Advertisement

Russian Journal of General Chemistry

, Volume 89, Issue 7, pp 1438–1446 | Cite as

Artificial Neural Network and Multiple Linear Regression for Prediction and Classification of Sustainability of Sodium and Potassium Coronates

  • N. V. BondarevEmail author
Article
  • 1 Downloads

Abstract

Models of multiple linear regression and multilayer artificial neural network have been developed for modeling and predicting the stability constants of sodium and potassium coronates basing on the properties of aqueous-organic solvents (water-methanol, water-propan-2-ol, water-acetonitrile, and water-acetone). The values of the coronates stability constants in water-ethanol solvents have been predicted, and the predictions of the models of multiple linear regression and an artificial neural network models have been compared. The contributions of electrostatic, cohesive, and electron-donating interactions to the increase in the stability of the coronates have been quantitatively assessed basing on the models of multiple linear regression and the principle of free energies linearity. Neural network models based on unsupervised (multilayer perceptrons) and supervised (Kohonen networks) learning algorithms have been developed to classify the stability of sodium and potassium coronates. The neural network classifiers have fully confirmed the classification of the coronated stability via the k-means exploration method.

Keywords

multiple regression multilayer perceptron Kohonen network prediction neural network classifier 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Artificial Neural Networks. Architectures and Applications, Suzuki, K., Ed., Chicago: University of Chicago, 2013. doi  https://doi.org/10.5772/3409 Google Scholar
  2. 2.
    Himmelblau, D.M., Korean Z. Chem. Eng., 2000, vol. 17, no. 4, p. 373. doi  https://doi.org/10.1007/BF02706848 CrossRefGoogle Scholar
  3. 3.
    Marini, F., Bucci, R., Magrì, A.L., and Magrì, A.D., Microchem. J., 2008, vol. 88, p. 178. doi  https://doi.org/10.1016/j.microc.2007.11.008 CrossRefGoogle Scholar
  4. 4.
    Huang, R.B., Du, Q.S., Wei, Y.T., Pang, Z.W., Wei, H., and Chou, K.C., J. Theor. Biol., 2009, vol. 256, no. 3, p. 428. doi  https://doi.org/10.1016/j.jtbi.2008.08.028 CrossRefGoogle Scholar
  5. 5.
    Bondarev, N.V., Russ. J. Gen. Chem., 2017, vol. 87, no. 2, p. 188. doi  https://doi.org/10.1134/S1070363217020062 CrossRefGoogle Scholar
  6. 6.
    Blanco, M., Coello, J., Iturriaga, H., Maspoch, S., and Redón, M., Anal. Chem., 1995, vol. 67, no. 24, p. 4477. doi  https://doi.org/10.1021/ac00120a008 CrossRefGoogle Scholar
  7. 7.
    Meiler, J., J. Biomol. NMR, 2003, vol. 26, no. 1, p. 25. doi  https://doi.org/10.1023/A:1023060720156 CrossRefGoogle Scholar
  8. 8.
    Nicelyab, J.M., Haniscob, T.F., and Ririsb, H., J. Quant. Spectrosc. Rad. Trans., 2018, vol. 211, p. 115. doi  https://doi.org/10.1016/j.jqsrt.2018.03.004 CrossRefGoogle Scholar
  9. 9.
    Tetko, I.V. and Tanchuk, V.Yu., J. Chem. Inf. Comput. Sci., 2002, vol. 42, no. 5, p. 1136. doi  https://doi.org/10.1021/ci025515j CrossRefGoogle Scholar
  10. 10.
    Tetko, I.V., Tanchuk, V.Y., and Villa, A.E., J. Chem. Inf. Comput. Sci., 2001, vol. 41, no. 5, p. 1407. doi  https://doi.org/10.1021/ci010368v CrossRefGoogle Scholar
  11. 11.
    Wang, B., Valentine, S., Plasencia, M., Raghuraman, S., and Zhang, X., BMC Bioinformatics, 2010, vol. 11, p. 182. doi  https://doi.org/10.1186/1471-2105-11-182 CrossRefGoogle Scholar
  12. 12.
    Kavšek, D., Bednárová, A., Biro, M., Kranvogl, R., Vončina, D.B., and Beinrohr, E., Cent. Eur. J. Chem., 2013, vol. 11, no. 9, p. 1481. doi  https://doi.org/10.2478/s11532-013-0280-x Google Scholar
  13. 13.
    Doua, Y., Sunb, Y., Renc, Y., and Rena, Y., Anal. Chim. Acta, 2005, vol. 528, no. 1, p. 55. doi  https://doi.org/10.1016/j.aca.2004.10.050 CrossRefGoogle Scholar
  14. 14.
    Wang, B., Liub, G., Liuc, S., Feia, Q., and Rena, Y., Vibr. Spectrosc., 2009, vol. 51, no. 2, p. 199. doi  https://doi.org/10.1016/j.vibspec.2009.04.007 CrossRefGoogle Scholar
  15. 15.
    Jovanović, M., Sokić, D., Grabnar, I., Vovk, T., Prostran, M., Erić, S., Kuzmanovski, I., Vučićević, K., and Miljković, B., J. Pharm. Pharm. Sci., 2015., 2015, vol. 18, no. 5, p. 856. doi  https://doi.org/10.18433/J33031 CrossRefGoogle Scholar
  16. 16.
    de Molfetta, F.A., Angelotti, W.F., Romero, R.A., Montanari, C.A., and da Silva, A.B., J. Mol. Model., 2008, vol. 14, no. 10, p. 9755. doi  https://doi.org/10.1007/s00894-008-0332-x CrossRefGoogle Scholar
  17. 17.
    Nandi, S., Vracko, M., and Bagchi, M.C., Chem. Biol. Drug Des., 2007, vol. 70, no. 5, p. 424. doi  https://doi.org/10.1111/j.1747-0285.2007.00575.x CrossRefGoogle Scholar
  18. 18.
    Cheng, F. and Vjaykumar, S., Clin. Exp. Pharmacol., 2012, vol. 2, p. 113. doi  https://doi.org/10.4172/2161-1459.1000e113 CrossRefGoogle Scholar
  19. 19.
    Honório, K.M., de Lima, E.F., Quiles, M.G., Romero, R.A., Molfetta, F.A., and da Silva, A.B., Chem. Biol. Drug Des., 2010, vol. 75, no. 6, p. 632. doi  https://doi.org/10.1111/j.1747-0285.2010.00966x CrossRefGoogle Scholar
  20. 20.
    Inci, C., Ayse, Y., Kürsad, U.M., Askin, D., Serap, C., and Omca, D., J. Food Nutr. Res., 2017, vol. 56, no. 2, p. 138.Google Scholar
  21. 21.
    Baha, H. and Dibi, Z., Sensors (Basel), 2009, vol. 9, no. 11, p. 8944. doi  https://doi.org/10.3390/s91108944 CrossRefGoogle Scholar
  22. 22.
    Padín, P.M., Peña, R.M., García, S., Iglesias, R., Barro, S., and Herrero, C., Analyst., 2001, vol. 126, no. 1, p. 97. doi  https://doi.org/10.1039/B007720H CrossRefGoogle Scholar
  23. 23.
    Moldes, O.A., Mejuto, J.C., Rial-Otero, R., and Simal-Gandara, J., Crit. Rev. Food Sci. Nutr., 2017, vol. 57, no. 13, p. 2896. doi  https://doi.org/10.1080/10408398.2015.1078277 CrossRefGoogle Scholar
  24. 24.
    Wine: Phenolic Composition, Classification and Health Benefits, New York: Nova Science Publishers, Inc., 2014, ch. 10, p. 245.Google Scholar
  25. 25.
    Penza, M. and Cassano, G., Food Chem., 2004, vol. 86, no. 2, p. 283. doi  https://doi.org/10.1016/j.foodchem.2003.09.027 CrossRefGoogle Scholar
  26. 26.
    Latorre, M.J., Peña, R., García, S., and Herrero, C., Analyst., 2000, vol. 125, p. 307. doi  https://doi.org/10.1039/A905978D CrossRefGoogle Scholar
  27. 27.
    Cordella, C.B., Militão, J.S., Clément, M.C., and CabrolBass, D., J. Agric. Food Chem., 2003, vol. 51, no. 11, p. 3234. doi  https://doi.org/10.1021/jf021100m CrossRefGoogle Scholar
  28. 28.
    Bos, A., Bos, M., and van der Linden, W.E., Anal. Chim. Acta, 1992, vol. 256, no. 1, p. 133. doi  https://doi.org/10.1016/0003-2670(92)85338-7 CrossRefGoogle Scholar
  29. 29.
    Cimpoiu C., Cristea, V.M., Hosu A., Sandru M., and Seserman L., Food Chem. 2011, vol. 127, no. 3, p. 1323. doi  https://doi.org/10.1016/j.foodchem.2011.01.091 CrossRefGoogle Scholar
  30. 30.
    Angerosa, F., Di Giacinto, L., Vito, R., and Cumitini, S., J. Sci. Food Agric., 1996, vol. 72, no. 3, p. 323. doi  https://doi.org/10.1002/(SICI)1097-0010(199611)72:3<323::AID-JSFA662>3.0.CO;2-A CrossRefGoogle Scholar
  31. 31.
    Zhang, G., Ni, Y., Churchill, J., and Kokot, S., Talanta, 2006, vol. 70, no. 2, p. 293. doi  https://doi.org/10.1016/j.talanta.2006.02.037 CrossRefGoogle Scholar
  32. 32.
    Cirovic, D.A., TrAC Trends Anal. Chem., 1997, vol. 16, no. 3, p. 148. doi  https://doi.org/10.1016/S0165-9936(97)00007-1 CrossRefGoogle Scholar
  33. 33.
    Meyer, M. and Weigelt, T., Anal. Chim. Acta, 1992, vol. 265, no. 2, p. 183. doi  https://doi.org/10.1016/0003-2670(92)85024-Z CrossRefGoogle Scholar
  34. 34.
    Amato, F., López, A., Méndez, E.M., Vaňhara, P., Hampl, A., and Havel, J., J. Appl. Biomed., 2013, vol. 11, no. 2, p. 47. doi  https://doi.org/10.2478/v10136-012-0031-x CrossRefGoogle Scholar
  35. 35.
    Maran, E., Novic, M., Barbieri, P., and Zupan, J., SAR QSAR Environ Res., 2004, vol. 15, nos. 5–6, p. 469. doi  https://doi.org/10.1080/10629360412331297461 CrossRefGoogle Scholar
  36. 36.
    Allison, T.C., J. Phys. Chem. (B), 2016, vol. 120, no. 8, p. 1854. doi  https://doi.org/10.1021/acs.jpcb.5b09558 CrossRefGoogle Scholar
  37. 37.
    Elçiçek, H., Akdoğan, E., and Karagöz, S., Sci. World J., 2014, vol. 2014, p. 9. doi  https://doi.org/10.1155/2014/194874 CrossRefGoogle Scholar
  38. 38.
    Rekha, C.R., Nayar, V.U., and Gopchandran, K.G., Optik, 2018, vol. 172, p. 721. doi  https://doi.org/10.1016/j.ijleo.2018.07.090 CrossRefGoogle Scholar
  39. 39.
    Sigman, M.E. and Rives, S.S., J. Chem. Inf. Comput. Sci., 1994, vol. 34, no. 3, p. 617. doi  https://doi.org/10.1021/ci00019a021 CrossRefGoogle Scholar
  40. 40.
    DiRusso, S.M., Sullivan, T., Holly, C., Cuff, S.N., and Savino, J., J. Trauma, 2000, vol. 49, no. 2, p. 212. doi  https://doi.org/10.1097/00005373-200008000-00006 CrossRefGoogle Scholar
  41. 41.
    Myint, K.Z. and Xie X-Q., Methods Mol. Biol., 2015, no. 1260, p. 149. doi  https://doi.org/10.1007/978-1-4939-2239-0_9
  42. 42.
    Wei, J.N., Duvenaud, D., and Aspuru-Guzik, A., ACS Cent. Sci., 2016, vol. 2, no. 10, p. 725. doi  https://doi.org/10.1021/acscentsci.6b00219 CrossRefGoogle Scholar
  43. 43.
    Environmental Medium Effects and Neural Network Analysis, Saarbrucken: LAP LAMBERT Academic Publishing, 2012.Google Scholar
  44. 44.
    Borovikov, V.P., STATISTICA. Iskusstvo analiza dannykh na komp’yutere: Dlya professionalov, (STATISTICA. The Art of Analyzing Data on a Computer: For Professionals), St. Petersburg: Piter 2003.Google Scholar
  45. 45.
    Borovikov, V.P., Populyarnoe vvedenie v sovremennyi analiz dannykh v sisteme STATISTICA. Uchebnoe posobie dlya vuzov, (A Popular Introduction to Modern Data Analysis in the STATISTICA System. Textbook for Universities), Moscow: Goryachaya LiniyaTelekom 2013.Google Scholar
  46. 46.
    Khaikin, S., Neironnye seti, (Neuron Networks), Moscow: Vil’yams 2006.Google Scholar
  47. 47.
    Gill, P.E., Murray, W., and Wright, M.H., Practical Optimization, London: Academic Press, 1981.Google Scholar
  48. 48.
    Bondarev, N.V., Russ. J. Gen. Chem., 2019, vol. 89, no. 2, p. 281. doi  https://doi.org/10.1134/S1070363219020191 CrossRefGoogle Scholar
  49. 49.
    Kohonen, T., Self-Organizing Maps, Springer-Verlag, 2001.Google Scholar
  50. 50.
    Kholin, Yu.V., Pushkareva, Ya.M., Panteleimonov, A.V., and Nekos, A.N., Khemometrichni metodu v rozv’yazanni zadach yakisnogo khimichnogo analizu ta klasifikaciï fiziko-khimichnih danikh, Kharkiv: KhNU im. V.N. Karazina, 2016.Google Scholar
  51. 51.
    de Boer, P.-T., Kroese, D., Mannor, S., and Rubinstein, R.Y., Ann. Oper. Res., 2005, vol. 134, no. 1, p. 19. doi  https://doi.org/10.1007/s10479-005-5724-z CrossRefGoogle Scholar
  52. 52.
    Schmid, R. and Sapunov, V.I., Informal Kinetics. Searches for Paths of Chemical Reactions, Moscow: Mir, 1985.Google Scholar

Copyright information

© Pleiades Publishing, Ltd. 2019

Authors and Affiliations

  1. 1.V.N. Karazin Kharkiv National UniversityKharkivUkraine

Personalised recommendations