- Email: [email protected]
Simultaneous determination of cobalt and nickel by spectrophotometric method and artificial neural network
Microchemical Journal 70 Ž2001. 35᎐40
Simultaneous determination of cobalt and nickel by spectrophotometric method and artificial neural network B. RezaeiU , A.A. Ensafi, F. Shandizi Collage of Chemistry, Isfahan Uni¨ ersity of Technology, Isfahan 84156, Iran Received 9 January 2001; received in revised form 31 May 2001; accepted 1 June 2001
Abstract A method for the simultaneous spectrophotometric determination of the cobaltŽII. and nickelŽII. based on formation of their complexes with pyrolidine and carbon disulfide is proposed. The complexes was extracted with p-xylene. An artificial neural network ŽANN. model was used to analyze the mixture spectra. The limit of detection for CoŽII. and NiŽII. were 0.005 and 0.006 grml, respectively. This procedure allows the simultaneous determination of the cited ions in alloy and synthetic samples. Good reliability of the determination was proved. 䊚 2001 Elsevier Science B.V. All rights reserved. Keywords: Cobalt; Nickel; Simultaneously; Spectrophotometry; ANN
1. Introduction Cobalt and nickel are metals that appear together in many real samples. Several techniques such as atomic absorption w1x, atomic fluorescence w2x, X-ray fluorescence w3x, voltammetric w4,5x and
U
Corresponding author. Tel.: q98-31-8912351; fax: q9831-8912350. E-mail address: [email protected] ŽB. Rezaei..
spectrophotometric methods w6,7x have been used for the determination of these ions in different samples. Among the most widely used analytical methods are those based on the UV᎐visible spectrophotometric techniques due to the resulting experimental rapidly, simplicity and the wide application. However, the simultaneous determination of these ions by the use of the traditional spectrophotometry is difficult because, generally, the absorption spectra overlap and the superimposed curves are not suitable for quantitative
0026-265Xr01r$ - see front matter 䊚 2001 Elsevier Science B.V. All rights reserved. PII: S 0 0 2 6 - 2 6 5 X Ž 0 1 . 0 0 0 9 4 - 7
B. Rezaei et al. r Microchemical Journal 70 (2001) 35᎐40
36
evaluation. Quantitative spectrophotometry has been greatly improved by the use of multivariate statistical methods, particularly principal component regression, partial least square regression and artificial neural networks ŽANN.. In recent years, ANN has shown to be very successful in modeling complex non-linear system and providing precise data extraction from single measurement w8x. The theory and application of ANN is spectrophotometry have been discussed by several workers w9,10x. This paper reports the extraction simultaneous spectrophotometric determination of CoŽII. and NiŽII. with p-xylene carbon disulfide ŽCS 2 . pyrolidine, which was expected to produce the wellknown chelating reagent, ammonium pyrolidine᎐dithiocarbomate ŽAPDC. w7x. A back propagation artificial neural network ŽBP᎐ANN. algorithm was used to handle such non-linear pectoral data. The results showed that the BP᎐ANN technique is quite satisfactory for treating the non-linearity embedded in the data. A back propagation network has input and output and at least one hidden layer. The network learns by calculating an error between desired and actual output and propagating this error information back to each node in the network. This back-propagation error is used to drive the learning at each node. The process of changing the weight of the connections to achieve some desired result is called learning or adaptation. The basic configuration used here consisted of a linear input layer, a hidden layer of neurons with tansigmoid transfer function wEq. Ž1.x and an output layer of two neurons with linear transfer function. f Ž x. s
e x y eyx e x q eyx
Ž1.
The tansigmoidal hidden layer is critical as it allows the network to learn non-linear relationships between inputs and outputs. ANN can model non-linear systems and therefore, ANN can be used in analytical chemistry for modeling nonlinear for calibration curve w11x.
2. Experimental 2.1. Reagent All chemicals were of analytical-reagent grade and doubly distilled water was used throughout. Stock standard solution Ž1000 grml. of CoŽII. and NiŽII. prepared by dissolving 1.0235 g and 1.239 g CoŽNO 3 . 2 .6H 2 O and NiŽNO 3 ..6H 2 O ŽMerck., respectively, in 250-ml volumetric flasks and diluted to the mark with distilled water. Pyrrolidinedithiocarbamic acid ŽPDC. forming p-xylene solution having a composition of 1.0:1.0:98 Žvrv%. for CS 2 rC 4 H 8 NHrpC 6 H 4ŽCH 3 . 2 was used. p-Xylene used only as a diluent, since CS 2 and C 4 H 8 NH would solidify upon mixing w7x. Then the solidified substance was dissolved in p-xylene in the presence of 0.017 M citric acid. 2.2. Apparatus A Perkin᎐Elmer Model 551 spectrophotometer equipped with glass or quartz cell with a 1-cm path length was used for recording absorbance spectra. The ANN algorithm was written in MATLAB ŽMath Work, version 5.3.0. and the program was run using a Pentium II, 233 IBM personal computer having 64 MB for RAM ŽWindow’s 98 operating system.. 2.3. Sample preparation For the alloys a known amount of accurately weighed sample Ž0.010 g. was dissolved in approximately 30 ml of aqua regia, the resulting solution was then evaporated to dryness and 1 ml of 6.0 M HCl added, it was then diluted with water in a 250-ml volumetric flask. The resulting solution was used for analysis. 2.4. Recommended procedure Standard or sample solutions were prepared in a 50-ml separation funnel by taking a required volume of the solution to be analyzed to obtain CoŽII. and NiŽII. concentrations over their re-
B. Rezaei et al. r Microchemical Journal 70 (2001) 35᎐40
spective determination ranges 0.005᎐6.00 grml for CoŽII. and 0.006᎐6.00 grml for NiŽII.. Then 1.0-ml buffer solution Žphosphate boric acid and KNO 3 0.1 M. and 4.0 ml 0.202 M, APDC in p-xylene were added. The mixture was mixed for 10 s and then equilibrated for 6 min. Then the organic phase was separated and the absorbance spectra were recorded from 325 to 370 nm. The network architecture adapted for this study was the back propagation network having single hidden neuron layers, the input layer consists of 42 neurons corresponding to the number of wavelength points selected for each spectrum. The output layer involves two neurons, responding to CoŽII. and NiŽII. concentration. A back propagation artificial neural network with one hidden layer containing 5᎐140 neurons have been considered for the model. The typical training time for 10 000 epochs took at least 25 min. In one epoch, the algorithm cycles went through the following task: presentation of 42 = 41 training data sets; and calculation of the rms.
3. Results and discussion CobaltŽII. and nickelŽII. react with PDC to form a complex in the organic phase. Fig. 1 shows the spectra of these complexes in the organic phase. The absorption maximum for Co᎐PDC and Ni᎐PDC is 332 and 340 nm. The characteristics of these complexes were described by Fujinaga et al. w7x. As shown in Fig. 1, the absorption curve of Co-PDC has been overlapped with Ni᎐PDC. ANN was used to resolve the spectrum of each complex in the mixture. For the first time, optimum condition for the complex formation was investigated.
37
Fig. 1. Absorption spectra for the Ža.: NiŽII. ᎐PDC complex, Žb.: CoŽII. ᎐PDC complex, and Žc.: mixture of NiŽII. and CoŽII., conditions: NiŽII., 0.200 grml; CoŽII., 0.200 grml; pH, 8.0; PDC, 0.10 M.
come leveled off. Therefore, pH 8.0 was selected for further study. The effect of mixing ratio pyrolidine and carbon disulfide to form PDC in p-xylene as a solvent has been studied w7x. PDC quantitatively formed by reaction of pyrolidine with excess of
3.1. Influence of ¨ ariable The effect of variation parameters such as pH, PDC concentration and ionic strength were studied. Fig. 2 shows the influence of pH in the range of 2᎐10 on the complex formation in the organic phase. The results show that by increasing the pH value from 2 to 8 the absorbance for Co᎐PDC and Ni᎐PDC increases and after that these be-
Fig. 2. Effect of pH on the sensitivity, conditions: Ža.: NiŽII. ᎐PDC complex and Žb.: CoŽII. ᎐PDC complex, conditions: NiŽII., 0.200 grml; CoŽII., 0.200 grml; PDC, 0.10 M.
38
B. Rezaei et al. r Microchemical Journal 70 (2001) 35᎐40
CS 2 in the organic phase. The influence in the amount of PDC in the organic phase on the absorbance intensity was studied at pH 8.0, with CoŽII., 0.100 grml and NiŽII., 0.100 grml. Fig. 3 shows that by increasing PDC concentration up to 0.174 M, the sensitivity increased, whereas, greater amounts of the reagent decreased the sensitivity. Therefore, 0.174 M PDC was selected for the study. The influence of ionic strength Ž2.0 M KNO 3 . on the sensitivity, due to the increasing KNO 3 concentration up to 0.2 M, the sensitivity increased and after this the absorbance is not effected. Thus, 0.2 M KNO 3 was selected to produce suitable ionic strength. A simplex optimization procedure method was also used to confirm the optimum conditions, which were obtained by the univariate procedure. Two major parameters ŽpH and PDC concentration. were optimized by the simplex procedure. After twelve experiment sets, the signal reached to maximum value and remained constant. The results are pHs 8.2 and PDC concentration of 0.178 M, which is close to the results of univariate method. 3.2. Selection of optimal number of factors ANN was utilized to process the absorbance response of Co᎐PDC and Ni᎐PDC complexes. The 42 spectral, response at a fixed bandwidth Žcontaining max of the two complexes 370᎐325 nm. were used as the input to the networks. Although, it desirable to utilize the whole spectrum of each measurement for the training of the ANN Žin order to obtain the best contrast and variation in spectral pattern uniqueness ., this process will entail large matrices for the weight connections and network thresholds. Hence, this will lead to a drastic increase in the network training time. Therefore, we selected 42 absorption points during 370᎐325 nm, to represent the actual input data for ANN. The 32 standard solutions were randomly chosen for training and 10 solutions were selected for the prediction set. The training was performed on several networks having varying number of neurons in the
Fig. 3. Effect of PDC concentration on the sensitivity. Conditions such as Fig. 2 with pH of 8.0.
hidden layer. During the training, the root means square error, which was taken for each training step. Fig. 4 shows the progress in the time, over the number of neurons in the hidden layer, required to get a fixed Ž2%. error. The results show that by increasing the number of neurons in the hidden layer the time of learning was decreased. However, the error in the training set decreases as far as 60 neurons, whereas after that, the error
Fig. 4. The relationship between numbers of neuron in hidden layers vs. time. Conditions such as Fig. 3 in the presence of PDC concentration of 0.174 M.
B. Rezaei et al. r Microchemical Journal 70 (2001) 35᎐40
39
Fig. 5. The relationship between numbers of hidden layer vs. time. Conditions such as Fig. 4.
Fig. 7. The relation between predicted and actual value for NiŽII..
increases. Therefore, 60 neurons over 10 000 epochs, as a hidden layer, were selected. The effected number of hidden layers Žwith 5 neurons in each layer. was studied at optimum reagent concentrations. Fig. 5 shows that by increasing the number of hidden layers, time of analysis increased intensity. Therefore, one hidden layer was selected for the study. At these optimized conditions, the expected vs. calculated values for ten predication sets were plotted as is
shown in Figs. 6 and 7. The results show the good regression between actual values and prediction values for CoŽII. and NiŽII. concentration. Under the optimized conditions, CoŽII. and NiŽII. can be determined in the range of 0.005 to 0.500 grml with the regression equation of Y s 0.2454q 0.0021C Ž r s 0.9940. and Y s 0.2795q 0.0025C Ž r s 0.9988., respectively, where C is the concentration of the metal ions as grml and Y is the absorbance. The limit of detection Žis equal to the average blank signal plus three times that of its standard deviation. was 0.004 grml for two ions. The influence of other potential interfering ions on the determination of 0.100 grml CoŽII. and NiŽII. was also shown in Table 1. Table 1 Influence of the other substances on the determination of 0.100 grml of NiŽII. and CoŽII.
Fig. 6. The relationship between the predicted and actual value for CoŽII..
Species
Tolerance ratio ŽWinorWNiŽII.,CoŽII. .
Sr2q, BrO3y Sb3q, Ca2q, Ba2q, Mg2q Csq, PO43y, C2 O42y Cr3, Cd2q, Pb2q, Hg2q Fe2q, Mn2q, Cu2q ClO3 y, Iy, Br2y
200 100 50 1
B. Rezaei et al. r Microchemical Journal 70 (2001) 35᎐40
40
Table 2 Determination of nickel and cobalt in alloys samples Nickel
Cobalt
Alloys samples
Found g ly1
Recovery %
In sample g ly1
Found g ly1
Recovery %
In sample g ly1
Hastelloy Da Inconcel 600a Nimonic 90b
5.892" 0.13 6.030" 0.12 0.580" 0.026
98.2 100.5 105.4
6.000 6.000 0.550
0.103" 0.006 ᎐ 0.180" 0.005
93.4 ᎐ 100.0
0.110 ᎐ 0.180
a b
Ref. w12x, p. 172. Ref. w12x, p. 211.
4. Application Finally, an application of the training network with 42 neurons in the input layer, 60 neurons in the hidden layer Žone hidden layer. was demonstrated by feeding it with new measurement spectra for three alloy samples Žtest samples. under optimum conditions, after dissolving it in the HCl᎐HNO3 media. The results as shown in Table 2. The recoveries of standard additions of Co and Ni to each sample were determined. The results show that the method is accurate and gives good recovery of added metal ions. The method has the same LOD with flame atomic absorption spectrometry, whereas, these elements can be analyzed simultaneously by using the proposed procedure.
Acknowledgements The authors acknowledge the Research Coun-
cil of Isfahan University of Technology for support of the project. References w1x w2x w3x w4x w5x w6x w7x w8x w9x w10x w11x w12x
Z. Shen, F. Nic, Y. Chen, Fensi Huaxane 19 Ž1991. 1272. V. Rigin, Anal. Chim. Acta 283 Ž1993. 895. O.W. Lan, S.Y. Ho, Anal. Chim. Acta 280 Ž1993. 269. Z. Shen, Z. Wang, Fensi Huaxane 21 Ž1993. 1313. A.A. Ensafi, H. Rahimi Mansour, K. Zarei, Fresenius J. Anal. Chem. 363 Ž1999. 646. D.T. Burns, M. Harriet, S.A. Barakat, Anal. Chim. Acta 259 Ž1992. 33. K. Fujinaga, M. Fukai, Y. Seike, M. Okumura, Anal. Sci. 15 Ž1999. 333. M.N. Tabi, R. Andrews, R. Naraganaswamy, Anal. Chim. Acta 330 Ž1996. 31. W.H. Chem, A.W.M. Lee, D.W.J. Kwong, Y.Z. Liang, K.M. Wang, Analyst 122 Ž1997. 657. J.M. Sutter, P.C. Jurs, Anal. Chem. 64 Ž1997. 856. E.B. Sandell, H. Onishi, Photometric Determination of Traces of Metals, Part I, John Wiley and Sons Inc, 1978, p. 476. W.H. Cubberly, P.M. Unterweiser, D. Benjamin, Metals Handbook American Society for Metals ŽASM., 9th Ed., Vol. 3, 1980.
Simultaneous determination of cobalt and nickel by spectrophotometric method and artificial neural network B. RezaeiU , A.A. Ensafi, F. Shandizi Collage of Chemistry, Isfahan Uni¨ ersity of Technology, Isfahan 84156, Iran Received 9 January 2001; received in revised form 31 May 2001; accepted 1 June 2001
Abstract A method for the simultaneous spectrophotometric determination of the cobaltŽII. and nickelŽII. based on formation of their complexes with pyrolidine and carbon disulfide is proposed. The complexes was extracted with p-xylene. An artificial neural network ŽANN. model was used to analyze the mixture spectra. The limit of detection for CoŽII. and NiŽII. were 0.005 and 0.006 grml, respectively. This procedure allows the simultaneous determination of the cited ions in alloy and synthetic samples. Good reliability of the determination was proved. 䊚 2001 Elsevier Science B.V. All rights reserved. Keywords: Cobalt; Nickel; Simultaneously; Spectrophotometry; ANN
1. Introduction Cobalt and nickel are metals that appear together in many real samples. Several techniques such as atomic absorption w1x, atomic fluorescence w2x, X-ray fluorescence w3x, voltammetric w4,5x and
U
Corresponding author. Tel.: q98-31-8912351; fax: q9831-8912350. E-mail address: [email protected] ŽB. Rezaei..
spectrophotometric methods w6,7x have been used for the determination of these ions in different samples. Among the most widely used analytical methods are those based on the UV᎐visible spectrophotometric techniques due to the resulting experimental rapidly, simplicity and the wide application. However, the simultaneous determination of these ions by the use of the traditional spectrophotometry is difficult because, generally, the absorption spectra overlap and the superimposed curves are not suitable for quantitative
0026-265Xr01r$ - see front matter 䊚 2001 Elsevier Science B.V. All rights reserved. PII: S 0 0 2 6 - 2 6 5 X Ž 0 1 . 0 0 0 9 4 - 7
B. Rezaei et al. r Microchemical Journal 70 (2001) 35᎐40
36
evaluation. Quantitative spectrophotometry has been greatly improved by the use of multivariate statistical methods, particularly principal component regression, partial least square regression and artificial neural networks ŽANN.. In recent years, ANN has shown to be very successful in modeling complex non-linear system and providing precise data extraction from single measurement w8x. The theory and application of ANN is spectrophotometry have been discussed by several workers w9,10x. This paper reports the extraction simultaneous spectrophotometric determination of CoŽII. and NiŽII. with p-xylene carbon disulfide ŽCS 2 . pyrolidine, which was expected to produce the wellknown chelating reagent, ammonium pyrolidine᎐dithiocarbomate ŽAPDC. w7x. A back propagation artificial neural network ŽBP᎐ANN. algorithm was used to handle such non-linear pectoral data. The results showed that the BP᎐ANN technique is quite satisfactory for treating the non-linearity embedded in the data. A back propagation network has input and output and at least one hidden layer. The network learns by calculating an error between desired and actual output and propagating this error information back to each node in the network. This back-propagation error is used to drive the learning at each node. The process of changing the weight of the connections to achieve some desired result is called learning or adaptation. The basic configuration used here consisted of a linear input layer, a hidden layer of neurons with tansigmoid transfer function wEq. Ž1.x and an output layer of two neurons with linear transfer function. f Ž x. s
e x y eyx e x q eyx
Ž1.
The tansigmoidal hidden layer is critical as it allows the network to learn non-linear relationships between inputs and outputs. ANN can model non-linear systems and therefore, ANN can be used in analytical chemistry for modeling nonlinear for calibration curve w11x.
2. Experimental 2.1. Reagent All chemicals were of analytical-reagent grade and doubly distilled water was used throughout. Stock standard solution Ž1000 grml. of CoŽII. and NiŽII. prepared by dissolving 1.0235 g and 1.239 g CoŽNO 3 . 2 .6H 2 O and NiŽNO 3 ..6H 2 O ŽMerck., respectively, in 250-ml volumetric flasks and diluted to the mark with distilled water. Pyrrolidinedithiocarbamic acid ŽPDC. forming p-xylene solution having a composition of 1.0:1.0:98 Žvrv%. for CS 2 rC 4 H 8 NHrpC 6 H 4ŽCH 3 . 2 was used. p-Xylene used only as a diluent, since CS 2 and C 4 H 8 NH would solidify upon mixing w7x. Then the solidified substance was dissolved in p-xylene in the presence of 0.017 M citric acid. 2.2. Apparatus A Perkin᎐Elmer Model 551 spectrophotometer equipped with glass or quartz cell with a 1-cm path length was used for recording absorbance spectra. The ANN algorithm was written in MATLAB ŽMath Work, version 5.3.0. and the program was run using a Pentium II, 233 IBM personal computer having 64 MB for RAM ŽWindow’s 98 operating system.. 2.3. Sample preparation For the alloys a known amount of accurately weighed sample Ž0.010 g. was dissolved in approximately 30 ml of aqua regia, the resulting solution was then evaporated to dryness and 1 ml of 6.0 M HCl added, it was then diluted with water in a 250-ml volumetric flask. The resulting solution was used for analysis. 2.4. Recommended procedure Standard or sample solutions were prepared in a 50-ml separation funnel by taking a required volume of the solution to be analyzed to obtain CoŽII. and NiŽII. concentrations over their re-
B. Rezaei et al. r Microchemical Journal 70 (2001) 35᎐40
spective determination ranges 0.005᎐6.00 grml for CoŽII. and 0.006᎐6.00 grml for NiŽII.. Then 1.0-ml buffer solution Žphosphate boric acid and KNO 3 0.1 M. and 4.0 ml 0.202 M, APDC in p-xylene were added. The mixture was mixed for 10 s and then equilibrated for 6 min. Then the organic phase was separated and the absorbance spectra were recorded from 325 to 370 nm. The network architecture adapted for this study was the back propagation network having single hidden neuron layers, the input layer consists of 42 neurons corresponding to the number of wavelength points selected for each spectrum. The output layer involves two neurons, responding to CoŽII. and NiŽII. concentration. A back propagation artificial neural network with one hidden layer containing 5᎐140 neurons have been considered for the model. The typical training time for 10 000 epochs took at least 25 min. In one epoch, the algorithm cycles went through the following task: presentation of 42 = 41 training data sets; and calculation of the rms.
3. Results and discussion CobaltŽII. and nickelŽII. react with PDC to form a complex in the organic phase. Fig. 1 shows the spectra of these complexes in the organic phase. The absorption maximum for Co᎐PDC and Ni᎐PDC is 332 and 340 nm. The characteristics of these complexes were described by Fujinaga et al. w7x. As shown in Fig. 1, the absorption curve of Co-PDC has been overlapped with Ni᎐PDC. ANN was used to resolve the spectrum of each complex in the mixture. For the first time, optimum condition for the complex formation was investigated.
37
Fig. 1. Absorption spectra for the Ža.: NiŽII. ᎐PDC complex, Žb.: CoŽII. ᎐PDC complex, and Žc.: mixture of NiŽII. and CoŽII., conditions: NiŽII., 0.200 grml; CoŽII., 0.200 grml; pH, 8.0; PDC, 0.10 M.
come leveled off. Therefore, pH 8.0 was selected for further study. The effect of mixing ratio pyrolidine and carbon disulfide to form PDC in p-xylene as a solvent has been studied w7x. PDC quantitatively formed by reaction of pyrolidine with excess of
3.1. Influence of ¨ ariable The effect of variation parameters such as pH, PDC concentration and ionic strength were studied. Fig. 2 shows the influence of pH in the range of 2᎐10 on the complex formation in the organic phase. The results show that by increasing the pH value from 2 to 8 the absorbance for Co᎐PDC and Ni᎐PDC increases and after that these be-
Fig. 2. Effect of pH on the sensitivity, conditions: Ža.: NiŽII. ᎐PDC complex and Žb.: CoŽII. ᎐PDC complex, conditions: NiŽII., 0.200 grml; CoŽII., 0.200 grml; PDC, 0.10 M.
38
B. Rezaei et al. r Microchemical Journal 70 (2001) 35᎐40
CS 2 in the organic phase. The influence in the amount of PDC in the organic phase on the absorbance intensity was studied at pH 8.0, with CoŽII., 0.100 grml and NiŽII., 0.100 grml. Fig. 3 shows that by increasing PDC concentration up to 0.174 M, the sensitivity increased, whereas, greater amounts of the reagent decreased the sensitivity. Therefore, 0.174 M PDC was selected for the study. The influence of ionic strength Ž2.0 M KNO 3 . on the sensitivity, due to the increasing KNO 3 concentration up to 0.2 M, the sensitivity increased and after this the absorbance is not effected. Thus, 0.2 M KNO 3 was selected to produce suitable ionic strength. A simplex optimization procedure method was also used to confirm the optimum conditions, which were obtained by the univariate procedure. Two major parameters ŽpH and PDC concentration. were optimized by the simplex procedure. After twelve experiment sets, the signal reached to maximum value and remained constant. The results are pHs 8.2 and PDC concentration of 0.178 M, which is close to the results of univariate method. 3.2. Selection of optimal number of factors ANN was utilized to process the absorbance response of Co᎐PDC and Ni᎐PDC complexes. The 42 spectral, response at a fixed bandwidth Žcontaining max of the two complexes 370᎐325 nm. were used as the input to the networks. Although, it desirable to utilize the whole spectrum of each measurement for the training of the ANN Žin order to obtain the best contrast and variation in spectral pattern uniqueness ., this process will entail large matrices for the weight connections and network thresholds. Hence, this will lead to a drastic increase in the network training time. Therefore, we selected 42 absorption points during 370᎐325 nm, to represent the actual input data for ANN. The 32 standard solutions were randomly chosen for training and 10 solutions were selected for the prediction set. The training was performed on several networks having varying number of neurons in the
Fig. 3. Effect of PDC concentration on the sensitivity. Conditions such as Fig. 2 with pH of 8.0.
hidden layer. During the training, the root means square error, which was taken for each training step. Fig. 4 shows the progress in the time, over the number of neurons in the hidden layer, required to get a fixed Ž2%. error. The results show that by increasing the number of neurons in the hidden layer the time of learning was decreased. However, the error in the training set decreases as far as 60 neurons, whereas after that, the error
Fig. 4. The relationship between numbers of neuron in hidden layers vs. time. Conditions such as Fig. 3 in the presence of PDC concentration of 0.174 M.
B. Rezaei et al. r Microchemical Journal 70 (2001) 35᎐40
39
Fig. 5. The relationship between numbers of hidden layer vs. time. Conditions such as Fig. 4.
Fig. 7. The relation between predicted and actual value for NiŽII..
increases. Therefore, 60 neurons over 10 000 epochs, as a hidden layer, were selected. The effected number of hidden layers Žwith 5 neurons in each layer. was studied at optimum reagent concentrations. Fig. 5 shows that by increasing the number of hidden layers, time of analysis increased intensity. Therefore, one hidden layer was selected for the study. At these optimized conditions, the expected vs. calculated values for ten predication sets were plotted as is
shown in Figs. 6 and 7. The results show the good regression between actual values and prediction values for CoŽII. and NiŽII. concentration. Under the optimized conditions, CoŽII. and NiŽII. can be determined in the range of 0.005 to 0.500 grml with the regression equation of Y s 0.2454q 0.0021C Ž r s 0.9940. and Y s 0.2795q 0.0025C Ž r s 0.9988., respectively, where C is the concentration of the metal ions as grml and Y is the absorbance. The limit of detection Žis equal to the average blank signal plus three times that of its standard deviation. was 0.004 grml for two ions. The influence of other potential interfering ions on the determination of 0.100 grml CoŽII. and NiŽII. was also shown in Table 1. Table 1 Influence of the other substances on the determination of 0.100 grml of NiŽII. and CoŽII.
Fig. 6. The relationship between the predicted and actual value for CoŽII..
Species
Tolerance ratio ŽWinorWNiŽII.,CoŽII. .
Sr2q, BrO3y Sb3q, Ca2q, Ba2q, Mg2q Csq, PO43y, C2 O42y Cr3, Cd2q, Pb2q, Hg2q Fe2q, Mn2q, Cu2q ClO3 y, Iy, Br2y
200 100 50 1
B. Rezaei et al. r Microchemical Journal 70 (2001) 35᎐40
40
Table 2 Determination of nickel and cobalt in alloys samples Nickel
Cobalt
Alloys samples
Found g ly1
Recovery %
In sample g ly1
Found g ly1
Recovery %
In sample g ly1
Hastelloy Da Inconcel 600a Nimonic 90b
5.892" 0.13 6.030" 0.12 0.580" 0.026
98.2 100.5 105.4
6.000 6.000 0.550
0.103" 0.006 ᎐ 0.180" 0.005
93.4 ᎐ 100.0
0.110 ᎐ 0.180
a b
Ref. w12x, p. 172. Ref. w12x, p. 211.
4. Application Finally, an application of the training network with 42 neurons in the input layer, 60 neurons in the hidden layer Žone hidden layer. was demonstrated by feeding it with new measurement spectra for three alloy samples Žtest samples. under optimum conditions, after dissolving it in the HCl᎐HNO3 media. The results as shown in Table 2. The recoveries of standard additions of Co and Ni to each sample were determined. The results show that the method is accurate and gives good recovery of added metal ions. The method has the same LOD with flame atomic absorption spectrometry, whereas, these elements can be analyzed simultaneously by using the proposed procedure.
Acknowledgements The authors acknowledge the Research Coun-
cil of Isfahan University of Technology for support of the project. References w1x w2x w3x w4x w5x w6x w7x w8x w9x w10x w11x w12x
Z. Shen, F. Nic, Y. Chen, Fensi Huaxane 19 Ž1991. 1272. V. Rigin, Anal. Chim. Acta 283 Ž1993. 895. O.W. Lan, S.Y. Ho, Anal. Chim. Acta 280 Ž1993. 269. Z. Shen, Z. Wang, Fensi Huaxane 21 Ž1993. 1313. A.A. Ensafi, H. Rahimi Mansour, K. Zarei, Fresenius J. Anal. Chem. 363 Ž1999. 646. D.T. Burns, M. Harriet, S.A. Barakat, Anal. Chim. Acta 259 Ž1992. 33. K. Fujinaga, M. Fukai, Y. Seike, M. Okumura, Anal. Sci. 15 Ž1999. 333. M.N. Tabi, R. Andrews, R. Naraganaswamy, Anal. Chim. Acta 330 Ž1996. 31. W.H. Chem, A.W.M. Lee, D.W.J. Kwong, Y.Z. Liang, K.M. Wang, Analyst 122 Ž1997. 657. J.M. Sutter, P.C. Jurs, Anal. Chem. 64 Ž1997. 856. E.B. Sandell, H. Onishi, Photometric Determination of Traces of Metals, Part I, John Wiley and Sons Inc, 1978, p. 476. W.H. Cubberly, P.M. Unterweiser, D. Benjamin, Metals Handbook American Society for Metals ŽASM., 9th Ed., Vol. 3, 1980.