Cite this article as:

Monakhova Y. B., Kuznetsova I. V. Chemometric Algorithms for the Monitoring of Milk Quality by Potentiometric Titration. Izvestiya of Saratov University. Chemistry. Biology. Ecology, 2019, vol. 19, iss. 4, pp. 387-395. DOI:

This is an open access article distributed under the terms of Creative Commons Attribution 4.0 International License (CC-BY 4.0).

Chemometric Algorithms for the Monitoring of Milk Quality by Potentiometric Titration


The acute problem in the analysis of dairy products is the potentiometric determination of the active and total titrated acidity of pasteurized milk from different manufacturers and their comparison with those for raw cow milk. In addition, for various expert purposes, fast definition of the manufacturer of this product is necessary. The conditions of potentiometric titration of pasteurized milk are specified. It is shown that the total acidity of pasteurized milk produced by different manufacturers differs insignificantly. Therefore, for modelling potentiometric titration curves of milk samples ICA and PCA methods were applied. ICA surpasses PCA in terms of reliability of class separation. ICA method solved the classification problem of assigning milk samples to a specific manufacturer, similarity with raw cow milk samples, as well as detecting products made from different raw materials or using different technologies.

  1. Siqueira L. F, Lima K. M. MIR-biospectroscopy coupled with chemometrics in cancer studies. Analyst, 2016, vol. 141, no. 16, pp. 4833–4847.
  2. Monakhova Y. B., Holzgrabe U., Diehl B. W. K. Current role and future perspectives of multivariate (chemometric) methods in NMR spectroscopic analysis of pharmaceutical products. J. Pharm. Biomed. Anal., 2018, vol. 147, pp. 580–589.
  3. Efenberger-Szmechtyk M., Nowak A., Kregiel D. Implementation of chemometrics in quality evaluation of food and beverages. Crit. Rev. Food Sci. Nutr., 2018, vol. 58, no. 10, pp. 1747–1766.
  4. Peets P., Leito I., Pelt J., Vahur S. Identifi cation and classifi cation of textile fi bres using ATR-FT-IR spectroscopy with chemometric methods. Spectrochim. Acta A. Mol. Biomol. Spectrosc., 2017, vol. 173, pp. 175–181.
  5. Elmi F., Movaghar A. F., Elmi M. M., Alinezhad H., Nikbakhsh N. Application of FT-IR spectroscopy on breast cancer serum analysis. Spectrochim. Acta A Mol. Biomol. Spectrosc., 2017, vol. 187, pp. 87–91.
  6. Ardila J. A, Funari C. S., Andrade A. M., Cavalheiro A. J., Carneiro R. L. Cluster analysis of commercial samples of Bauhinia spp. using HPLC-UV/ PDA and MCR-ALS/PCA without peak alignment procedure. Phytochem. Anal., 2015, vol. 26, no. 5, pp. 367–373.
  7. Mejia A. F., Nebel M. B., Eloyan A., Caffo B., Lindquist M. A. PCA leverage: outlier detection for highdimensional functional magnetic resonance imaging data. Biostatistics, 2017, vol. 18, no. 3, pp. 521–536.
  8. GOST 32892-2014. Moloko i molochnaia produktsiia. Metod izmereniia aktivnoi kislotnosti [State Standard 32892-2014 Milk and Dairy Products: Active Acidity Measurement Technique]. Moscow, 2015. 10 p. (in Russian).
  9. GOST R 54669-2011. Moloko i produkty pererabotki moloka. Metody opredeleniia kislotnosti [Russian State Standard 54669-2011 Milk and Dairy Products. Acidity Measurement Techniques]. Мoscow, 2013. 11 p. (in Russian).
  10. Ni Y., Kokot S. Does chemometrics enhance the performance of electroanalysis? Anal. Chim. Acta, 2008, vol. 626, no. 2, pp. 130–146.
  11. Yaroshenko I., Kirsanov D., Kartsova L., Sidorova A., Borisova I., Legin A. Determination of urine ionic composition with potentiometric multisensor system. Talanta, 2015, vol. 131, pp. 556–561.
  12. Terouzi W., Omari S., Boutoial K., Oussama A. Quantitative Detection of Cow Milk in Goat Milk by Chemometrics Analysis Based on Mid Infrared Spectroscopy Journal of Research in Agriculture and Animal. Science, 2016, vol. 4, no. 1, pp. 1–7.
  13. Cossignani L., Blasi F., Blasi F., Bosi A., Damiani P. Detection of cow milk in donkey milk by chemometric procedures on triacylglycerol stereospecifi c analysis results. J. of Dairy Research, 2011, vol. 78, no. 3, pp. 335–342.
  14. Souza S. S., Cruz A. G., Walter E. H. M., Fari J. A. F., Celeghini R. M. S., Ferreira M. M. C., Granatod D., Sant’Ana S.Monitoring the authenticity of Brazilian UHT milk. A Chemometric Approachood Chemistry, 2011, vol. 124, no. 2, pp. 692–695.
  15. Cordella C.B.Y., Bertrand D. SAISIR A new general chemometric toolbox. Trends Anal. Chem., 2014, vol. 54, no. 2, pp. 75–82.
  16. Stögbauer H., Kraskov A., Astakhov S. A., Grassberger P. Least-dependent-component analysis based on mutual information. Phys. Rev. E. Stat. Nonlin. Soft Matter. Phys., 2004, vol. 70, no. 6, pp. 066123.
  17. Harlee F. R., Burgess C., Alcock R. M. Solution Equilibria. Hemel Hempstead. Horwood Ellis, Ltd. 1980. 362 p. (Russ. ed.: F. Khartli K. Berges R. Olkok Ravnovesiia v rastvorakh. Moscow, Mir Publ., 1994. 360 p.). Химия 395
  18. Monakhova Yu. B., Kuballa T., Lachenmeier D. V. Chemometric Methods in NMR Spectroscopic Analysis of Food Products. Journal of Analytical Chemistry, 2013, vol. 68, iss. 9, pp. 755–766. DOI:  https://
  19. Monakhova Y. B., Kuballa T., Leitz J., Andlauer C., Lachenmeier D. W. NMR spectroscopy as a screening tool to validate nutrition labeling of milk, lactose-free milk, and milk substitutes based on soy and grains. Dairy Sci. Technol., 2012, vol. 92, no. 2, pp. 109–120.
  20. Monakhova Yu. B., Tsikin A. M., Mushtakova S. P. Independent Component Analysis as an Alternative to Principal Component Analysis and Discriminant Algorithms in the Processing of Spectroscopic Data. Journal of Analytical C hemistry, 2015, vol. 70, iss. 9, pp. 1055– 1061. DOI:
Full text (in Russian):