Method for Selecting Combinations of Optical Chemosensory Materials for Identification of Vapor-Phase Ecotoxicants

Introduction. Environmental pollution represents a serious threat to the public and ecological safety. Cross-reactive optical chemosensor materials and their combinations can be effectively used for timely identification of ecotoxicants in the vapor phase. The selection of chemosensor combinations for a reliable identification of toxic substances has received insufficient research attention. As a rule, all available chemosensor materials are used, although a smaller combination may be more reliable and informative.

Aim. To propose a method for selecting from a set of available chemosensory materials the optimal combination most suitable for identification of the required group of vapor-phase substances.

Materials and methods. A metric for assessing the quality of a chemosensor material combination for identification of toxic substances is proposed. This metric numerically describes the degree of orthogonality and the proximity of distributions of response vectors of the combination to the exposure to analyzed substances. On the basis of the metric, a method for selecting optimal combinations is formulated. Classification models based on the support vector method and the principal components method are used to classify responses of the combination of materials. The proposed method is tested on the example of selecting combinations of permeable fluorescent materials for identification of saturated vapor-phase nitroaromatic ecotoxicants and interfering substances.

Results. A combination of permeable fluorescent materials, sufficiently reliable for identification of vapor-phase nitroaromatic ecotoxicants, was determined. The possibility of rapid identification of toxic substances during the prolonged exposure of materials to their vapors is presented. It is shown that the quality metric is lower for a combination of all available fluorescent materials compared to a smaller combination selected via the proposed method.

Conclusion. An approach to solving the problem of finding an optimal combination of chemosensory materials for identification of the specified group of substances is proposed. The proposed method increases the reliability of identification of toxic substances while reducing the number of chemosensory materials involved in the process.

1. Harbison R. D., Bourgeois M. M., Johnson G. T. Hamilton and Hardy's Industrial Toxicology. 6th ed. Hoboken, Wiley, 2015, 1376 p. doi: 10.1002/9781118834015

2. Ewing R. G., Waltman M. J., Atkinson D. A., Grate J. W., Hotchkiss P. J. The Vapor Pressures of Explosives. Trends in Analytical Chemistry. 2013, vol. 42, pp. 35–48. doi: 10.1016/j.trac.2012.09.010

3. Östmark H., Wallin S., Ang H. G. Vapor Pres-sure of Explosives: A Critical Review. Propell. Explos. Pyrot. 2012, vol. 37, pp. 12–23. doi: 10.1002/prep.201100083

4. Giannoukos S., Brkić B., Taylor S., Marshall A., Verbeck G. F. Chemical Sniffing Instrumentation for Security Applications. Chem. Rev. 2016, vol. 116, iss. 14, pp. 8146–8172. doi: 10.1021/acs.chemrev.6b00065

5. Daeid N. N., Yu H. A., Beardah M. S. Investi-gating TNT Loss Between Sample Collection and Analysis. Science & Justice. 2016, vol. 57, iss. 2, pp. 95–100. doi: 10.1016/j.scijus.2016.10.007

6. Verbitskiy E. V., Rusinov G. L., Chupakhin O. N., Charushin V. N. Design of Fluorescent Sensors Based on Azaheterocyclic Push-Pull Systems Towards Nitroaromatic Explosives and Related Compounds: A Review. Dyes Pigm. 2020, vol. 180, p. 108414. doi: 10.1016/j.dyepig.2020.108414

7. Zyryanov G. V., Kopchuk D. S., Kovalev I. S., Rusinov V. L., Chupakhin O. N. Chemosensors for Detection of Nitroaromatic Compounds (Explosives). Russian Chemical Reviews. 2014, vol. 83, iss. 9, pp. 783–819. doi: 10.1070/RC2014v083n09ABEH00446 (In Russ.)

8. Fido X4. Available at: https://archive.fo/aC1Oy (accessed: 04.04.2024)

9. Askim J. R., Suslick K. S. Hand-Held Reader for Colorimetric Sensor Arrays. Anal. Chem. 2015, vol. 87, iss. 15, pp. 7810–7816. doi: 10.1021/acs.analchem.5b01499

10. Li Z., Suslick K. S. The Optoelectronic Nose. Acc. Chem. Res. 2021, vol. 54, pp. 950–960. doi: 10.1021/acs.accounts.0c00671

11. Liu K., Shang C., Wang Z., Miao R., Liu K., Liu T., Fang Y. Non-Contact Identification and Differentiation of Illicit Drugs Using Fluorescent Films. Nature Communications. 2018, vol. 9, art. num. 1695. doi: 10.1038/s41467-018-04119-6

12. Li Z., Askim J. R., Suslick K. S. The Optoelectronic Nose: Colorimetric and Fluorometric Sensor Arrays. Chem. Rev. 2019, vol. 119, iss. 1, pp. 231–292. doi: 10.1021/acs.chemrev.8b00226

13. Bolse N., Eckstein R., Schend M., Habermehl A., Eschenbaum C., Hernandez-Sosa G., Lemmer U. A Digitally Printed Optoelectronic Nose for the Selective Trace Detection of Nitroaromatic Explosive Vapours Using Fluorescence Quenching. Flex. Print. Electron. 2017, vol. 2, p. 024001. doi: 10.1088/2058-8585/aa6601

14. Hastie T., Tibshirani R., Friedman J. The EleMents of Statistical Learning. 2nd ed. New York, Springer New York, 2009, 745 p. doi: 10.1007/978-0-387-84858-7

15. Jolliffe I. T., Cadima J. Principal component analysis: a review and recent developments. Phil. Trans. R. Soc. A. 2016, vol. 374, p. 20150202. doi: 10.1098/rsta.2015.0202

16. Chuvashov R. D., Baranova A. A., Khokhlov K. O., Kvashnin Y. A., Verbitskiy E. V. Combinations of Luminescent Materials for Unambiguous Identification of Nitrocompound Vapors. Life Safety. Security Technologies. 2023, vol. 4, pp. 5–16. doi: 10.17223/7783494/4/1 (In Russ.)

17. Chuvashov R. D., Zhilina E. F., Lugovik K. I., Baranova A. A., Khokhlov K. O., et al. Trimethylsilylethynyl-Substituted Pyrene Doped Materials as Improved Fluorescent Sensors towards Nitroaromatic Explosives and Related Compounds. Chemosensors. 2023, vol. 11, iss. 3, p. 167. doi: 10.3390/chemosensors11030167

18. Zen Eddin M., Zhilina E. F., Chuvashov R. D., Dubovik A. I., Mekhave A. V., Chistyakov K. A., Baranova A. A., Khokhlov K. O., Rusinov G. L., Verbitskiy E. V., Charushin V. N. Random Copolymers of Styrene with Pendant Fluorophore Moieties: Synthesis and Applications as Fluorescence Sensors for Nitroaromatics. Molecules. 2022, vol. 27, no. 20, p. 6957. doi: 10.3390/molecules27206957

19. Lynch E. J., Wilke C. R. Vapor Pressure of Nitro-benzene at Low Temperatures. J. Chem. Eng. Data. 1960, vol. 5, iss. 3, p. 300. doi: 10.1021/je60007a018

20. Dykyj J., Svoboda J., Wilhoit R.C., Frenkel M., Hall K. R. Vapor Pressure of Chemicals. Subvolume B: Vapor Pressure and Antoine Constants for Oxygen Containing Organic Compounds. Berlin, Springer-Verlag, 2000, 327 p.

21. Toluene. NIST Chemistry WebBook. Available at: https://archive.fo/luQNt (accessed: 04.04.2024)

22. Ammonia. NIST Chemistry WebBook. Available at: https://archive.fo/QbeZz (accessed: 04.04.2024)

23. Dichlorobenzene. NIST Chemistry WebBook. Available at: https://archive.fo/T29XZ (accessed: 04.04.2024)

24. Chuvashov R. D., Belyaev D. V., Khokhlov K. O., Baranova A. A., Eddin M. Z., Milman I. I., Verbitskiy E. V. Fluorescent detection of nitrobenzene vapors via fluorophore-doped polystyrene materials. Analytics and Control. 2022, vol. 26, no. 4, pp. 284–297. doi: 10.15826/analitika.2022.26.4.005 (In Russ.)

25. Gupta A., Biswas B., Dutta T. Approaches and Applications of Early Classification of Time Series: A Review. IEEE Transactions on Artificial Intelligence. 2020, vol. 1, iss. 1, pp. 47–61. doi: 10.1109/TAI.2020.3027279

26. Brereton R. G. The Chi Squared and Multinormal Distributions. J. Chemometrics. 2014, vol. 29, pp. 9–12. doi: 10.1002/cem.2680