Optimization and Empirical Modeling of HG-ICP-AES Analytical Technique through Artificial Neural Networks
An artificial neural network technique has been applied to the optimization of a hydride generation-inductively coupled plasma-atomic emission spectrometry (HG-ICP-AES) coupling for the determination of Ge at trace levels. The back propagation of errors net architecture was used. Experimental parame...
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2001
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| LEADER | 07885caa a22007097a 4500 | ||
|---|---|---|---|
| 001 | PAPER-1881 | ||
| 003 | AR-BaUEN | ||
| 005 | 20230518203113.0 | ||
| 008 | 190411s2001 xx ||||fo|||| 00| 0 eng|d | ||
| 024 | 7 | |2 scopus |a 2-s2.0-0035353666 | |
| 040 | |a Scopus |b spa |c AR-BaUEN |d AR-BaUEN | ||
| 030 | |a JCISD | ||
| 100 | 1 | |a Magallanes, J.F. | |
| 245 | 1 | 0 | |a Optimization and Empirical Modeling of HG-ICP-AES Analytical Technique through Artificial Neural Networks |
| 260 | |c 2001 | ||
| 270 | 1 | 0 | |m Magallanes, J.F.; Unidad de Actividad Química, Centro Atómico Constituyentes, Comn. Nac. de Ener. Atómica, Av. del Libertador 8250, 1429 Buenos Aires, Argentina; email: magallan@cnea.gov.ar |
| 506 | |2 openaire |e Política editorial | ||
| 504 | |a Smichowski, P., Marrero, J., Comparative Study to Evaluate the Effect of Different Acids on the Determination of Germanium by Hydride Generation-Inductively Coupled Plasma - Atomic Emission Spectrometry (1998) Anal. Chim. Acta, 376, pp. 283-291 | ||
| 504 | |a Nakahara, T., The Determination of Trace Amounts of Tin by Inductively Coupled Ar Plasma-Atomic Emission Spectrometry with Volatile Hydride Method (1983) Appl. Spectrosc., 37, pp. 539-545 | ||
| 504 | |a Farías, S., Smichowski, P., Determination of Germanium at Trace Levels in Environmental Matrices by Chloride Generation-Inductively Coupled Plasma-Atomic Emission Spectrometry (1999) J. Anal. At. Spectrom., 14, pp. 809-814 | ||
| 504 | |a Madrid, Y., Meseguer, J., Bonilla, M., Cámara, C., Lead Hydride Generation in a Lactic Acid-Potassium Dichromate Medium and its Application to the Determination of Lead in Fish, Vegetables and Drink Samples (1990) Anal. Chim. Acta, 237, pp. 181-187 | ||
| 504 | |a Massart, D.L., Vandegiste, B.G.M., Buydens, L.M.C., De Jong, S., Lewi, P.J., Smeyers-Verbeke, J., (1997) Handbook of Chemometrics and Qualimetrics, (PART A), p. 651. , Vandegiste, B. G. M., Rutan, S. C., Eds.; Elsevier: The Netherlands, Chapter 21 | ||
| 504 | |a Parker L.R., Jr., Tioh, N.H., Barnes, R.M., Optimization Approaches to the Determination of Arsenic and Selenium by Hydride Generation and ICP-AES (1985) Appl. Spectrosc., 39, pp. 45-48 | ||
| 504 | |a Le, X.C., Cullen, W.R., Reimer, K.J., Brindle, I.D., A New Continuous Hydride Generation for the Determination of Arsenic, Antimony and Tin by Hydride Generation Atomic Absorption Spectrometry (1992) Anal. Chim. Acta, 258, pp. 307-315 | ||
| 504 | |a Hilligsoe, B., Hansen, E.H., Application of Factorial Design and Simplex Optimization in the Development of Flow-Injection-Hydride Generation-Graphite Furnace Atomic Absorption Spectrometry (F1-HG-GFAAS) Procedure as Demonstrated for the Determination of Trace Levels of Germanium (1997) Fresenius J. Anal. Chem., 358, pp. 775-781 | ||
| 504 | |a Zupan, J., Gasteiger, J., (1993) Neural Networks for Chemists, , VCH Publishers: New York and Germany | ||
| 504 | |a Chauvin, Y., Rumelhart, D.E., (1995) Back-propagation: Theory, Architectures, and Applications, , Lawrence Erlbaum Associates, Publishers: Hillsdale, NJ | ||
| 504 | |a Fu, L.-M., (1994) Neural Networks in Computer Intelligence, , McGraw Hill: Singapore | ||
| 504 | |a Zupan, J., Movi, M., Ruisánchez, I., Kohonen and Counterpropagation Artificial Neural Networks in Analytical Chemistry (1997) Chemometrics and Intelligent Laboratory Systems, 38, pp. 1-23 | ||
| 504 | |a Cirovic, D.A., Feed-forward Artificial Neural Networks: Applications to Spectroscopy (1997) Trends Anal. Chem., 16, pp. 148-155 | ||
| 504 | |a (1997) Anal. Chim. Acta, 348, pp. 1-568. , Papers Presented at the International Conference on Chemometrics in Analytical Chemistry; Tarragona, Spain, Special | ||
| 504 | |a Magallanes, J.F., Vázquez, C., Automatic Classification of Steels by Processing Energy-Dispersive X-ray Spectra with Artificial Neural Networks (1998) J. Chem. Inf. Comput. Sci., 38, pp. 605-609 | ||
| 504 | |a Ruisánchez, I., Potokar, P., Zupan, I., Classification of Energy Dispersion X-ray Spectra of Mineralogical Samples by Artificial Neural Networks (1996) J. Chem. Inf. Comput Sci., 36, pp. 214-220 | ||
| 504 | |a Sun, L.-X., Danzer, K., Thiel, G., Classification of Wine Samples by Means of Artificial Neural Networks and Discrimination Analytical Methods (1997) Fresenius J. Anal. Chem., 359, pp. 143-149 | ||
| 504 | |a Lastres, E., Armas, G., Catasús, M., Alpízar, J., García, L., Cerdá, V., Use of Neural Networks in Solving Interferences Caused by Formation of Intermetallic Compounds in Anodic Stripping Voltametry (1997) Electroanalysis, 9, pp. 251-254 | ||
| 504 | |a Caldera, A., Alpízar, J., Estela, J.M., Cerdá, V., Catasús, M., Lastres, E., García, L., Resolution of Highly Overapping Differential Pulse Anodic Stripping Volatametric Signals Using Multicomponent Analysis and Neural Networks (1997) Anal. Chim. Acta, 350, pp. 163-169 | ||
| 504 | |a Jimenez, O., Benito, I., Marina, L.M., Neural Networks as a Tool for Modelling the Retention Behaviour of Dihydropyridines in Micellar Liquid Chromatography (1997) Anal. Chim. Acta, 353, pp. 367-379 | ||
| 504 | |a Goodacre, R., Timmins, É.M., Jones, A., Kell, D.B., Maddock, J., Heginbothom, M.L., Magee, J.T., On Mass Spectrometer Instrument Standarization and Interlaboratory Calibration Transfer Using Neural Networks (1997) Anal. Chim. Acta, 348, pp. 511-532 | ||
| 504 | |a Despagne, F., Massart, L., Neural Networks in Multivariate Calibration (1998) Analyst, 123, pp. 157R-178R | ||
| 504 | |a Andreae, M.O., Froelich P.N., Jr., Determination of Germanium in Natural Waters by Graphite Furnace Absorption Atomic Spectrometry with Hydride Generation (1981) Anal. Chem., 53, pp. 287-291 | ||
| 520 | 3 | |a An artificial neural network technique has been applied to the optimization of a hydride generation-inductively coupled plasma-atomic emission spectrometry (HG-ICP-AES) coupling for the determination of Ge at trace levels. The back propagation of errors net architecture was used. Experimental parameters and their relationship have been studied, obtaining a surface response of the system. The results and optimization aspects achieved with the neural network approach have been compared to the "one variable at time" and SIMPLEX methods. |l eng | |
| 593 | |a Unidad de Actividad Química, Centro Atómico Constituyentes, Comisión Nacional de Energía Atómica, Av. del Libertador 8250, 1429 Buenos Aires, Argentina | ||
| 593 | |a Departamento de Química Inorgánica, Analítica y Quimicafísica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria de Nuñez, Pabelĺn 2, 1428 Buenos Aires, Argentina | ||
| 593 | |a Unidad de Actividad Geología, Centro Atómico Ezeiza, Comisión Nacional de Energía Atómica, Av. del Libertador 8250, 1429 Buenos Aires, Argentina | ||
| 690 | 1 | 0 | |a ARTICLE |
| 690 | 1 | 0 | |a BACKPROPAGATION |
| 690 | 1 | 0 | |a COMPUTER SOFTWARE |
| 690 | 1 | 0 | |a NEURAL NETWORKS |
| 690 | 1 | 0 | |a PLASMAS |
| 690 | 1 | 0 | |a SPECTROMETRY |
| 690 | 1 | 0 | |a FLOW RATE |
| 690 | 1 | 0 | |a CHEMICAL ANALYSIS |
| 700 | 1 | |a Smichowski, P. | |
| 700 | 1 | |a Marrero, J. | |
| 773 | 0 | |d 2001 |g v. 41 |h pp. 824-829 |k n. 3 |p J. Chem. Inf. Comput. Sci. |x 00952338 |t Journal of Chemical Information and Computer Sciences | |
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| 856 | 4 | 0 | |u https://doi.org/10.1021/ci000337k |y DOI |
| 856 | 4 | 0 | |u https://hdl.handle.net/20.500.12110/paper_00952338_v41_n3_p824_Magallanes |y Handle |
| 856 | 4 | 0 | |u https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00952338_v41_n3_p824_Magallanes |y Registro en la Biblioteca Digital |
| 961 | |a paper_00952338_v41_n3_p824_Magallanes |b paper |c PE | ||
| 962 | |a info:eu-repo/semantics/article |a info:ar-repo/semantics/artículo |b info:eu-repo/semantics/publishedVersion | ||
| 999 | |c 62834 | ||