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Analytica Chimica Acta 487 (2003) 181–188

Simultaneous determination of copper, nickel, cobalt andzinc using zincon as a metallochromic indicator with

partial least squares

J. Ghasemia,∗, Sh. Ahmadia, K. Torkestaniba 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 andzinc based on the formation of their complexes with 2-carboxy-2′-hydroxy-5′-sulfoformazyl benzene (zincon). The absorptionspectra were recorded from 515 through 750 nm. The effect of pH on sensitivity and the selectivity was studied in the range3.0–10.0 and the pH 8.0 was choused according to net analyte signal (NAS) as a function of pH. The concentration rangefor Cu2+, Ni2+, Co2+and Zn2+ in solution calibration sets were 0–2.6, 0–4.6, 0–3.0 and 0–4.92 ppm, respectively. The rootmean squares differences (RMSD) for copper, nickel, cobalt and zinc were 0.0181, 0.0488, 0.0309 and 0.0463, respectively.© 2003 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 ap-pear together in many real samples. Several techniquessuch as X-ray fluorescence[1], atomic fluorescencespectrometry[2], polarography[3], chromatography[4], atomic absorption spectrometry[5,6], etc. havebeen used for the simultaneous determination of theseions in different samples. Among the most widelyused analytical methods are those based on the UV-Visspectrophotometry techniques[7–11], due to the re-sulting experimental rapidity, simplicity and the wideapplication. However, the simultaneous determinationof these ions by the use of the traditional spectropho-

∗ Corresponding author. Tel.:+98-8317233063;fax: +98-8318231618.E-mail address: [email protected] (J. Ghasemi).

tometry techniques is difficult because, generally, theabsorption spectra overlap in a bright region and thesuperimposed curves are not suitable for quantitativeevaluation.

Nowadays quantitative spectrophotometry has beengreatly improved by the use of a variety of multivariatestatistical method; particularly principle componentregression (PCR) and partial least squares regression(PLS).

PLS regression has been found important in han-dling regression tasks in case there are many variables.The theoretical basic for PLS regression is found inseveral references[16–21]. The basic aspect of PLSregression is that it suggests that, after decompositionof X andY matrices into two new score and loadingmatrices using singular value decomposition or princi-pal component analysis, we should maximize the co-variance between score vector inX-space and a score

0003-2670/03/$ – see front matter © 2003 Published by Elsevier Science B.V.doi:10.1016/S0003-2670(03)00556-7

182 J. Ghasemi et al. / Analytica Chimica Acta 487 (2003) 181–188

vector inY-space or equivalently to maximize the sizeof the loading vector inY-space derived from the scorethe vector inX-space.

Since the metallochromic indicators are also knownas acid–base indicators, then, the complex formationreaction is affected by the variation of pH of the so-lution. So it is clear to see that the efficiency of thecomplex formation and as well as the sensitivity andselectivity are depends on the established pH value.The effect of pH on the sensitivity and selectivity wasstudied according to the net analyte signal (NAS) foreach component in a first-order system[15,16]. Thenet analyte signal is defined inEq. (1).

NAS = (I − RnR+n )rn (1)

whereI is the identity matrix,Rn the matrix of purespectra of all constituents except thenth analyte,R+

n

the pseudo inverse ofRn and rn is the spectrum ofthe analyte. The NAS is a vector and is related to theregression vector in following equations:

c = Rb + e (2)

b = NAS

‖NAS‖2(3)

in which ||NAS||2 designates the square root of the sumof squares of each element in the vector,b. Sensitivityand selectivity was calculated by using the followingequations:

SEN= 1

‖b‖2= ‖NAS‖2 (4)

SEL = 1

‖b‖2 ‖rn‖2= ‖NAS‖2

‖rn‖2(5)

In this work, the simultaneous spectrophotometricdetermination of copper, nickel, cobalt and zinc withzincon using PLS algorithm is reported. This ligand isa good chromogenic reagent[23].

2. Experimental

2.1. Reagent

All chemical were of analytical-reagent grade anddeionized water was used throughout. Stock solutionsof copper, nickel, cobalt and zinc were prepared from

their nitrate salts. A stock zincon solution (0.001 M) inwater was prepared by dissolving solid reagent sam-ples. Universal buffer solutions (pH 3–10) were pre-pared by mixing phosphoric, acetic and boric acids.0.04 M, of each chemical and sufficient amount of0.2 N NaOH solutions is poured into 100 ml of themixture.

2.2. Apparatus

Electronic absorption measurements were carriedout on a CECIL 9000 spectrophotometer (slit width0.2 nm and scan rate 300 nm/min) using glass orquartz cells of 1 cm path length. A Metrohm 692pH-meter furnished with a combined glass-saturatedcalomel electrode was used for pH measurements.The pH-meter was calibrated with at least two buffersolutions at pH 2.00 and 9.00.

2.3. Computer hardware and software

All absorption spectra were gathered, digitized andstored from 515 to 750 nm in steps of 1 nm and thentransferred (in ASCII format) to a Pentium 200 MHzcomputer for subsequent manipulation by PLS pro-gram. The data treatment was done with MATLABfor windows (Mathworks, version 6.0). PLS program(for calibration–prediction and experimental design)of PLS-Toolbox (Eigenvector company); was used.

2.4. Procedure

Known amounts of the standard solutions of eachcation, 2.0 ml of buffer solution were placed in a 10 mlvolumetric flask and completed to final volume withdeionized water (final pH was 8.0). The final con-centration of these solutions varied between 0.0–2.6,0.0–4.6, 0.0–3.0 and 0.0–4.92 ppm for Cu, Ni, Co andZn, respectively. Finally, the spectra of all preparedsolutions were recorded on spectrophotometer.

3. Results and discussion

Fig. 1shows the influence of the pH of the mediumon the absorption spectra of metal complexes werestudied over the pH range 3.0–10.0. Using NAS basedrelations[13,14], sensitivity and selectivity for each

J. Ghasemi et al. / Analytica Chimica Acta 487 (2003) 181–188 183

Fig. 1. Absorption spectra of the zincon and copper, nickel, cobalt and zinc complexes in different pH-value. Concentration of zincon is1.168× 10−5 and each ion is 1 ppm, () zincon, (---) Cu, (�) Ni, (+) Co, (×) Zn.

analyte at different pH values were calculatedFig. 2.Therefore, pH 8.0 was selected as the optimum value,to compromise the sensitivity and selectivity of all fourmetal ions.

The effect of zincon concentration was also inves-tigated, a reagent concentration of 1.6 × 10−4 M waschosen because it ensures a sufficient reagent excess.

3.1. One component calibration

In order to find the linear dynamic range of concen-tration of each cation, one component calibration wasperformed for each element. For the preparation ofzincon solution (0.001 M), appropriate amount addedto 25 ml universal buffer[22] in a 100 ml volumetric


Fig. 2. Plot of the sensitivity and selectivity versus pH for thezincon and four complexes: (�) zincon, (�) Cu, (�) Ni, (�) Co,(�) Zn.

Fig. 3. Analytical curve for univariate determination of copper, nickel, cobalt and zinc complexes.

flask and diluted to the mark with distilled water. Dif-ferent volumes of 100 ppm solution of copper wereadded to 5.0 ml of zincon solution at pH= 8 in a10.0 ml volumetric flask and diluted to the mark withdistilled water. After 30 min, the absorbance was readat the 600 nm. The same procedure was followed fornickel, cobalt, copper and zinc and the absorbanceof the solutions were read at 665, 656 and 630 nm,respectively. Linear regression results, line equationsandR2 are shown on theFig. 3.

3.2. Calibration and test mixtures

Two sets of standard solutions were prepared. Thecalibration set contains 35 standard solutions. Thecompositions of the calibration mixtures were selectedaccording to a (4, 4) simplex lattice design[12,13]. Formodel assessment, it was used of seven test mixtures.The concentration of each cation solution was in thelinear dynamic range of the cation, for the preparationof each solution, different volumes of four cation so-lutions (20 ppm) were added to 5.0 ml zincon solution(pH = 8) in a 10 ml volumetric flask. After 30 min,


Table 1The 35 designed experimental

No. Concentration (ppm)

Cu Ni Co Zn

1 2.60 0.00 0.00 0.002 1.95 1.15 0.00 0.003 1.95 0.00 0.75 0.004 1.95 0.00 0.00 1.235 1.30 2.30 0.00 0.006 1.30 1.15 0.75 0.007 1.30 1.15 0.00 1.238 1.30 0.00 1.50 0.009 1.30 0.00 0.75 1.23

10 1.30 0.00 0.00 2.4611 0.65 3.45 0.00 0.0012 0.65 2.30 0.75 0.0013 0.65 2.30 0.00 1.2314 0.65 1.15 1.50 0.0015 0.65 1.15 0.00 2.4616 0.65 0.00 3.00 0.0017 0.65 0.00 1.50 1.2318 0.65 0.00 0.75 2.4619 0.65 0.00 0.00 3.6920 0.00 4.60 0.00 0.0021 0.00 3.45 0.75 0.0022 0.00 3.45 0.00 1.2323 0.00 2.30 1.50 0.0024 0.00 2.30 0.75 1.2325 0.00 2.30 0.00 2.4626 0.00 1.15 2.25 0.0027 0.00 1.15 1.50 1.2328 0.00 1.15 0.75 2.4629 0.00 1.15 0.00 3.6930 0.00 0.00 3.00 0.0031 0.00 0.00 2.25 1.2332 0.00 0.00 1.50 2.4633 0.00 0.00 0.75 3.6934 0.00 0.00 0.00 4.9235 1.15 1.15 0.75 1.23

absorption spectra of the mixtures recorded. Thecalibration matrix used for the analysis is shown inTable 1.

3.3. Selection of optimum number of factors

To select the number of factors in PLS algorithm,in order to model the system without over fitting theconcentration data, a cross-validation method, leav-ing out one sample at a time, was used[17]. Giventhe set of 35 calibration spectra, the PLS calibrationon 34 spectra were performed, and using this calibra-

tion the concentration of the compounds in the sampleleft out during calibration was predicted. This processwas repeated 35 times until each calibration samplehad been left out once. The predicted concentrationof the compounds in each sample was compared withthe known concentration of the compound in this ref-erence sample and prediction residual error sum ofsquares (PRESS) was calculated. The PRESS was cal-culated in the same manner each time a new factor isadded to the PLS model.

The maximum number of factors used to calculatethe optimum PRESS was selected 18 (half the num-ber of standard plus one). One reasonable choice forthe optimum number of factors would be that numberwhich yielded the minimum PRESS. However, usingthe number of factors (h∗) that yields a minimum inPRESS usually lead to some over fitting. A better cri-terion for selecting the optimum number of factors in-volves the comparison of PRESS from model is notsignificantly greater than PRESS from the model withh∗ factors. TheF statistic was used to make the sig-nificance determination.

Haaland and Thomas[17] empirically determinedthat anF-ratio probability of 0.75 is a good choice.We selected as the optimum the number of factors forthe first PRESS values theF-ratio probability, whichdrops below 0.75,Fig. 4. The PRESS values are min-imum in the number of 6, 5, 8 and 5 for Cu, Ni, Coand Zn, respectively, then these numbers of factorsare selected as optimum for the calibration model. InFig. 4, the PRESS obtained by optimizing the calibra-tion matrix of the absorbance data with PLS method isshown.

The results obtained by applying PLS algorithm tothe seven prediction set samples are listed inTable 2.The plots of these predicted concentrations versusactual concentration using the optimum model areshown in Fig. 5. The correlation coefficients are0.9428, 0.9849, 0.9680 and 0.9918 for Cu, Ni, Co andZn, respectively, which again verify the good perfor-mance of PLS model in predicting the concentrationsof cations in mixture solutions.

3.4. Statistical parameters

For the constructed model, three parameters wereselected to test the prediction ability of the model forsimultaneous determination of Cu, Ni, Co and Zn. The


Fig. 4. Plot of PRESS vs. number of significant factors: (�) Ni, (�) Zn, (�) Co, (�) Cu.

RMSD, the square of the correlation coefficient (R2)and the relative error of prediction (REP), calculatedfor each component as follows:

RMSD =[

1

n

n∑i=1

(x̂i − xi)2

]0.5

R2 =∑n

i=1(x̂i − x̄i)2∑n

i=1(xi − x̄i)2

REP(%) = 100

x̄

[1

n

n∑i=1

(x̂i − xi)2

]0.5

wherexi is the true concentration of the analyte in thesamplei, x̂i represented the estimated concentration of

Table 2Prediction 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

1 0.66 0.80 0.80 1.50 0.63 0.86 0.88 1.74 −4.54 7.50 11.25 16.002 0.50 1.30 0.90 1.10 0.47 1.31 0.97 1.26 −6.00 0.76 8.88 14.543 0.90 1.50 0.58 0.70 0.82 1.34 0.66 0.70 −8.88 −10.66 13.79 0.004 0.60 1.40 0.70 1.14 0.56 1.40 0.76 1.26 −6.66 0.00 8.57 10.525 0.80 1.50 0.60 0.82 0.73 1.64 0.64 0.87 −8.75 9.33 6.66 6.096 0.38 0.00 0.00 4.20 0.37 −0.15 −0.09 4.13 −2.63 – – −1.667 0.00 4.00 0.00 0.64 −0.05 4.21 −0.11 0.65 – 5.25 – 1.56

the analyte in the samplei, x̄i the mean of true concen-tration in the prediction set andn is the total numbersample used in the prediction sets. The concentrationdata, the predicted values and their relative errors ofthe prediction set are shown inTable 2. The value ofRMSD, REP,R2, number of factors and PRESS ac-cording the values listed inTable 2are summarized inTable 3.

3.5. Effect of foreign ions

The interference due to several cations and an-ions was studied in detail. For these studies differentamounts of the ionic species were added to a mix-ture of Cu, Ni, Co and Zn containing 0.9, 1.5, 0.58


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 a2000 ppm of interference to metal ions, and if the in-terference occurred the concentration of interferencewas progressively reduced until interference ceased.The tolerance limits were taken as those concentra-tions causing changes no greater than±%5 in theconcentration of analytes. The tolerance limits aredepicted inTable 4.

3.6. Determination of Cu, Ni, Co and Zn in realsamples

In order to test the applicability and matrix inter-ference of the proposed method to the analysis ofreal matrix samples, the method was applied in a

Table 3Statistical parameters of the test matrix using the PLS model

Cations No. offactors

RMSD R2 REP (%)

Cu2+ 6 0.018 0.94 3.29Ni2+ 5 0.049 0.99 3.25Co2+ 8 0.031 0.97 6.05Zn2+ 5 0.045 0.99 3.21

variety of synthetic solutions. Permute, alloy sampleand red brass composition like solution were pre-pared and analyzed as described under experimen-tal. The results of the prediction are summarized inTable 5.

It can be seen that good recovery values are obtainedby the PLS method using absorbance data. Thesesatisfactory results indicate that the method wouldbe effective for the analysis of samples of similarcomplexity.

Table 4Effect of various ions on the determination of four metals

Concentration(ppm)

Foreign ions

>2000 F−, Cl−, Br−, I−, CO32−, CH3COO−, NO3

−1000 Li(I), Na(I), K(I), Tl(I), Rb(I), Mg(II)500 IO4

−, BrO3−, S2O3

2−400 SCN−, Tartarate, ClO42− SO4

2−20 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)


Table 5Some industrial alloys analysis

Sample Predicted value (ppm) Recovery (%)

Cu Ni Co Zn Cu Ni Co Zn

M1a Cu2+ (1.48), Ni2+ (0.64), Co2+ (0.91) 1.41 0.70 0.93 −0.01 95.11 109.44 102.00 –M2a Cu2+ (2.03), Ni2+ (0.30), Zn2+ (0.78) 1.92 0.44 0.09 0.75 94.43 110 – 95.76M3a Cu2+ (2.12), Zn2+ (0.94) 2.06 0.21 0.00 0.92 97.22 – – 97.35

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 ex-tremely difficult complex system due to the highspectral overlapping observed between the absorptionspectra for these components. PLS modeling wasestablished, with good prediction ability in the realmatrix samples. Results show that partial least squareis an excellent calibration method to determination ofthese metals together without pretreatment in complexsamples.

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) 1313.[4] N. Xie, C. Huang, H.D. Fu, Sepu 8 (2) (1990) 114;

N. Xie, C. Huang, H.D. Pu, Chem. Abstr. 113 (1990) 094170.[5] Z. Shen, F. Nie, Y. Chem, Fensi Huaxane 19 (11) (1991)

1272.

[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) 1389.[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,

1981.[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) 1167.[16] H. Martens, T. Naes, Multivariate Calibration, Wiley,

Chichester, 1989.[17] D.M. Halaand, E.V. Thomas, Anal. Chem. 60 (1988) 1193.[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) 1345.[23] M. Otto, W. Wegscheider, Anal. Chem. 61 (1989) 1847.

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