A Bayesian Regularized Artificial Neural Network for Simultaneous Determination of Loratadine, Naproxen and Diclofenac in Wastewaters

Author(s): Mojtaba Mohammadpoor, Roya Mohammadzadeh Kakhki*, Hakimeh Assadi

Journal Name: Current Pharmaceutical Analysis

Volume 16 , Issue 8 , 2020

Become EABM
Become Reviewer
Call for Editor

Graphical Abstract:


Background: Simultaneous determination of medication components in pharmaceutical samples using ordinary methods have some difficulties and therefore these determinations usually were made by expensive methods and instruments. Chemometric methods are an effective way to analyze several components simultaneously.

Objective: In this paper, a novel approach based on Bayesian regularized artificial neural network is developed for the determination of Loratadine, Naproxen, and Diclofenac in water using UV-Vis spectroscopy.

Methods: A dataset is collected by performing several chemical experiments and recording the UV-Vis spectra and actual constituent values. The effect of a different number of neurons in the hidden layer was analyzed based on final mean square error, and the optimum number was selected. Principle Component Analysis (PCA) was also applied to the data. Other back-propagation methods, such as Levenberg-Marquardt, scaled conjugate gradient, and resilient backpropagation, were tested.

Results: In order to see the proposed network performance, it was performed on two crossvalidation methods, namely partitioning data into train and test parts, and leave-one-out technique. Mean square errors between expected results and predicted ones implied that the proposed method has a strong ability in predicting the expected values.

Conclusion: The results showed that the Bayesian regularization algorithm has the best performance among other methods for simultaneous determination of Loratadine, Naproxen, and Diclofenac in water samples.

Keywords: Bayesian regularized artificial neural networks, loratadine, naproxen, diclofenac, UV-Vis spectroscopy, principle component analysis.

Boxall, A.B.; Rudd, M.A.; Brooks, B.W.; Caldwell, D.J.; Choi, K.; Hickmann, S.; Innes, E.; Ostapyk, K.; Staveley, J.P.; Verslycke, T.; Ankley, G.T.; Beazley, K.F.; Belanger, S.E.; Berninger, J.P. Carri- quiriborde, P.; Coors, A.; Deleo, P.C.; Dyer, S.D.; Ericson, J.F.; Gagné, F.; Giesy, J.P.; Gouin, T.; Hallstrom, L.; Karlsson, M.V.; Larsson, D.G.; Lazorchak, J.M.; Mastrocco, F.; McLaughlin, A.; McMaster, M.E.; Meyerhoff, R.D.; Moore, R.; Parrott, J.L.; Snape, J.R.; Murray-Smith, R.; Servos, M.R.; Sibley, P.K.; Straub, J.O.; Szabo, N.D.; Topp, E.; Tetreault, G.R.; Trudeau, V.L.; Van Der Kraak, G. Pharmaceuticals and personal care products in the envi- ronment: what are the big questions? Environ. Health Perspect., 2012, 120(9), 1221-1229.
[http://dx.doi.org/10.1289/ehp.1104477] [PMID: 22647657]
Boynton, C. Dick, C. Mayer,G. NSAIDs: An Overview. Clin. Pharmacol., 1988, 28(6), 512-517.
Elsinghorst, P.W.; Kinzig, M.; Rodamer, M.; Holzgrabe, U.; Sörgel, F. An LC-MS/MS procedure for the quantification of naproxen in human plasma: development, validation, comparison with other methods, and application to a pharmacokinetic study. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci., 2011, 879(19), 1686-1696.
[http://dx.doi.org/10.1016/j.jchromb.2011.04.012] [PMID: 21536505]
Sun, Y.; Zhang, Z.; Xi, Z.; Shi, Z. Determination of naproxen in human urine by high-performance liquid chromatography with direct electrogenerated chemiluminescence detection. Talanta, 2009, 79(3), 676-680.
[http://dx.doi.org/10.1016/j.talanta.2009.04.048] [PMID: 19576429]
Sidelmann, U.G.; Bjørnsdottir, I.; Shockcor, J.P.; Hansen, S.H.; Lindon, J.C.; Nicholson, J.K. Directly coupled HPLC-NMR and HPLC-MS approaches for the rapid characterisation of drug metabolites in urine: application to the human metabolism of naproxen. J. Pharm. Biomed. Anal., 2001, 24(4), 569-579.
[http://dx.doi.org/10.1016/S0731-7085(00)00482-9] [PMID: 11272313]
Baeyens, W.R.G.; van der Weken, G.; Schelkens, M. Diclofenac and naproxen analysis by microbore liquid chromatography (LC) with native fluorescence detection. J. Fluoresc., 1995, 5(2), 131-134.
[http://dx.doi.org/10.1007/BF00727529] [PMID: 24226654]
Haria, M.; Fitton, A.; Peters, D.H. Loratadine. A reappraisal of its pharmacological properties and therapeutic use in allergic disorders. Drugs, 1994, 48(4), 617-637.
[http://dx.doi.org/10.2165/00003495-199448040-00009] [PMID: 7528133]
Kay, G.G.; Harris, A.G. Loratadine: a non-sedating antihistamine. Review of its effects on cognition, psychomotor performance, mood and sedation. Clin. Exp. Allergy, 1999, 29(Suppl. 3), 147-150.
[http://dx.doi.org/10.1046/j.1365-2222.1999.0290s3147.x] [PMID: 10444229]
Dhavale, N.; Gandhi, S.; Sabnis, S.; Bothara, K. Simultaneous HPTLC Determination of Escitalopram Oxalate and Clonazepam in Combined Tablets. Chromatographia, 2008, 67(5-6), 487-490.
Gandhi, S.V.; Dhavale, N.D.; Jadhav, V.Y.; Sabnis, S.S. Spectrophotometric and reversed-phase high-performance liquid chromatographic methods for simultaneous determination of escitalopram oxalate and clonazepam in combined tablet dosage form. J. AOAC Int., 2008, 91(1), 33-38.
[PMID: 18376583]
Taha, E.A.; Salama, N.N.; Wang, S. Micelle enhanced fluorimetric and thin layer chromatography densitometric methods for the determination of (+/-) citalopram and its S-enantiomer escitalopram. Anal. Chem. Insights, 2009, 4, 1-9.
[http://dx.doi.org/10.4137/ACI.S2274] [PMID: 19652757]
Lala, L.G.; D’Mello, P.M.; Naik, S.R. HPTLC determination of diclofenac sodium from serum. J. Pharm. Biomed. Anal., 2002, 29(3), 539-544.
[http://dx.doi.org/10.1016/S0731-7085(02)00131-0] [PMID: 12062654]
Mazurek, S.; Szostak, R. Quantitative determination of captopril and prednisolone in tablets by FT-Raman spectroscopy. J. Pharm. Biomed. Anal., 2006, 40(5), 1225-1230.
[http://dx.doi.org/10.1016/j.jpba.2005.03.047] [PMID: 16253463]
Yang, X.; Wang, F.; Hu, S. Enhanced oxidation of diclofenac sodium at a nano-structured electrochemical sensing film constructed by multi-wall carbon nanotubes–surfactant composite. Mater. Sci. Eng. C, 2008, 28(1), 188-194.
Ramos Payán, M.; Bello López, M.A.; Fernández-Torres, R.; Pérez Bernal, J.L.; Callejón Mochón, M. HPLC determination of ibuprofen, diclofenac and salicylic acid using hollow fiber-based liquid phase microextraction (HF-LPME). Anal. Chim. Acta, 2009, 653(2), 184-190.
[http://dx.doi.org/10.1016/j.aca.2009.09.018] [PMID: 19808112]
Benoudjit, N.E. Cools, Marc Meurens, and Michel Verleysen. Chemometric calibration of infrared spectrometers: selection and validation of variables by non-linear models. Chemom. Intell. Lab. Syst., 2004, 70(1), 47-53.
Altiokka, G.; Kircali, K. Simple method of paroxetine determination using a single channel FIA with no in-line reaction process. Anal. Sci., 2003, 19(4), 629-631.
[http://dx.doi.org/10.2116/analsci.19.629] [PMID: 12725406]
Raggi, M.A.; Bugamelli, F.; Casamenti, G.; Mandrioli, R.; De Ronchi, D.; Volterra, V. Analytical methods for the quality control of Prozac capsules. J. Pharm. Biomed. Anal., 1998, 18(4-5), 699-706.
[http://dx.doi.org/10.1016/S0731-7085(98)00215-5] [PMID: 9919971]
Abbasi-Tarighat, M. Spectrophotometric simultaneous determination of metal ions in cows’ milk and vegetables with the aid of artificial neural networks using synthetic 2-benzylspiro[isoindoline-1,5′-oxazolidine]-2′,3,4′-trione. J. Sci. Food Agric., 2014, 94(8), 1513-1520.
[http://dx.doi.org/10.1002/jsfa.6447] [PMID: 24130057]
Wu, D.; Olson, D.L. Introduction to special section on Risk and Technology. Technol. Forecast. Soc. Change, 2010, 77(6), 837-839.
Asadabadi, E.B.; Abdolmaleki, P.; Barkooie, S.M.H.; Jahandideh, S.; Rezaei, M.A. A combinatorial feature selection approach to describe the QSAR of dual site inhibitors of acetylcholinesterase. Comput. Biol. Med., 2009, 39(12), 1089-1095.
[http://dx.doi.org/10.1016/j.compbiomed.2009.09.003] [PMID: 19854437]
Heshmati, E.; Abdolmaleki, P.; Mozdarani, H.; Sarvestani, A.S. Effects of halogen substitution on Watson-Crick base pairing: a possible mechanism for radiosensitivity. Bioorg. Med. Chem. Lett., 2009, 19(17), 5256-5260.
[http://dx.doi.org/10.1016/j.bmcl.2009.06.105] [PMID: 19643605]
Dinç, E.; Baleanu, D. Application of Haar and Mexican hat wavelets to double divisor-ratio spectra for the multicomponent determination of ascorbic acid, acetylsalicylic acid and paracetamol in effervescent tablets. J. Braz. Chem. Soc., 2008, 19(3), 434-444.
Dinç, E.; Baleanu, D. Ratio spectra-continuous wavelet transform and ratio spectra-derivative spectrophotometry for the quantitative analysis of effervescent tablets of vitamin C and aspirin. REV. CHIM, 2008, 59(5), 499-504.
Dinç, E.; Kanbur, M.; Baleanu, D. Comparative spectral analysis of veterinary powder product by continuous wavelet and derivative transforms. Spectrochim. Acta A Mol. Biomol. Spectrosc., 2007, 68(2), 225-230.
[http://dx.doi.org/10.1016/j.saa.2006.11.018] [PMID: 17320471]
Dinç, E.; Ragno, G.; Ioele, G.; Baleanu, D. Fractional wavelet analysis for the simultaneous quantitative analysis of lacidipine and its photodegradation product by continuous wavelet transform and multilinear regression calibration. J. AOAC Int., 2006, 89(6), 1538-1546.
[http://dx.doi.org/10.1093/jaoac/89.6.1538] [PMID: 17225599]
Dinç, E. The spectrophotometric multicomponent analysis of a ternary mixture of ascorbic acid, acetylsalicylic acid and paracetamol by the double divisor-ratio spectra derivative and ratio spectrazero crossing methods. Talanta, 1999, 48(5), 1145-57.
Ukil, A.; Bernasconi, J. Neural network-Based active learning in multivariate calibration. IEEE Trans. Syst. Man Cybern. C, 2012, 42(6), 1763-1771.
Rezaei, B.; Ensafi, AA; Shandizi, F. Simultaneous determination of cobalt and nickel by spectrophotometric method and artificial neural network. Microchem. J., 2001, 70(1), 35-40.
Cirovic, D.A. Feed-forward artificial neural networks: applications to spectroscopy. Trends Analyt. Chem., 1997, 16(3), 148-155.
Mutihac, L.; Mutihac, R. Mining in chemometrics. Anal. Chim. Acta, 2008, 612(1), 1-18.
[http://dx.doi.org/10.1016/j.aca.2008.02.025] [PMID: 18331852]
Ruiz-Aguilar, J.J.; Moscoso-López, J.A.; Turias, I.; González-Enrique, J. 2018.
Rojas, R. Book: Neural networks: a systematic introduction; , 2013.
Burden, F.; Winkler, D. Bayesian regularization of neural networks. Methods Mol. Biol., 2008, 458, 25-44.
[http://dx.doi.org/10.1007/978-1-60327-101-1_3] [PMID: 19065804]
Ticknor, J.L. A Bayesian regularized artificial neural network for stock market forecasting. Expert Syst. Appl., 2013, 40(14), 5501-5506.
Dan Foresee, F.; Hagan, M.T. Gauss-Newton approximation to Bayesian learning. International Conference on Neural Networks, 1997, pp. 1930-1935.
Kayri, M. Predictive abilities of bayesian regularization and Levenberg–Marquardt algorithms in artificial neural networks: a comparative empirical study on social data. Mathematical and Computational Applications, 2016, 21(20), 1-11.
Shao, J.; Xu, D.; Wang, L.; Wang, Y. Bayesian neural networks for prediction of protein secondary structure. International Conference on Advanced Data Mining and Applications, 2005, pp. 544-551.
Ganorkar, S.B.; Rathi, A.A.; Kondalkar, A.R.; Joshi, Y.N. Spectrophotometric Determination of Loratadine in Bulk and Pharmaceutical Formulations. Asian J. Chem., 2011, 23(8), 3350-3352.
Gunji, R.; Nadendla, R.R.; Ponnuru, V.S. Simultaneous UVspectrophotometric determination and validation of diclofenac sodium and rabeprazole sodium using hydrotropic agents in its tablet dosage form. International Journal of Drug Development and Research, 2012, 4(1), 316-324.
Hashim Zuberi, M.; Haroon, U.Y. BiBi, T. Mehmood, I. Mehmood, Optimization of Quantitative Analysis of Naproxin Sodium Using UV Spectrophotometery in Different Solvent Mediums. Am. J. Anal. Chem., 2014, 5, 211-214.
Kohavi, R. A study of cross-validation and bootstrap for accuracy estimation and model selection. Proceedings of the 14th International Joint Conference on Artificial Intelligence, Montreal1995, Vol. 2, pp. 1137-1145.
Rodríguez-Pérez, R.; Fernández, L.; Marco, S. Overoptimism in cross-validation when using partial least squares-discriminant analysis for omics data: a systematic study. Anal. Bioanal. Chem., 2018, 410(23), 5981-5992.
[http://dx.doi.org/10.1007/s00216-018-1217-1] [PMID: 29959482]
Saini, L.M. Peak load forecasting using Bayesian regularization, Resilient and adaptive backpropagation learning based artificial neural networks. Electr. Power Syst. Res., 2008, 78(7), 1302-1310.
Gursoy, M.I. EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Syst. Appl., 2010, 37(12), 8659-8666.
Zupan, J.; Gasteiger, J. Neural networks for chemists: an introduction, 1993.
Zupan, J.; Gasteiger, J. Neural networks: A new method for solving chemical problems or just a passing phase. Anal. Chim. Acta, 1991, 248(1), 1-30.

Rights & PermissionsPrintExport Cite as

Article Details

Year: 2020
Published on: 27 September, 2020
Page: [1083 - 1092]
Pages: 10
DOI: 10.2174/1573412915666190618123154
Price: $65

Article Metrics

PDF: 19