Simultaneous determination of copper, nickel, cobalt and zinc using zincon as a metallochromic indicator with partial least squares

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1 Analytica Chimica Acta 487 (2003) Simultaneous determination of copper, nickel, cobalt and zinc using zincon as a metallochromic indicator with partial least squares J. Ghasemi a,, Sh. Ahmadi a, K. Torkestani b a Department of Chemistry, Razi University, Kermanshah, Iran b Research Institute of Petroleum Industry, Tehran, Iran Received 3 December 2002; received in revised form 17 April 2003; accepted 2 May 2003 Abstract The partial least squares (PLS) applied to the simultaneous determination of the divalent ions of copper, nickel, cobalt and zinc based on the formation of their complexes with 2-carboxy-2 -hydroxy-5 -sulfoformazyl benzene (zincon). The absorption spectra were recorded from 515 through 750 nm. The effect of ph on sensitivity and the selectivity was studied in the range and the ph 8.0 was choused according to net analyte signal (NAS) as a function of ph. The concentration range for Cu 2+,Ni 2+,Co 2+ and Zn 2+ in solution calibration sets were 0 2.6, 0 4.6, and ppm, respectively. The root mean squares differences (RMSD) for copper, nickel, cobalt and zinc were , , and , respectively Published by Elsevier Science B.V. Keywords: Partial least squares; Copper; Nickel; Cobalt; Zinc; Spectrophotometric determination 1. Introduction Copper, nickel, cobalt and zinc are metals that appear together in many real samples. Several techniques such as X-ray fluorescence [1], atomic fluorescence spectrometry [2], polarography [3], chromatography [4], atomic absorption spectrometry [5,6], etc. have been used for the simultaneous determination of these ions in different samples. Among the most widely used analytical methods are those based on the UV-Vis spectrophotometry techniques [7 11], due to the resulting experimental rapidity, simplicity and the wide application. However, the simultaneous determination of these ions by the use of the traditional spectropho- Corresponding author. Tel.: ; fax: address: jahan.ghasemi@tataa.com (J. Ghasemi). tometry techniques is difficult because, generally, the absorption spectra overlap in a bright region and the superimposed curves are not suitable for quantitative evaluation. Nowadays quantitative spectrophotometry has been greatly improved by the use of a variety of multivariate statistical method; particularly principle component regression (PCR) and partial least squares regression (PLS). PLS regression has been found important in handling regression tasks in case there are many variables. The theoretical basic for PLS regression is found in several references [16 21]. The basic aspect of PLS regression is that it suggests that, after decomposition of X and Y matrices into two new score and loading matrices using singular value decomposition or principal component analysis, we should maximize the covariance between score vector in X-space and a score /03/$ see front matter 2003 Published by Elsevier Science B.V. doi: /s (03)

2 182 J. Ghasemi et al. / Analytica Chimica Acta 487 (2003) vector in Y-space or equivalently to maximize the size of the loading vector in Y-space derived from the score the vector in X-space. Since the metallochromic indicators are also known as acid base indicators, then, the complex formation reaction is affected by the variation of ph of the solution. So it is clear to see that the efficiency of the complex formation and as well as the sensitivity and selectivity are depends on the established ph value. The effect of ph on the sensitivity and selectivity was studied according to the net analyte signal (NAS) for each component in a first-order system [15,16]. The net analyte signal is defined in Eq. (1). NAS = (I R n R + n )r n (1) where I is the identity matrix, R n the matrix of pure spectra of all constituents except the nth analyte, R + n the pseudo inverse of R n and r n is the spectrum of the analyte. The NAS is a vector and is related to the regression vector in following equations: c = Rb + e (2) b = NAS (3) NAS 2 in which NAS 2 designates the square root of the sum of squares of each element in the vector, b. Sensitivity and selectivity was calculated by using the following equations: SEN = 1 = NAS b 2 (4) 2 1 SEL = = NAS 2 (5) b 2 r n 2 r n 2 In this work, the simultaneous spectrophotometric determination of copper, nickel, cobalt and zinc with zincon using PLS algorithm is reported. This ligand is a good chromogenic reagent [23]. 2. Experimental 2.1. Reagent All chemical were of analytical-reagent grade and deionized water was used throughout. Stock solutions of copper, nickel, cobalt and zinc were prepared from their nitrate salts. A stock zincon solution (0.001 M) in water was prepared by dissolving solid reagent samples. Universal buffer solutions (ph 3 10) were prepared by mixing phosphoric, acetic and boric acids M, of each chemical and sufficient amount of 0.2 N NaOH solutions is poured into 100 ml of the mixture Apparatus Electronic absorption measurements were carried out on a CECIL 9000 spectrophotometer (slit width 0.2 nm and scan rate 300 nm/min) using glass or quartz cells of 1 cm path length. A Metrohm 692 ph-meter furnished with a combined glass-saturated calomel electrode was used for ph measurements. The ph-meter was calibrated with at least two buffer solutions at ph 2.00 and Computer hardware and software All absorption spectra were gathered, digitized and stored from 515 to 750 nm in steps of 1 nm and then transferred (in ASCII format) to a Pentium 200 MHz computer for subsequent manipulation by PLS program. The data treatment was done with MATLAB for windows (Mathworks, version 6.0). PLS program (for calibration prediction and experimental design) of PLS-Toolbox (Eigenvector company); was used Procedure Known amounts of the standard solutions of each cation, 2.0 ml of buffer solution were placed in a 10 ml volumetric flask and completed to final volume with deionized water (final ph was 8.0). The final concentration of these solutions varied between , , and ppm for Cu, Ni, Co and Zn, respectively. Finally, the spectra of all prepared solutions were recorded on spectrophotometer. 3. Results and discussion Fig. 1 shows the influence of the ph of the medium on the absorption spectra of metal complexes were studied over the ph range Using NAS based relations [13,14], sensitivity and selectivity for each

3 J. Ghasemi et al. / Analytica Chimica Acta 487 (2003) Fig. 1. Absorption spectra of the zincon and copper, nickel, cobalt and zinc complexes in different ph-value. Concentration of zincon is and each ion is 1 ppm, ( ) zincon, (---) Cu, ( ) Ni, (+) Co, ( ) Zn. analyte at different ph values were calculated Fig. 2. Therefore, ph 8.0 was selected as the optimum value, to compromise the sensitivity and selectivity of all four metal ions. The effect of zincon concentration was also investigated, a reagent concentration of Mwas chosen because it ensures a sufficient reagent excess One component calibration In order to find the linear dynamic range of concentration of each cation, one component calibration was performed for each element. For the preparation of zincon solution (0.001 M), appropriate amount added to 25 ml universal buffer [22] in a 100 ml volumetric

4 184 J. Ghasemi et al. / Analytica Chimica Acta 487 (2003) flask and diluted to the mark with distilled water. Different volumes of 100 ppm solution of copper were added to 5.0 ml of zincon solution at ph = 8ina 10.0 ml volumetric flask and diluted to the mark with distilled water. After 30 min, the absorbance was read at the 600 nm. The same procedure was followed for nickel, cobalt, copper and zinc and the absorbance of the solutions were read at 665, 656 and 630 nm, respectively. Linear regression results, line equations and R 2 are shown on the Fig Calibration and test mixtures Fig. 2. Plot of the sensitivity and selectivity versus ph for the zincon and four complexes: ( ) zincon, ( ) Cu, ( ) Ni, ( ) Co, ( ) Zn. Two sets of standard solutions were prepared. The calibration set contains 35 standard solutions. The compositions of the calibration mixtures were selected according to a (4, 4) simplex lattice design [12,13]. For model assessment, it was used of seven test mixtures. The concentration of each cation solution was in the linear dynamic range of the cation, for the preparation of each solution, different volumes of four cation solutions (20 ppm) were added to 5.0 ml zincon solution (ph = 8) in a 10 ml volumetric flask. After 30 min, Fig. 3. Analytical curve for univariate determination of copper, nickel, cobalt and zinc complexes.

5 J. Ghasemi et al. / Analytica Chimica Acta 487 (2003) Table 1 The 35 designed experimental No. Concentration (ppm) Cu Ni Co Zn absorption spectra of the mixtures recorded. The calibration matrix used for the analysis is shown in Table Selection of optimum number of factors To select the number of factors in PLS algorithm, in order to model the system without over fitting the concentration data, a cross-validation method, leaving out one sample at a time, was used [17]. Given the set of 35 calibration spectra, the PLS calibration on 34 spectra were performed, and using this calibration the concentration of the compounds in the sample left out during calibration was predicted. This process was repeated 35 times until each calibration sample had been left out once. The predicted concentration of the compounds in each sample was compared with the known concentration of the compound in this reference sample and prediction residual error sum of squares (PRESS) was calculated. The PRESS was calculated in the same manner each time a new factor is added to the PLS model. The maximum number of factors used to calculate the optimum PRESS was selected 18 (half the number of standard plus one). One reasonable choice for the optimum number of factors would be that number which yielded the minimum PRESS. However, using the number of factors (h ) that yields a minimum in PRESS usually lead to some over fitting. A better criterion for selecting the optimum number of factors involves the comparison of PRESS from model is not significantly greater than PRESS from the model with h factors. The F statistic was used to make the significance determination. Haaland and Thomas [17] empirically determined that an F-ratio probability of 0.75 is a good choice. We selected as the optimum the number of factors for the first PRESS values the F-ratio probability, which drops below 0.75, Fig. 4. The PRESS values are minimum in the number of 6, 5, 8 and 5 for Cu, Ni, Co and Zn, respectively, then these numbers of factors are selected as optimum for the calibration model. In Fig. 4, the PRESS obtained by optimizing the calibration matrix of the absorbance data with PLS method is shown. The results obtained by applying PLS algorithm to the seven prediction set samples are listed in Table 2. The plots of these predicted concentrations versus actual concentration using the optimum model are shown in Fig. 5. The correlation coefficients are , , and for Cu, Ni, Co and Zn, respectively, which again verify the good performance of PLS model in predicting the concentrations of cations in mixture solutions Statistical parameters For the constructed model, three parameters were selected to test the prediction ability of the model for simultaneous determination of Cu, Ni, Co and Zn. The

6 186 J. Ghasemi et al. / Analytica Chimica Acta 487 (2003) Fig. 4. Plot of PRESS vs. number of significant factors: ( ) Ni, ( ) Zn, ( ) Co, ( ) Cu. RMSD, the square of the correlation coefficient (R 2 ) and the relative error of prediction (REP), calculated for each component as follows: [ ] n RMSD = (ˆx i x i ) 2 n R 2 = i=1 ni=1 (ˆx i x i ) 2 ni=1 (x i x i ) 2 REP(%) = 100 x [ 1 n ] 0.5 n (ˆx i x i ) 2 i=1 where x i is the true concentration of the analyte in the sample i, ˆx i represented the estimated concentration of the analyte in the sample i, x i the mean of true concentration in the prediction set and n is the total number sample used in the prediction sets. The concentration data, the predicted values and their relative errors of the prediction set are shown in Table 2. The value of RMSD, REP, R 2, number of factors and PRESS according the values listed in Table 2 are summarized in Table Effect of foreign ions The interference due to several cations and anions was studied in detail. For these studies different amounts of the ionic species were added to a mixture of Cu, Ni, Co and Zn containing 0.9, 1.5, 0.58 Table 2 Prediction set composition, predicted values and relative errors No. Actual value (ppm) Predicted value (ppm) Relative error (%) Cu Ni Co Zn Cu Ni Co Zn Cu Ni Co Zn

7 J. Ghasemi et al. / Analytica Chimica Acta 487 (2003) Fig. 5. Plots of predicted concentration vs. actual concentration for four cations in the prediction set. and 0.7 ppm, respectively. The starting point was a 2000 ppm of interference to metal ions, and if the interference occurred the concentration of interference was progressively reduced until interference ceased. The tolerance limits were taken as those concentrations causing changes no greater than ±%5 in the concentration of analytes. The tolerance limits are depicted in Table Determination of Cu, Ni, Co and Zn in real samples In order to test the applicability and matrix interference of the proposed method to the analysis of real matrix samples, the method was applied in a Table 3 Statistical parameters of the test matrix using the PLS model Cations No. of factors RMSD R 2 REP (%) Cu Ni Co Zn variety of synthetic solutions. Permute, alloy sample and red brass composition like solution were prepared and analyzed as described under experimental. The results of the prediction are summarized in Table 5. It can be seen that good recovery values are obtained by the PLS method using absorbance data. These satisfactory results indicate that the method would be effective for the analysis of samples of similar complexity. Table 4 Effect of various ions on the determination of four metals Concentration Foreign ions (ppm) >2000 F,Cl,Br,I,CO 2 3,CH 3 COO,NO Li(I), Na(I), K(I), Tl(I), Rb(I), Mg(II) 500 IO 4, BrO 3,S 2 O SCN, Tartarate, ClO 2 4 SO Mo(IV), Sr(II) 10 Ba(II), Cd(II) 5 Pb(II) 2 Ag(I) 1 Mn(II), Fe(III) <1 Zr(IV), Al(III), Hg(II)

8 188 J. Ghasemi et al. / Analytica Chimica Acta 487 (2003) Table 5 Some industrial alloys analysis Sample Predicted value (ppm) Recovery (%) Cu Ni Co Zn Cu Ni Co Zn M1 a Cu 2+ (1.48), Ni 2+ (0.64), Co 2+ (0.91) M2 a Cu 2+ (2.03), Ni 2+ (0.30), Zn 2+ (0.78) M3 a Cu 2+ (2.12), Zn 2+ (0.94) The number in parenthesis indicates the concentration of element taken for analysis in ppm. a M1, M2 and M3 are permute, alloy sample and red brass, respectively. 4. Conclusions The copper nickel cobalt zinc mixture is an extremely difficult complex system due to the high spectral overlapping observed between the absorption spectra for these components. PLS modeling was established, with good prediction ability in the real matrix samples. Results show that partial least square is an excellent calibration method to determination of these metals together without pretreatment in complex samples. References [1] O.W. Lau, S.Y. Ho, Anal. Chim. Acta 280 (2) (1993) 269. [2] V. Rigin, Anal. Chim. Acta 283 (2) (1993) 895. [3] Z. Shen, Z. Wang, Fensi. Huaxue 21 (11) (1993) [4] N. Xie, C. Huang, H.D. Fu, Sepu 8 (2) (1990) 114; N. Xie, C. Huang, H.D. Pu, Chem. Abstr. 113 (1990) [5] Z. Shen, F. Nie, Y. Chem, Fensi Huaxane 19 (11) (1991) [6] R.M. Brotheridge, et al., Analyst 123 (19998) 69. [7] J.H. Kalivas, Anal. Chim. Acta 428 (2001) 31. [8] J. Ghasemi, A. Niazi, A. Safavi, Anal. Lett. 34 (2001) [9] J. Ghasemi, A. Niazi, Microchem. J. 71 (2002) 1. [10] J. Ghasemi, R. Amini, A. Niazi, Anal. Lett. 35 (2002) 533. [11] A.M. Garcia Rodriguez, A. Garcia de Torres, J.M. Cano Pavon, C. Bosch Ojeda, Talanta 47 (1998) 463. [12] J.A. Corenl, Experiment with Mixture, Wiley, New York, [13] I. Duran, M. Arsenio, M. dela Pena, A.E. Mansila, F. Salinas, Analyst 118 (1993) 807. [14] K.S. Booksh, B.R. Kowalski, Anal. Chem. 66 (1994) 782A. [15] A. Lorber, Anal. Chem. 58 (1986) [16] H. Martens, T. Naes, Multivariate Calibration, Wiley, Chichester, [17] D.M. Halaand, E.V. Thomas, Anal. Chem. 60 (1988) [18] K.R. Beebe, B.R. Kowalski, Anal. Chem. 59 (1987) 1007A. [19] S. Wold, P. Geladi, K. Esbensen, J. Ochman, J. Chemom. 1 (1987) 41. [20] S. Wold, et al., Chemom. Intell. Lab. Syst. 58 (2001) 109. [21] R.W. Gerlach, B.R. Kowalski, H. Wold, Anal. Chim. Acta 112 (1979) 417. [22] R.M. Rush, J.H. Yoe, Anal. Chem. 26 (1954) [23] M. Otto, W. Wegscheider, Anal. Chem. 61 (1989) 1847.