Development a Spectrophotometric of Fe(Ⅲ), Al(Ⅲ) and Cu(Ⅱ) Using Eriochrome Cyanine R Ligand and Assessment of the Obtained Data by Partial Least-Squares and Artificial Neural Network Method—Application to Natural Waters
A. Hakan AKTAŞ
Department of Chemistry, Faculty of Science & Art, Süleyman Demirel University, Isparta 32260, Turkey
Development a Spectrophotometric of Fe(Ⅲ), Al(Ⅲ) and Cu(Ⅱ) Using Eriochrome Cyanine R Ligand and Assessment of the Obtained Data by Partial Least-Squares and Artificial Neural Network Method—Application to Natural Waters
A. Hakan AKTAŞ
Department of Chemistry, Faculty of Science & Art, Süleyman Demirel University, Isparta 32260, Turkey
摘要: Simultaneous determination of heavy metal cations and accurate quantitative prediction of them are of great interest in analytical chemistry. This work has focused on a comprehensive comparison of partial least squares (PLS-1) and artificial neural networks (ANN) as two types of chemometric methods. For this purpose, aluminum, iron and copper were studied as three analytes whose UV-Vis absorption spectra highly overlap each other. Accordance with determined parameters (ligand concentration, pH, waiting times, the relationship between absorbance and concentration of metal ion effect and foreign ions) are provided and the optimum conditions. After establishing the optimum conditions for Fe3+, Al3+ and Cu2+ containing mixtures spectrophotometric determinations and the data calibration method of least squares (PLS-1) regression, and artificial neural network (ANN) methods were used. Chemometric methods are applied in a fast, simple, and the results are applicable.
关键词:UV-Vis spectrophotometry;Partial least squares;Artificial neural network;Aluminum;Iron;Copper
Abstract:Simultaneous determination of heavy metal cations and accurate quantitative prediction of them are of great interest in analytical chemistry. This work has focused on a comprehensive comparison of partial least squares (PLS-1) and artificial neural networks (ANN) as two types of chemometric methods. For this purpose, aluminum, iron and copper were studied as three analytes whose UV-Vis absorption spectra highly overlap each other. Accordance with determined parameters (ligand concentration, pH, waiting times, the relationship between absorbance and concentration of metal ion effect and foreign ions) are provided and the optimum conditions. After establishing the optimum conditions for Fe3+, Al3+ and Cu2+ containing mixtures spectrophotometric determinations and the data calibration method of least squares (PLS-1) regression, and artificial neural network (ANN) methods were used. Chemometric methods are applied in a fast, simple, and the results are applicable.
Key words:UV-Vis spectrophotometry;Partial least squares;Artificial neural network;Aluminum;Iron;Copper
A. Hakan AKTAŞ. Development a Spectrophotometric of Fe(Ⅲ), Al(Ⅲ) and Cu(Ⅱ) Using Eriochrome Cyanine R Ligand and Assessment of the Obtained Data by Partial Least-Squares and Artificial Neural Network Method—Application to Natural Waters[J]. 光谱学与光谱分析, 2018, 38(08): 2638-2644.
A. Hakan AKTAŞ. Development a Spectrophotometric of Fe(Ⅲ), Al(Ⅲ) and Cu(Ⅱ) Using Eriochrome Cyanine R Ligand and Assessment of the Obtained Data by Partial Least-Squares and Artificial Neural Network Method—Application to Natural Waters. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(08): 2638-2644.
[1] Yongnian N, Chunfang H, Kokot S. Anal. Chim. Acta, 2007, 599: 209.
[2] Ekinci D, Beydemir Ş, Küfreviolu Oİ. Journal of Enzyme Inhibition and Medicinal Chemistry, 2008,22(6): 745.
[3] Çoban A T, Şentürk M, Çiftçi M, et al. Protein & Peptide Letters, 2007, 14(10): 1027.
[4] Tekman B, Özdemir H, Şentürk,et al. Comparative Biochemistry and Physiology C- Toxicology & Pharmacology, 2008, 148(2): 117.
[5] Ceyhun S B, Şentürk M, Yerlikaya E, et al. Environmental Toxicology and Physiology C-Toxicology & Pharmacology, 2008, 148(2): 117.
[6] Demirda R, Yerlikaya E, Şentürk M, et al. Enzyme Inhibition and Medicinal Chemistry, 2013, 28(2): 278.
[7] Scancar J, Benedik R M M, Kr?zaj I. Talanta, 2003, 59: 355.
[8] Tahan J E, Granadilla V A, Romero R A. Anal. Chim. Acta, 2004, 295: 187.
[9] Nagaoku M H, Maitani T. Analyst, 2000, 125: 1962.
[10] Coscaine A,R, Andrade J,C, Poppi R J. Analyst, 2002, 127: 135.
[11] Kendüzler E, Türker A R. Anal. Chim. Acta, 2003, 480: 259.
[12] Ryan E, Meaney M. Analyst, 1992, 117: 1435.
[13] Coscine A R, Andrade J C, Poppi R J, et al. Anal. Chim. Acta, 2000, 423: 31.
[14] Wang N, Liang W. Talanta, 1993, 40(6): 897.
[15] Limson J, Nyokong T, Daya S. Journal of Pineal Research, 1998, 24(1): 15.
[16] Safavi A, Abdollahi H, Mirzajani R. Spectrochimica Acta Part A, 2006, 63: 196.
[17] Peng J, Liu S, Deng C. Anal. Sci., 2005, 21(3): 259.
[18] Reddy V K, Reddy S M, et al. Journal of Analytical Chemistry, 2000, 55(5): 435.
[19] Honorato R S, Carneiro J M T,Zagatto E A G. Anal. Chim. Acta, 2001, 441: 309.
[20] Rodrigues J L, Magalhaes S C, Luccas P O. J. of Phar. and Bio. Ana., 2005, 36: 1119.
[21] Royset O. Anal. Chim. Acta, 1985, 178: 223.
[22] Marczenko Z, Kaowska H. Anal. Chim. Acta, 1981, 123: 279.
[23] Gao H,W, Chen L, Yang Q Z. Journal of AOAC International, 2005, 88: 1231.
[24] Thomas E V, Haaland D M. Anal. Chem., 1990, 62: 1091.
[25] Aktaş A H, Kitiş F. Croatica Chim. Acta, 2014, 87(1): 69.
[26] Lorber A. Anal. Chem., 1986, 58: 1167.
[27] Booksh K S, Kowalski B R. Anal. Chem., 1994, 66: 782A.
[28] Karim A Z, Sara M K. Journal of Chinese Chemical Society, 2015, 62: 772.
[29] Goicoechea H C, Olivieri A C. Anal. Chem., 1999, 71: 4361.
[30] Boque’ R, Rius F X. Chemom. Intell. Lab. Syst., 1996, 32: 11.
[31] Turkish Standards. Water-Drinking and Potable Water TS, Ankara, 1997, 266: 32.
[32] WHO. World Health Organization, 2nd ed., Genova, 1993, 1:188.