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Journal of Analysis and Testing

, Volume 2, Issue 4, pp 291–298 | Cite as

Topological Data Analysis of Potentiometric Multisensor Measurements in Treated Wastewater

  • Valeria Belikova
  • Vitaly Panchuk
  • Evgeny Legin
  • Anastasia Melenteva
  • Andrey Legin
  • Dmitry Kirsanov
Original Paper
  • 139 Downloads

Abstract

In this study, a multisensor system consisting of 23 potentiometric sensors was applied for long-term online measurements in outlet flow of the water treatment plant. Within 1 month of continuous measurements, the data set of more than 295,000 observations was acquired. The processing of this dataset with conventional chemometric tools was cumbersome and not very informative. Topological data analysis (TDA) was recently suggested in chemometric literature to deal with large spectroscopic datasets. In this research, we explore the opportunities of TDA with respect to multisensor data with only 23 variables. It is shown that TDA allows for convenient data visualization, studying the evolution of water quality during the measurements and tracking the periodical structure in the data related to the water quality depending on the time of the day and the day of the week. TDA appears to be a valuable tool for multisensor data exploration.

Keywords

Topological data analysis Multisensor systems Potentiometric sensors Water quality 

Notes

Acknowledgements

The authors are grateful to O. Lominoga and Zh. Lyadova from SUE “Vodokanal of St. Petersburg” for their valuable help in organizing the experiments. DK acknowledges financial support from RFBR project #17-33-50101. EL and AL acknowledge partial financial support from the Government of Russian Federation, Grant 08-08. VB thanks the Russian Ministry of Education and Science for support of this work within the framework of the basic part of the state task on the theme: “Adaptive technologies of analytical control based on optical sensors” (Project No. 4.7001.2017/BP).

References

  1. 1.
    Lvova L, Kirsanov D, Di Natale C, Legin A. Multisensor systems for chemical analysis: materials and sensors. Singapore: Pan Stanford Publishers; 2013. p. 1–392.  https://doi.org/10.4032/9789814411165.CrossRefGoogle Scholar
  2. 2.
    Krantz-Rülcker C, Stenberg M, Winquist F, Lundström I. Electronic tongues for environmental monitoring based on sensor arrays and pattern recognition: a review. Anal Chim Acta. 2001;426:217–26.  https://doi.org/10.1016/S0003-2670(00)00873-4.CrossRefGoogle Scholar
  3. 3.
    Kirsanov D, Zadorozhnaya O, Krasheninnikov A, Komarova N, Popov A, Legin A. Water toxicity evaluation in terms of bioassay with an Electronic Tongue. Sens Actuat B Chem. 2013;179:282–6.  https://doi.org/10.1016/j.snb.2012.09.106.CrossRefGoogle Scholar
  4. 4.
    Dias LA, Peres AM, Veloso ACA, Reis FS, Vilas-Boas M, Machado AASC. An electronic tongue taste evaluation: identification of goat milk adulteration with bovine milk. Sens Actuat B Chem. 2009;136:209–17.  https://doi.org/10.1016/j.snb.2008.09.025.CrossRefGoogle Scholar
  5. 5.
    Peris M, Escuder-Gilabert L. Electronic noses and tongues to assess food authenticity and adulteration. Trends Food Sci Technol. 2016;58:40–54.  https://doi.org/10.1016/j.tifs.2016.10.014.CrossRefGoogle Scholar
  6. 6.
    Carlsson G. Topology and data. Bul Amer Math Soc. 2009;46:255–308.  https://doi.org/10.1090/S0273-0979-09-01249-X.CrossRefGoogle Scholar
  7. 7.
    Offroy M, Duponchel L. Topological data analysis: a promising big data exploration tool in biology, analytical chemistry and physical chemistry. Anal Chim Acta. 2016;910:1–11.  https://doi.org/10.1016/j.aca.2015.12.037.CrossRefPubMedGoogle Scholar
  8. 8.
    Duponchel L. Exploring hyperspectral imaging data sets with topological data analysis. Anal Chim Acta. 2018;1000:123–31.  https://doi.org/10.1016/j.aca.2017.11.029.CrossRefPubMedGoogle Scholar
  9. 9.
    Savic A, Toth G, Duponchel L. Topological data analysis (TDA) applied to reveal pedogenetic principles of European topsoil system. Sci Total Environ. 2017;586:1091–100.  https://doi.org/10.1016/j.scitotenv.2017.02.095.CrossRefPubMedGoogle Scholar
  10. 10.
    Vogel AI, Svehla G. Vogel’s Textbook of Macro and Semimicro Qualitative Inorganic Analysis. 5th ed. London: Longman; 1979 (ISBN 0-582-44367-9).Google Scholar
  11. 11.
    Wold S, Sjostrom M, Eriksson L. PLS-regression: a basic tool of chemometrics. Chemom Intell Lab Syst. 2001;58:109–30.  https://doi.org/10.1016/S0169-7439(01)00155-1.CrossRefGoogle Scholar
  12. 12.
    Bro R, Smilde A. Principal component analysis. Anal Methods. 2014;6:2812–31.CrossRefGoogle Scholar
  13. 13.
    Halko N, Martinsson PG, Tropp JA. Finding structure with randomness: probabilistic algorithms for constructing approximate matrix decompositions. SIAM Rev. 2010;53:217–88.  https://doi.org/10.1137/090771806.CrossRefGoogle Scholar
  14. 14.
    R Development Core Team. R: a language and environment for statistical computing, R Foundation for statistical computing. Vienna: R-project.org; 2010.Google Scholar
  15. 15.
    Singh G, Memoli F, Carlsson G. Topological methods for the analysis of high dimensional data sets and 3D object recognition. In: Botsch PM, Pajarola R, editors. Prague: Eurographics Symposium on Point-Based Graphics; 2007.Google Scholar

Copyright information

© The Nonferrous Metals Society of China 2018

Authors and Affiliations

  1. 1.Laboratory of Multivariate Analysis and Global ModelingSamara State Technical UniversitySamaraRussia
  2. 2.Institute of ChemistrySt. Petersburg State UniversitySt. PetersburgRussia
  3. 3.Laboratory of Artificial Sensory SystemsITMO UniversitySt. PetersburgRussia

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