Preview

Industrial laboratory. Diagnostics of materials

Advanced search
Open Access Open Access  Restricted Access Subscription Access

Binary colorimetry in chemical analysis of drinking, natural, and waste water: comparison with photometry

https://doi.org/10.26896/1028-6861-2026-92-6-24-33

Abstract

The article presents the results of a comparative study on the applicability of binary colorimetry versus photometry in the chemical analysis of drinking, natural, and waste waters for iron (II, III), nickel (II), and copper (II) contents. Sample preparation was identical for both methods under consideration. For digital photography of the solutions analised a mobile device under controlled conditions was used. To minimize external light interference, photography was conducted in a darkened room. An assessment of the influence of scattered light showed no significant contribution to the colorimetric measurement results. The obtained images were processed using specialized software and converted to a binary format, subsequently determining the binarization threshold, which was used as the analytical signal. It was found that the relative standard deviations of the results obtained by binary colorimetry and photometry do not exceed 0.12 and 0.06, respectively. Two-way analysis of variance revealed no statistically significant differences in the accuracy of the methods under consideration. However, their difference was demonstrated at different concentration levels of Fe, Ni, and Cu. It was established that in the concentration ranges of 0.10 – 2.00 mg/L Fe, 0.02 – 0.10 mg/L Ni, and 0.25 – 1.50 mg/L Cu, the sensitivity characteristics of binary colorimetry are predictably inferior to those of photometry. It was shown that for the colorimetric determination of Fe (II, III), Ni (II), and Cu (II) at concentration levels close to the detection limits of these ions, the analyzed water sample should be preconcentrated. The results of the conducted study indicate that binary colorimetry can be considered an accessible alternative in the absence of a photometer for the determination of Fe (II, III), Ni (II), and Cu (II) in water.

About the Authors

S. V. Araslankin
Lobachevsky Nizhny Novgorod National Research State University; «Exponenta» LLC
Russian Federation

Sergei V. Araslankin 

23, prosp. Gagarina, Nizhny Novgorod, 603022; 
26A, ul. Stanislavskogo, Ruzaevka, 431448



O. V. Nipruk
Lobachevsky Nizhny Novgorod National Research State University
Russian Federation

Oksana V. Nipruk 

23, prosp. Gagarina, Nizhny Novgorod, 603022, Russia



E. N. Golovina
«Ruzkhimmash» JSC
Russian Federation

Ekaterina N. Golovina

16 bld. 1, ul. Titova, Ruzaevka, 431440



References

1. Zolotov Yu. A. Methodological aspects of analytical chemistry / J. Anal. Chem. 2021. Vol. 76. No. 1. P. 1 – 14. DOI: 10.1134/s1061934821010160

2. Lurye Yu. Yu. Analytical chemistry of industrial wastewater. — Moscow: Khimiya, 1984. — 448 p. [in Russian].

3. Zolotov Yu. A. The general methodology of analytical environmental control / J. Anal. Chem. 2010. Vol. 65. No. 3. P. 221 – 222. DOI: 10.1134/s1061934810030019

4. Monogarova O. V., Oskolok K. V., Apyari V. V. Colorimetry in chemical analysis / J. Anal. Chem. 2018. Vol. 73. No. 11. P. 1076 – 1084. DOI: 10.1134/s1061934818110060

5. Ivanov V. M., Monogarova O. V., Oskolok K. V. Capabilities and prospects of the development of a chromaticity method in analytical chemistry / J. Anal. Chem. 2015. Vol. 70. No. 10. P. 1165 – 1178. DOI: 10.1134/s1061934815100111

6. Shults E. V., Monogarova O. V., Oskolok K. V. Digital colorimetry: analytical possibilities and prospects of use / Moscow Univ. Chem. Bull. 2019. Vol. 74. P. 55 – 62. DOI: 10.3103/S002713141902007x

7. Chaplenko A. A., Monogarova O. V., Oskolok K. V., et al. Digital colorimetry in chemical and pharmaceutical analysis / Moscow Univ. Chem. Bull. 2022. Vol. 77. P. 61 – 67. DOI: 10.3103/s002713142202002x

8. Apyari V. V., Gorbunova M. V., Isachenko A. I., et al. Use of household color-recording devices in quantitative chemical analysis / J. Anal. Chem. 2017. Vol. 72. No. 11. P. 1127 – 1137. DOI: 10.1134/s106193481711003x

9. Shogah Z. A. C., Bolshakov D. S., Amelin V. G. Using smartphones in chemical analysis / J. Anal. Chem. 2023. Vol. 78. No. 4. P. 426 – 449. DOI: 10.1134/s1061934823030139

10. Domasev M. V., Gnatyuk S. P. Color, color management, color calculations and measurements. — St. Petersburg: Piter, 2009. — 224 p. [in Russian].

11. Shapiro L., Stockman J. Computer vision. — Moscow: Matematika, 2013. — 752 p. [in Russian].

12. Lozhkarev A. S., Timofeev I. A. Investigation of the process of binarization of images using local values of the threshold / Prikl. Informatika. 2021. Vol. 16. No. 6. P. 54 – 65 [in Russian]. DOI: 10.37791/2687-0649-2021-16-6-54-65

13. Gonzalez R., Woods R. Digital image processing. — Moscow: Tekhnosfera, 2012. — 1104 p. [in Russian].

14. Araslankin S. V., Shchankin M. V., Golovina E. N., et al. Evaluation of the degree of starch pyrodextrinization using binary digital image colorimetry / Inorg. Mater. 2024. Vol. 60. No. 3. P. 359 – 366. DOI: 10.1134/s002016852470033x

15. Hasenplaugh W. C., Neifeld M. A. Image binarization techniques for correlation-based pattern recognition / Opt. Eng. 1999. Vol. 38. No. 11. P. 1907 – 1917. DOI: 10.1117/1.602241

16. Otsu N. A threshold selection method from gray-level histograms / IEEE Trans. Syst., Man Cybern. 1979. Vol. 9. No. 1. P. 62 – 66. DOI: 10.1109/tsmc.1979.4310076

17. Sauvola J., Pietikäinen M. Adaptive document image binarization / Pattern Recognit. 2000. Vol. 33. No. 2. P. 225 – 236. DOI: 10.1016/s0031-3203(99)00055-2

18. Gatos B., Pratikakis I., Perantonis S. J. Adaptive degraded document image binarization / Pattern Recognit. 2006. Vol. 39. No. 3. P 317 – 327. DOI: 10.1016/j.patcog.2005.09.010

19. Saleh L. O. A., Khlopin S. V., Chernenkaya L. V., et al. Algorithm for determining the concentration of impurities in a liquid from optical data / Izv. TulGU. Tekhn. Nauki. 2023. No. 1. P. 247 – 256 [in Russian]. DOI: 10.24412/2071-6168-2023-1-247-256

20. Mehlig J. Colorimetric determination of copper with ammonia / Ind. Eng. Chem. Anal. Ed. 1941. Vol. 13. No. 8. P. 533 – 535. DOI: 10.1021/i560096a006

21. Lazarev A. I., Kharlamov I. P., Yakovlev P. Ya., et al. Handbook for analytical chemists. — Moscow: Metallurgiya, 1976. — 184 p. [in Russian].

22. Schneider C. A., Rasband W. S., Eliceiri K. W. NIH Image to ImageJ: 25 years of image analysis / Nat. Methods. 2012. Vol. 9. No. 7. P. 671 – 675. DOI: 10.1038/nmeth.2089

23. Akhnazarova S. L., Kafarov V. V. Methods for optimizing an experiment in chemical technology: a textbook for chemical engineering specialties of universities. — Moscow: Vysshaya shkola, 1985. — 327 p. [in Russian].

24. Doerffel K. Statistics in analytical chemistry. — Moscow: Mir, 1994. — 268 p. [in Russian].

25. Eksperiandova L. P., Belikov K. N., Khimchenko S. V., Blank T. A. Once again about determination and detection limits / J. Anal. Chem. 2010. Vol. 65. P. 223 – 228. DOI: 10.1134/s1061934810030020

26. Dvorkin V. I. Metrology and quality assurance of quantitative chemical analysis. — Moscow: Tekhnosfera, 2019. — 318 p. [in Russian].


Review

For citations:


Araslankin S.V., Nipruk O.V., Golovina E.N. Binary colorimetry in chemical analysis of drinking, natural, and waste water: comparison with photometry. Industrial laboratory. Diagnostics of materials. 2026;92(6):24-33. (In Russ.) https://doi.org/10.26896/1028-6861-2026-92-6-24-33

Views: 45

JATS XML

ISSN 1028-6861 (Print)
ISSN 2588-0187 (Online)