chemosphere 83(6), 799–805 (2011). https://doi.org/10.1016/j.chemosphere.2011.02.080

    Article  Google Scholar 

  10. Karlson, K., Ishaq, R., Becker, G., et al.: PCBs, DDTs and methyl sulphone metabolites in various tissues of harbour porpoises from Swedish waters. Environ. Pollut. 110(1), 29–46 (2000). https://doi.org/10.1016/s0269-7491(99)00283-3

    Article  Google Scholar 

  11. Keller, P., Kangas, L., Troyer, G., et al.: Nuclear spectral analysis via artificial neural networks for waste handling. IEEE Trans. Nucl. Sci. 42(4), 709–715 (1995). https://doi.org/10.1109/23.467888

    Article  Google Scholar 

  12. Kohonen, T., Ruohonen, M.: Representation of associated data by matrix operators. IEEE Trans. Comput. C–22, 701–702 (1973). https://doi.org/10.1109/TC.1973.5009138

    Article  Google Scholar 

  13. Martínez-Vidal, J., González-Rodríguez, M., et al.: Selective extraction and determination of multiclass pesticide residues in post-harvest French beans by low-pressure gas chromatography/tandem mass spectrometry. J. AOAC Int. 1, 856–867 (2003). https://doi.org/10.1093/jaoac/86.4.856

  14. Meijer, S., Ockenden, W., Sweetman, A., et al.: Global distribution and budget of PCBs and HCB in background surface soils: implications for sources and environmental processes. Environ. Sci. Technol. 37(4), 667–672 (2003). https://doi.org/10.1021/es025809l

    Article  Google Scholar 

  15. Mijović, B., De Vos, M., Gligorijević, I., et al.: Huffel source separation from single-channel recordings by combining empirical-mode decomposition and independent component analysis. IEEE Trans. Biomed. Eng. 57(9), 2188–2196 (2010). https://doi.org/10.1109/tbme.2010.2051440

    Article  Google Scholar 

  16. Montory, M., Habit, E., Fernandez, P., et al.: PCBs and PBDEs in wild Chinook salmon (Oncorhynchus tshawytscha) in the Northern Patagonia. Chile. Chemosphere 78(10), 1193–1199 (2010). https://doi.org/10.1016/j.chemosphere.2009.12.072

    Article  Google Scholar 

  17. Olszewski, S., et al.: Some features of the numerical deconvolution of mixed molecular spectra. In: Lytvynenko, V., Babichev, S., Wójcik, W., Vynokurova, O., Vyshemyrskaya, S., Radetskaya, S. (eds.) ISDMCI 2019. AISC, vol. 1020, pp. 20–34. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-26474-1_2

    Chapter  Google Scholar 

  18. Piersanti, M., Materassi, M., Cicone, A., et al.: Adaptive local iterative filtering: a promising technique for the analysis of nonstationary signals. J. Geophys. Res.: Space Phys. 123(1), 1031–1046 (2018). https://doi.org/10.1002/2017JA024153

    Article  Google Scholar 

  19. Poster, D., Kucklick, J., Schantz, M., et al.: Determination of polychlorinated biphenyl congeners and chlorinated pesticides in a fish tissue standard reference material. Anal. Bioanal. Chem. 375(2), 223–241 (2003). https://doi.org/10.1007/s00216-002-1680-5

    Article  Google Scholar 

  20. Qian, T.: Mono-components for decomposition of signals. Math. Meth. Appl. Sci. 29, 1187–1198 (2006). https://doi.org/10.1002/mma.721

    Article  MathSciNet  MATH  Google Scholar 

  21. Singh, P., Joshi1, S., Patney, R., Saha, K.: The fourier decomposition method for nonlinear and non-stationary time series analysis. Proc. R. Soc. A 473, art. no. 20160871 (2017). https://doi.org/10.1098/rspa.2016.0871

  22. Stanković, L., Thayaparan, T., Daković, M., et al.: Signal decomposition by using the S-method with application to the analysis of HF radar signals in sea-clutter. IEEE Trans. Signal Process. 54(11), 318–336 (2006). https://doi.org/10.1109/TSP.2006.880248

    Article  MATH  Google Scholar 

  23. Vautard, R., Yiou, P., Ghil, M.: Singular-spectrum analysis: a toolkit for short, noisy chaotic signals. Physica D 58, 95–126 (1992). https://doi.org/10.1016/0167-2789(92)90103-T

    Article  Google Scholar 

  24. Vetter, W., Weichbrodt, M., Scholz, E., et al.: Levels of organochlorines (DDT, PCBs, Toxaphene, Chlordane, Dieldrin, and HCHs) in blubber of south African Fur seals (Arctocephalus pusillus pusillus) from Cape Cross/Namibia. Mar. Pollut. Bull. 38(9), 830–836 (1999). https://doi.org/10.1016/S0025-326X(99)00071-5

    Article  Google Scholar 

  25. Wu, Z., Huang, N.: Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv. Adapt. Data Anal. 1(1), 1–41 (2009). https://doi.org/10.1142/S1793536909000047

    Article  Google Scholar 

  26. Wu, Z., Huang, N., Chen, X.: The multi-dimensional ensemble empirical mode decomposition method. Adv. Adapt. Data Anal. 1(1), 339–372 (2009). https://doi.org/10.1142/S1793536909000187

    Article  MathSciNet  Google Scholar 

  27. Zhang, S., Zhang, Q., Darisaw, S., et al.: Simultaneous quantification of polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), and pharmaceuticals and personal care products (PPCPs) in Mississippi river water, in new Orleans, Louisiana, USA. Chemosphere 66(6), 1057–1069 (2007). https://doi.org/10.1016/j.chemosphere.2006.06.067

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Taras Shevchenko National University of Kyiv, Kyiv, Ukraine

    Serge Olszewski

  2. Laboratory of Analytical Chemistry and Monitoring of Toxic Substances State Institution “Kundiiev Institute of Occupational Health of the National Academy of Medical Sciences of Ukraine”, Kyiv, Ukraine

    Serge Olszewski, Yeva Zajets & Violetta Demchenko

  3. Department of Informatics and Computing Technology, Kherson National Technical University, Kherson, Ukraine

    Oleg Boskin, Mariia Voronenko, Volodymyr Lytvynenko & Dmitry Stepanchikov

  4. Kharkiv National University of Radio Electronics, Kharkiv, Ukraine

    Iryna Perova

Authors
  1. Serge Olszewski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yeva Zajets
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Violetta Demchenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Oleg Boskin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mariia Voronenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Volodymyr Lytvynenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Iryna Perova
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Dmitry Stepanchikov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Volodymyr Lytvynenko .

Editor information

Editors and Affiliations

  1. Department of Informatics, Univerzita Jana Evangelisty Purkyně v Ústí nad Labem, Ústí nad Labem, Czech Republic

    Sergii Babichev

  2. GeoGuard, Kharkiv, Ukraine

    Dmytro Peleshko

  3. Kharkiv National University of Radio Electronics, Kharkiv, Ukraine

    Olena Vynokurova

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Olszewski, S. et al. (2020). Expansion of the Capabilities of Chromatography-Mass Spectrometry Due to the Numerical Decomposition of the Signal with the Mutual Superposition of Mass Spectra. In: Babichev, S., Peleshko, D., Vynokurova, O. (eds) Data Stream Mining & Processing. DSMP 2020. Communications in Computer and Information Science, vol 1158. Springer, Cham. https://doi.org/10.1007/978-3-030-61656-4_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-61656-4_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61655-7

  • Online ISBN: 978-3-030-61656-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation