Automatic simultaneous determination of Ca and Mg in natural waters with no interference separation

Chemometrics and intelligent laboratory systems Chemometrics and Intelligent Laboratory Systems 24 (1994) 55-63

Automatic simultaneous determination of Ca and Mg in natural waters with no interference separation I. RuisBnchez, A. Rius, M.S. Larrechi, M.P. Callao, F.X. Rius * Department

of Chemistry,

Uniuersitat Rouira i virgili de Tarragona, PI. Imperial T&raco, I, 43005 Tarragona, Spain

(Received 25 October 1993; accepted 9 March 1994)

Abstract In this study a methodology is reported for the automatic, simultaneous determination of Ca and Mg in natural waters which is not affected by the presence of species that may interfere with other methods. A flow system based on the sinusoidal injection analysis (SIA) methodology with diode array spectrophotometric detection of the complex formed by both analytes with the Arsenazo III has been used. The technique of multivariate calibration used, PLSl, enables Ca to be determined in the concentration range 45-85 mg 1-l and Mg to be determined in the range 2-70 mg I-‘, with no need for the samples to be previously diluted. The accuracy of the method has been tested by comparing the results obtained with the standard atomic absorption spectrometry method. With an analysis speed of 20-30 samples per hour, the RMSE in terms of standard deviation of the original variables was 6.1 for Mg and 6.4 for Ca.

1. Introduction Determining Ca and Mg is a routine sort of analysis in laboratories dedicated to water control. The standard methods of analysis range from the more classical gravimetric and volumetric techniques to the use of UV-VIS and atomic absorption [ll spectrophotometric (AAS) instrumental techniques. In all of these techniques, every cation of every sample has to be determined independently to the corresponding detriment in reproducibility, analysis time or cost. In recent years the tendency towards automation

* Corresponding

author.

has favoured the development of flow injection analysis (FIA) methodologies to determine these cations in various sample types. Among other automatic flow methods several applications that use potentiometers with selective electrodes [2,3], combinations of flame photometry and AAS [4,5], determining total alkalinity by means of acid-base reactions [6], spectrophotometric determinations using various chromogenic reagents [7-111 and, very recently, the simultaneous determination of these cations by using flow-rate gradients and spectrophotometric detection in the unsegmented system [12,131, can be found. In these studies, when both cations are determined simultaneously, a complementary study of the interference exerted by other ions present in the sample is needed.

0169-7439/94/%07.00 0 1994 Elsevier Science B.V. All rights reserved SSDI0169-7439(94)00025-E

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In this paper a methodology is reported for the simultaneous determination of Ca and Mg using a sinusoidal flow [14,15] with multivariate spectrophotometric detection of the complex formed with Arsenazo III. For control analyses that do not require any great versatility the sinusoidal injection analysis @IA) methodology has several advantages over FIA, among which it is worth pointing out the pulse free flow, the chance of using low cost simple instrumental designs which are cheap to maintain [15] and, by means of an appropriate control of the interdispersion of the samples and the reagents, the ability to determine these analytes in a wide range of concentrations. The use of a SIA system equipped with a diode array detector allows the simultaneous determination of Ca and Mg without previously separating the interferences since their effect forms part of the multivariate calibration model, PLS, developed.

2. Experimental 2.1. Samples Multivariate calibration was carried out with 29 samples of natural water fit for human consumption, 7 of which were commercially available

Fig. 1. Block diagram

Systems 24 (1994) 55-63

and 22 of which were obtained by mixing the same. The accuracy of the results was checked by using riverine water reference material for trace metals, SLRS-2 [ 161. 2.2. Reagents For the determination of Ca and Mg by multivariate SIA detection a stock solution of 0.98 g l- ’ Arsenazo III, and a buffer of 0.5 TRIS (tris hydroxymethyl aminomethane) whose pH is adjusted to 8.5 with Merck analytical reagent grade HCl. For their determination by AAS, standards of both cations prepared from 1000 ppm Merck Tritisol solutions were used. 5000 ppm lanthanum solution was made from La,0 analytical reagent. All the solutions were prepared using Millipore water. 2.3. Apparatus and manifold The analytical system used is shown in Fig. 1. It consists of a Gilson Minipuls 2 peristaltic pump suitably adapted so as to provide a sinusoidal flow by equipping it with a ‘cam’ and a ‘plunger’ which is connected to a Hamilton 1005 TTL syringe, a EUROSAS EPS 136 PBP six-way automatic valve, OMNIFIT, PTFE connecting tubing and a Hellma 178.711-QS flow cell.

of the analytical

system used.

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2,4. Automatic flow control The detection in our problem’s flow system is based on the recording of the complex spectrum formed by the analytes of the sample and reagent which were injected consecutively into the flow. The measurement cycle includes the following operations: (a) aspiration of the carrier (doubly distilled water); (b) aspiration of the sample; (c) aspiration of the reagent; (d) aspiration of the carrier in such a way that the sample-reagent overlap area is displaced to the detector cell; (e) a period of flow stoppage during which the measurement is carried out; (f) the ejection of the aspirated liquid in stages (a-d). This procedure is controlled from a PC for which it is necessary: (a) to connect the GSIOC communication channel of the Gilson pump to a RS-232 inlet by means of a 60.5 RS-232 adaptor and (b) to equip the computer with an A/D PC-711s data acquisition card and to connect it to the TTL outlet of the EPS 1306 valve. The drivers that go with the card as well as the interface linked to the 605 RS-232 adaptor, enabled home-made programmes, written in TurboBasic, to automatically and simultaneously control the pump and the valve from the computer.

Table 1 Operational parameters

:

9

e :: 9

0.6

I

450

500

550 A (nd

600

I

650

Fig. 2. UV-Vis spectra of experimental samples: (1) Arsenazo-III; (2-4) Ca standards; (5-7) Mg standards.

2.5. Operational flow parameters In SIA the key parameter that must be controlled is the overlap zone between the sample and the reagent. Within this zone a detection point must be located at which the coefficient of dispersion, D, is greater than 2 so that there is efficient mixing. As this value goes up, so the dilution of the sample increases. This value is related to the volume of sample injected, V,, and the volume of the reagent, V,. These volumes, for a value of D = 2, are related according to the expression:

Manifold dimensions

Teflon Teflon

0.8 mm 855 ul 170 cm Ll 75 cm 0.8 mm 375 I.LI L2 Flow cell: 30 ul, optical path length 10 mm Sinusoidal flow parameters

Pump frequency Syringe volume Cam rotation angles, a Radius of the cam Radius of the syringe V,, stroke volume Maximum flow ratio

0.25 rpm 5 ml initial = 0” final = 141” 2.35 cm 0.52 cm 3.56 ml 3.1 ml/min

Injected volumes and interval times v,l

v, K

VCl

Carrier 1.9 ml Sample 52 ~1 Reagent 310 ~1 Carrier 1.3 ml

58 s reversed 1 s reversed 6 s reversed 29 s reversed

stroke stroke stroke stroke

iV being the number of mixing stages and V1,2 the volume of the sample. In our case, since the relation V,/V, is 0.14 (Table 11, independently of N, we may suppose that the value of D is greater than 2. Another necessary condition is that there be sufficient excess of reagent at the measurement point so that there is a complete reaction in the whole range of Ca and Mg concentrations which are normal for the waters under consideration. This is achieved when the volume of reagent in relation to the volume of sample is greater than 4 [15]. In our cases this relation is 6 (Table 1). The appropriate measurement point was calculated experimentally by means of Ca and Mg

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standards whose concentrations were similar to the highest and lowest limits of the samples to be studied. This point corresponds to I’= 1.3 ml (29 s after initiating the second aspiration of the carrier) (Table 1). Fig. 2 shows the spectra of the reagent together with those of the prepared calibration standards. A slight difference can be seen between the spectrum of the reagent and the spectrum of the complex with Mg when the concentration of the latter is greater than 10 ppm. When the spectra of the reagent and the spectra of the complexes with Ca are compared, differences with the reagent are observed as from 20 ppm of Ca and up to 100 ppm. Since the concentrations of both cations for the samples under consideration are included in this range we consider this point in the flow system as suitable for carrying out the determination. The spectrophotometric measurement at this point is carried out 15 s after stopping the flow, after which time the absorbance remains practically constant. 2.6. Data acquisition and processing A Hewlett-Packard 8452A diode array spectrophotometer was used connected to an HP Vectra 286/12 PC and DESK JET 500 printer. The incorporated software, HP89531A, enables us to select the data acquisition conditions and provides a file in ASCII code. The spectrum is registered between 450 and 650 nm with an integration time of 0.1 s. The data considered consisted of absorbance measurements taken every 2 nm within this range and in this way the spectrum was numbered with 101 absorbances values within the range under consideration. The following programmes were used for the processing of the data: STATGRAPHICS [17] for the preprocessing of the data, UNSCRAMBLER 1181to establish the multivariate calibration model (PLS) and ULC [19] for the statistical validation of the method. The calcium and magnesium content of the water selected as calibration standards and test samples were determined by AAS using standard procedures [l]. To do so an atomic absorption instrumentation laboratory AA/ AE spectrophotometer 551 was used, assessing the absence of

matrix effects by means of the standard additions technique.

3. Results and discussion The application of the analytical procedure described to the 29 analysed water samples gives a data matrix (29 x 101) in which each vector row represents an absorption spectrum of the complex formed by the ions present in the water which react with the Arsenazo III (Fig. 2). The water samples were chosen so that the concentration of Ca and Mg covers, as homogenously as possible, the range of concentration in which these analytes can be found in the commonest natural waters. Neither an initial visual examination of the original data matrix, nor the representation of the water samples (scores) in the reduced space of the first two principal components calculated in the modelling process of the Ca variable (Fig. 3) show any tendencies which may suggest the presence of several different models. Nor does it allow the presence of any outliers to be observed at this point. Plotting the scores of the objects for the first factors assessed for Mg enable to draw conclusions that are analogous to those for Ca. However, as far as the variables are concerned, the presence of spurious peaks can be seen possibly due to deficiencies in the detection system. This presence is greater at higher wave frequencies and so, prior to establishing the mul-

29

24 0.3

0.0

.

14

21

10 6

11 59

19

26

13

2015

3

16 27 25 28

23

” : 2

19

22

2 -0.3c

I2 17

7 1

8 4

-0.50

0.00 Factor1

0.50

Fig. 3. Score plot for the first two factors of the PLS model developed for Ca (explained variance 98%).

I. Ruisknchez et al. /Chemometrics and Intelligent Laboratory Systems 24 (1994) 55-63

tivariate calibration model, it was deemed appropriate to carry out a smoothing of the spectrum. This was done using the three-point average technique [20] by means of the smoothing module of the STATGRAPHICS package. The various multivariate calibration techniques establish a linear relationship between the responses, in our case the calcium and magnesium content in the analysed samples, and the recorded spectral data. Due to the collinearity in the values of recorded absorbance, X variables, and to the absence of a significant correlation between the concentration of the determined metal ions, Y variables, the PLSl multivariate regression technique [21] was chosen using the UNSCRAMBLER software. Prior to calibration the variables were centered without undergoing a standardization procedure since, as they are all spectroscopic data, they are all expressed in the same units and there are no variables that incorporate special conditions of noise. Moreover, in our case, a standardization would signify giving the same importance to those wavelengths whose absorbance varies ostensibly with concentration as to those others in which the spectrum undergoes less change. Since we are working in the UV-Vis zone, it was not considered appropriate to previously select the spectral work areas because both the Ca(I1) and the Mg(I1) complexes absorb to a greater or lesser degree throughout the chosen wavelength range. The number of significant factors that form part of the multivariate calibration model was assessed by crossvalidation, using the leave-oneout procedure. Fig. 4 shows the mean square error (MSE) of prediction for each Y variable as a function of the number of factors used in each model. The prediction ability shown by the model developed for Mg with four factors, MSE = 37.5, (85.0% of the total Y variance) is higher than that shown by the Ca model which, with a maximum of two factors, has an MSE prediction error of 41.5 (66.3% of the total variance in Y). These values show that while the Mg concentration of waters whose composition is unknown may be determined with a certain precision, the determination of the Ca concentration will be less pre-

59

cise. From a chemical point of view, the four representative factors for Mg could be interpreted as four main causes of variation in spectral data: the presence of concentrations of Ca, Mg, reagent and other interferences. Although the chemical composition is the same for the Ca modelling, this is not reflected in the number of representative factors which, in this case, are only two. From the chemical point of view, this may be explained by the fact that as the Ca concentration changes there is a more important change in the spectrum registered in relation to the reagent than the one observed when the Mg concentration is varied (Fig. 2). This fact could cause, in the case of Ca, the other sources that provoke the variation (Mg and possible interferences) to be minimized compared to the main ones (Ca and Arsenazo). The study of the plots of the loading weights, rva (not shown), for the two assessed models, which relate the X variables, or spectral variables, with the Y variables or concentrations, by maximizing the covariance between X,_,w, and y and the plot of the spectral loadings, p,, corresponding to the modelling of the X variables according to the model X = TP’ + E showed that W, and p, are different for all factors considered showing that there are differences between the modelling of the X-Y relationship and the X-X relationship.

0

),

t

1

I

I

2 Factor

3

4

5

6

number

Fig. 4. Prediction ability expressed as mean square error (MSE) versus number of factors of the PLSl model for each variable.

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The first two factors corresponding to the model calculated for the Ca variable account for 98% of the total variance of the X variables and for 66.3% of the variance of the Y variable in the calibration process. So the plotting of wr versus w2, Fig. 5, of the spectral variables throughout the whole range of the spectrum considered shows the correlation that exists between the absorbances of neighbouring wavelengths. On the other hand, there is a negative correlation between shorter wavelengths and the concentration of Ca. For the former the loading weight values are negative for both factors while for the Ca variable it is positive. A similar but positive correlation exists between the longer wavelengths and the concentration of this ion, reflecting, in fact, the variations that the original spectra undergo due to the different levels of the Ca ion. Thus, all the considered spectral variables contain information for the modelling of the ion under consideration. This conclusion is reinforced when the plot of the B, coefficients is analysed versus the original variables shown in Fig. 6. The B, regression coefficients intervene in the multivariate linear prediction model: Y = B, + B,X and reveal the importance of the original spectral variables in predicting the metal ion concentrations. The plot of these coefficients for the model established with two factors for Ca clearly shows that the loadings calculated in the modelling process for the first

-0.10

t

I -0.1 .

0

0.1 Factor

0.2

0:3

/w

Fig. 5. Plot of the loading weights, Ca (factor 1 versus factor 2).

w,, for the PLS model for

I

I

450

490

I

530

570

I

610

65t

A (md

Fig. 6. Regression coefficients B, for the simplified PLS model for Ca (Y = B, + B,X).

two-factor

factor practically coincide with the values of the prediction model, reinforcing the very important weight of this first factor for the construction of the Ca model (this first factor accounts for 47.7% of the variance while the second raises it an additional 18.6% of the calibration’s total Y variance). A similar discussion to the one developed previously by means of Fig. 5 for the assessed Ca model will lead to similar conclusions for Mg, although in this case it must be pointed out that by taking into account the first two factors, 97% of the variance of the X variables but only 9.2% of the variance in Y (out of the 87.2% considered with four factors) is taken into account in the calibration process. A more precise vision of the importance of the original variables in the Mg multivariate model can be obtained by analysing the plots of the B, coefficients, taking the four model factors into account, Fig. 7. From this it can be seen that although all the variables contribute to the model shown, the variables at the end of the spectrum are of more importance. The absence of outliers, objects and variables in the calibration process was confirmed by means of influence plots in which the residual calibration variance of objects and variables are plotted versus their leverage values [22], Fig. 8. From this figure it can be seen that object 28 has a considerable influence on the model established with two factors for Ca, even though it is not far from

1

1

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limitations of the joint test for slope and intercept in these cases [22], the regression parameters of both straight lines do not show statistically significant differences from the theoretical values at a 0.05 level of significance, showing that the accuracy of the method developed is comparable to the standard AAS method.

Systems 24 (1994) 55-63

Acknowledgements The economic support from the Spanish Ministry of Education and Science (DGICyT project BP90-0453) is acknowledged.

References 4. Conclusions The present application shows the possibilities of automating chemical analysis by means of a SIA system. This system has the generic advantages of flow systems: speed of analysis, low consumption of samples and reagents, high level of automation and the possibility of increasing this level. It also has the specific advantages of sequential injection, such as simplicity of manifold at the expense of versatility of components, which is not aimed at in specific methodologies such as the one developed. The range of concentrations which can be determined is wide, and includes the content of these ions in most natural samples, due to being able to choose a measurement point at which the sample has dispersed sufficiently. This makes analysis easier because the samples do not need to be previously diluted. Diode array detection makes the system more expensive, but enables a multivariate calibration to be drawn up which incorporates the interferences in the constructed model without them affecting the accuracy of the results. Precision is, however, affected in this application, especially in the case of calcium, probably due to the high absorbance of the reagent used in the wavelengths worked with. It may be assumed that the results would be better if a less absorbing reagent were to be used. Likewise it can be assumed that the system described would be easy to use in the spectrophotometric determination of many other analytes for which conventional methods of analysis have been described. With an analysis speed of 20-30 samples per hour, the method developed here is, with respect to this performance characteristic, certainly superior to the standard AAS method.

[l] M.A.H. Franson, American Public Health Association, American Water Works Association, Water Polution Control Federation (Editors), Standard Methods for the Examination of Water and Was&water, Diaz de Santos, Madrid, 17th edn., 1992 (in Spanish). [2] R.J. Foster and D. Diamond, Non linear calibration of ion-selective electrode arrays for flow injection analysis, Analytical Chemistyv, 64 (1992) 1721-1728. [3] H. Wada, T. Ozawa, G. Nakagawa, Y. Asano and S. Ito,

Preparation and examination of calcium ion-selective electrodes for flow analysis, Analytica Chimica Acta, 211 (1988) 213-221. [4] W.D. Basson and J.F. van Staden, Simultaneous determi-

nation of sodium, potassium, magnesium and calcium in surface, ground and domestic water by flow injection analysis, Fresenius’ Zeitschrift fiir Analytische Chemie, 302 (1980) 370-374. [51 Z. Fang, J.M. Harris, J. Ruzicka and E.H. Hansen, Simultaneous flame photometric determination of lithium, sodium, potassium and calcium by flow injection analysis with gradient scanning standard addition, Analytical Chemistry., 7 (1985) 1457-1461. [6] F. Caiiete, A. Rios, M.D. Luque de Castro and M. Valdrcel, Determination of analytical parameters in drinking water by flow injection analysis. I. Simultaneous determination of pH, alkalinity, total ionic concentration, Analyst, 112 (1987) 263-268. [7] Y. Yuan. Y. Wang and K. Qu. - Flow iniection analvsis micellar solubilization spectrophotometry. II. Simultaneous spectrophotometric determination of calcium and magnesium with orange-cetyl-trimethylammonium bromide system, Fenvi Huaxue, 16 (1988) 546-551. [81Y. Yuan, Simultaneous spectrophotometric determination of calcium and magnesium with chlorophosphonazo III by flow injection analysis, Analytica Chimicu Acta, 212 (1988) 291-295.

[91 M. Blanco, J. Coello, J. Geni,

H. Iturriaga and S. Maspoch, Use of diode array detectors for the simultaneous spectrophotometric determination of calcium and magnesium by flow injection, Analyrica Chimicu Acta, 224 (1989) 23-30. DOI F. Caiiete, A. Rios, M.D. Luque de Castro and M. Valclrcel, Determination of analytical parameters in drinking water by flow injection analysis. II. Simultaneous determination of calcium and magnesium, Analyst, 112 (1987) 267-272.

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titrations in unsegmented flow systems based on variable flow-rate patterns. Part 2. Complexometric and redox titrations, Analytica Chimica Acta, 261 (1992) 495-503. [14] J. Ruzicka, G.D. Marshall and G.D. Christian, Variable flow rates and sinusoidal flow pump for flow injection analysis, Analytical Chemistry, 62 (1990) 1861-1866. [15] T. Giibeli, G.D. Christian and J. Ruzicka, Fundamentals of sinusoidal flow sequential injection spectrophotometry, Analytical Chemistry, 63 (1991) 2407-2413. [16] National Research Council, Canada, Division of Chemistry, Marine Analytical Chemistry, Standards Program, Ottawa, Canada KlA 0R6.

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[17] STATGRAPHICS, Statistical graphics system by Statistical Graphics Corporation, STSC, Inc. USA, 1986, 22-2. [18] UNSCRAMBLER II, version 4.0, Software for Multivariate data analysis applying PCA, PCR and PLS, CAM0 A/S, Nonvay. [19] R. Boque, F.X. Rius and D.L. Massart, Straight line calibration. Something more than slopes, intercepts and correlation coefficients, Journal of Chemical Education, 11 (1994). [20] D.L Massart, B.G.M. Vandengiste, S.N. Deming, Y. Michotte and L. Kaufman, Chemometrics: a Textbook, Elsevier, Amsterdam, 1988. [21] H. Martens and T. Nms, Multiuariate Calibration, Wiley, Chichester, 1989. [22] C. Hartmann, J. Smeyers-Verbeke and D.L Massart, Problems in method-comparison studies, Analusis, 23 (1993) 125.