Independent component analysis algorithms for spectral decomposition in UV/VIS analysis of metal- containing mixtures including multimineral food supplements and platinum concentrates Yulia B. Monakhova, * Svetlana S. Kolesnikova and Svetlana P. Mushtakova Various independent component analysis (ICA) algorithms (MILCA, JADE, SIMPLISMA, RADICAL) are applied for simultaneous spectroscopic determination of two groups of transition metals: Co(II)–Fe(III)– Cu(II)–Zn(II)–Ni(II) and Pt(IV)–Pd(II)–Ir(IV)–Rh(III)–Ru(III)) in complex mixtures. The analysis is based on the decomposition of spectra of multicomponent mixtures in the UV-VIS region based on the natural absorbance of metal salts, or, when a better sensitivity is desirable, based on the absorbance of their complexes with 4-(2-pyridylazo)resorcinol (PAR) and ethylenediaminetetraacetic acid (EDTA). Good quality spectral resolution of up to seven-component mixtures was achieved (correlation coefficients between resolved and experimental spectra are not less than 0.90). In general, the relative errors in the recovered concentrations are at levels of only several percent. While being superior to other ICA algorithms, MILCA is comparable or even outperforms other classical chemometric methods for quantitative analysis that were used for comparison purposes (Partial Least Squares (PLS), Principal Component Regression (PCR), Alternating Least Squares (ALS)). Simultaneous quantitative analysis is possible for mixtures containing up to five metals in the broad concentration ranges even when individual spectra show 99% overlap. A small excess of derivatization reagent (till threefold excess to the sum of metal concentrations) is optimal to obtain good quantitative results. The proposed method was used for analysis of authentic samples (multimineral supplements and platinum concentrates). The resolved ICA concentrations match well with the labelled amounts and the results of other chemometric methods (ALS, PLS). ICA decomposition considerably improves the application range of spectroscopy for metal quantification in mixtures. Introduction Metals are one of the main classes of inorganic substances, and a proper control of their content is required in metallurgy, environmental control, medicine and pharmaceutical industry. To date, a wide range of classical methods exist for the quali- tative and quantitative determination of metals in different matrices. 1,2 Among the main drawbacks of existing methods are complexity, necessity of separation steps, lack of sensitivity and high costs. Another difficulty is the wide range of metal-con- taining matrices (alloys, food stuff, supplements and environ- mental samples), which requires continuous development of a proper analytical methodology in each particular case. In connection with this, a lot of efforts should be aimed at searching and developing new sensitive methods that would be able to simultaneously determine several metals in a single analytical run. With the appearance of chemometric methods, researchers are increasingly applying different mathematical algorithms to metal analysis to overcome the above mentioned problems of classical methods (e.g., low specicity and labo- rious sample preparation) (see, for example, ref. 3–6). One of the most common methods for determination of metals is atomic spectroscopy (regardless of whether chemo- metric algorithms are used for modelling of experimental data or not). 4–11 A broad range of multivariate methods could be used either to directly quantify several metals or to interpret results of classical methods. For example, the combination of atomic emission spectroscopy with parallel factor analysis (PARAFAC), analysis of variance (ANOVA) or principal component analysis (PCA) is extremely helpful to increase efficiency of metal anal- ysis. 4–6 Atomic absorption spectroscopy in combination with mixed-level orthogonal array design (OAD), factor analysis (FA) and cluster analysis (CA) was used to determine several transition metals (Cd, Cu, Fe, Ni, Pb, V and Zn) in atmospheric ne parti- cles. 7 Also, CA, PCA, FA and discriminant analysis (DA) were successfully applied to the data of atomic absorption spectrom- etry for the examination of water quality in water reservoirs. 8 Department of Chemistry, Saratov State University, Astrakhanskaya str. 83, Saratov, 410012 Russia. E-mail: [email protected]Cite this: Anal. Methods, 2013, 5, 2761 Received 14th January 2013 Accepted 27th March 2013 DOI: 10.1039/c3ay40082d www.rsc.org/methods This journal is ª The Royal Society of Chemistry 2013 Anal. Methods, 2013, 5, 2761–2772 | 2761 Analytical Methods PAPER Published on 28 March 2013. Downloaded by University of Regina on 28/09/2013 07:23:51. View Article Online View Journal | View Issue
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AnalyticalMethods
PAPER
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Department of Chemistry, Saratov State Un
410012 Russia. E-mail: yul-monakhova@ma
Cite this: Anal. Methods, 2013, 5, 2761
Received 14th January 2013Accepted 27th March 2013
DOI: 10.1039/c3ay40082d
www.rsc.org/methods
This journal is ª The Royal Society of
Independent component analysis algorithms forspectral decomposition in UV/VIS analysis of metal-containing mixtures including multimineral foodsupplements and platinum concentrates
Yulia B. Monakhova,* Svetlana S. Kolesnikova and Svetlana P. Mushtakova
Various independent component analysis (ICA) algorithms (MILCA, JADE, SIMPLISMA, RADICAL) are
applied for simultaneous spectroscopic determination of two groups of transition metals: Co(II)–Fe(III)–
Cu(II)–Zn(II)–Ni(II) and Pt(IV)–Pd(II)–Ir(IV)–Rh(III)–Ru(III)) in complex mixtures. The analysis is based on the
decomposition of spectra of multicomponent mixtures in the UV-VIS region based on the natural
absorbance of metal salts, or, when a better sensitivity is desirable, based on the absorbance of their
complexes with 4-(2-pyridylazo)resorcinol (PAR) and ethylenediaminetetraacetic acid (EDTA). Good
quality spectral resolution of up to seven-component mixtures was achieved (correlation coefficients
between resolved and experimental spectra are not less than 0.90). In general, the relative errors in the
recovered concentrations are at levels of only several percent. While being superior to other ICA
algorithms, MILCA is comparable or even outperforms other classical chemometric methods for
quantitative analysis that were used for comparison purposes (Partial Least Squares (PLS), Principal
Component Regression (PCR), Alternating Least Squares (ALS)). Simultaneous quantitative analysis is
possible for mixtures containing up to five metals in the broad concentration ranges even when
individual spectra show 99% overlap. A small excess of derivatization reagent (till threefold excess to
the sum of metal concentrations) is optimal to obtain good quantitative results. The proposed method
was used for analysis of authentic samples (multimineral supplements and platinum concentrates). The
resolved ICA concentrations match well with the labelled amounts and the results of other chemometric
methods (ALS, PLS). ICA decomposition considerably improves the application range of spectroscopy for
metal quantification in mixtures.
Introduction
Metals are one of the main classes of inorganic substances, anda proper control of their content is required in metallurgy,environmental control, medicine and pharmaceutical industry.To date, a wide range of classical methods exist for the quali-tative and quantitative determination of metals in differentmatrices.1,2 Among the main drawbacks of existing methods arecomplexity, necessity of separation steps, lack of sensitivity andhigh costs. Another difficulty is the wide range of metal-con-taining matrices (alloys, food stuff, supplements and environ-mental samples), which requires continuous development of aproper analytical methodology in each particular case. Inconnection with this, a lot of efforts should be aimed atsearching and developing new sensitive methods that would beable to simultaneously determine several metals in a singleanalytical run. With the appearance of chemometric methods,
iversity, Astrakhanskaya str. 83, Saratov,
il.ru
Chemistry 2013
researchers are increasingly applying different mathematicalalgorithms to metal analysis to overcome the above mentionedproblems of classical methods (e.g., low specicity and labo-rious sample preparation) (see, for example, ref. 3–6).
One of the most common methods for determination ofmetals is atomic spectroscopy (regardless of whether chemo-metric algorithms are used for modelling of experimental data ornot).4–11 A broad range of multivariate methods could be usedeither to directly quantify several metals or to interpret results ofclassical methods. For example, the combination of atomicemission spectroscopy with parallel factor analysis (PARAFAC),analysis of variance (ANOVA) or principal component analysis(PCA) is extremely helpful to increase efficiency of metal anal-ysis.4–6 Atomic absorption spectroscopy in combination withmixed-level orthogonal array design (OAD), factor analysis (FA)and cluster analysis (CA) was used to determine several transitionmetals (Cd, Cu, Fe, Ni, Pb, V and Zn) in atmospheric ne parti-cles.7 Also, CA, PCA, FA and discriminant analysis (DA) weresuccessfully applied to the data of atomic absorption spectrom-etry for the examination of water quality in water reservoirs.8
Different types of molecular spectroscopy (X-ray uores-cence,12 luminescence and uorescence,13,14 UV-VIS spectros-copy,15–17 Raman spectroscopy18,19) are also widely used ininorganic analysis in combination with multivariate methods.Mass spectrometry is a good modern alternative for identica-tion and quantication of different metals, where chemometricmethods are used quite oen.4,6,20–23
Electrochemical methods could be also used.24,25 Forexample, the problem of overlapping peaks in voltammetry wassolved in ref. 24. The following strategy was proposed: rst,initial estimates for individual signals are obtained using PCA;then evolving factor analysis (EFA) or simple-to-use interactiveself-modeling mixture analysis (SIMPLISMA) is applied toextract preliminary information of voltammetric proles for allmetals and, nally, multivariate curve resolution-alternatingleast squares (MCR-ALS) is used for simultaneous quantica-tion of Cd(II), In(III), Pb(II) and Tl(I). The combination of threechemometric algorithms signicantly reduces the error ofquantitative analysis.
Our literature survey has shown that chemometric algo-rithms are promising methods to improve the quality of metalanalysis. However, in most of the above mentioned studies,instead of one particular method, their combination (3–4algorithms) or complex preprocessing should be used.4–8,24,26
This results in increasing time of analysis, and, in addition, mayintroduce additional error in quantitative determination.Therefore, despite a wide range of chemometric methods to beused, it still would be desirable to nd more efficient andgenerally applicable algorithms.
A good alternative can be found in a new group ofapproaches, generally termed “blind source separation” (BSS)with its most developed branch known as “independentcomponent analysis” (ICA).27,28 These methods are focused toseek a solution to the “black mixture” problem by resorting toan abstract mixture model as superposition of unknowncomponents with no assumptions about their molecularstructure or type of spectra. Notably, ICA decomposition algo-rithms do not demand a training dataset and specializedmethods for calibration or initial estimates. Researchersreconstruct matrices S (individual spectra) and A (concentra-tions) given only the observed experimental matrix X.27,28
ICA techniques were extensively used for spectroscopicanalysis of organic compounds in different matrices.29–38
However, to the best of our knowledge, there is no researchdealing with the application of ICA algorithms to the analysis ofmetal-containing samples. In this paper we used a cheap andeasy UV-VIS spectroscopic method to generate experimentalsignals of multicomponent metal mixtures. Simultaneousdetermination of metal cations in complicated mixtures in onestage using classical UV-VIS spectroscopy is hampered due tohighly overlapping spectra of metals or their complexes, whichprevent their calibration and most need a preliminarypretreatment, addition of suitable masking agents or extractionstep, which make the operation laborious. Therefore, we havehypothesized that ICA methods could overcome these problemsalso in the case of metal analysis. Furthermore, currently ICA isjust entering this area of practical applications and, therefore,
2762 | Anal. Methods, 2013, 5, 2761–2772
largely unexplored remains also the inuence of various factorssuch as concentration range, number of components and extentof spectral overlap on ICA decomposition results.
The aim of this article is to examine the applicability of ICAalgorithms to determination of metals using UV-VIS spectros-copy (which is sensitive and accurate but traditionally has a lackof specicity regarding metal analysis) and, thereby, present ananalytical method to simultaneously quantify them in complexmixtures. We also compare ICA results with those obtained byother chemometric techniques such as partial least squaresregression (PLS), principal component regression (PCR) andMCR-ALS.
ExperimentalReagents and equipment
The UV-VIS spectra (190–1100 nm) were recorded at 1 nmresolution on a SHIMADZU-1800 spectrometer (Shimadzu,Tokyo, Japan) with the cells having pathlengths of 1.00 cm.
All materials and solvents used were of analytical-reagentgrade. We used 4-(2-pyridylazo)resorcinol (PAR) and ethyl-enediaminetetraacetic acid (EDTA) as reagents, and metalsolutions Fe(III), Co(II), Zn(II), Cu(II), Ni(II), Ca(II), Mg(II), Rh(III),Ru(III), Pd(II), Pt(IV) were prepared from their respective chlo-rides purchased from Sigma-Aldrich (Buchs, Switzerland). Stocksolution of Ir(IV) was prepared from H2IrCl6. In our work we alsoused certied samples of platinum concentrates (PC-1, PC-2,PC-3, PC-3(1), PC-5) (Governmental Institute of Ore Standards“Sibzvetmetproect”, Krasnojrsk, Russia). Multivitamin supple-ments “Sana-Sol” (Krueger GmbH and Co., Bergisch Gladbach,Germany), “Complivit” (OAO Pharmstandard-Ufavita, Ufa,Russia), “Elevit Prenatal” (F. Hoffman-La Roche, Basel, Swit-zerland) and “Alphavit” (ZAO Akvilon, Moscow, Russia) werebought in local drug stores and pharmacies in Saratov, Russia.
Model mixtures of metals
All standard stock solutions were standardized according togenerally accepted procedures. For the solution preparation,PAR, EDTA and metal salts (Ni(II), Fe(III), Co(II), Zn(II), Cu(II),Ca(II), Mg(II)) were carefully weighted and dissolved in bidis-tilled water. Working solutions of PAR–metal complexes wereprepared by mixing certain aliquots of the reagent and metal(s)with 1 mL of buffer (pH 6.2; 5.15 mL 1 M CH3COOH, 5 mL of1 M NaOH in 50 mL volumetric asks) and bidistilled water in25 mL volumetric asks. EDTA–metal complexes were preparedby mixing aliquots of EDTA and metal(s) stock solution with5 mL ammonia buffer (pH 10; 6 g NH4Cl and 57 mL of NH4OHsolution (0.880 g mL�1) in 500 mL of bidistilled water) in 25 mLvolumetric asks.
Standard solutions of Pd(II) and Pt(IV) salts at a nalconcentration of 1.00 mg mL�1 were prepared by weighting of25 mg of the substance and dissolving it in nitric acid. Solutionsof Ir(IV), Ru(III) and Rh(III) (each of 1.00 mg mL�1) were preparedby weighing the salts, dissolved and completed to volume withhydrochloric acid (1 M). Model mixtures of platinum metalswere made by dissolving appropriate aliquots in hydrochloric
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acid (1 M). Further, throughout the text we omit metal chargesfor simplicity.
Sample preparation of authentic products: platinumconcentrates and multimineral supplements
For analysis of platinum concentrates we utilized the followingscheme. 0.25 g of a sample wasmixed with 3mL of concentratednitric acid and 2 mL of bidistilled water. The resulting mixturewas then heated for 20 minutes. Aer that 3 mL of bidistilledwater was added, and the sediment was ltered. The lters weredried, burned, and the residues placed in a corundum cruciblewith the addition of 0.5 g Na2O2. The crucible was then placedin a cold muffle furnace and the temperature was raised grad-ually to 600 �C. The resulting ash was transferred to a 25 mLask and dissolved in HCl : H2O solution (1 : 1). The solutionswere then used for direct measurements. Additionally, ltrateswere analyzed separately for transition metals (Cu, Ni, Fe) withPAR as described above.
For preparation of multimineral supplements, 20 tabletswere nely powdered and then 100–300 mg were mixedsequentially with 5 mL of buffer (pH 10) and bidistilled water in25 mL volumetric asks. Then solutions for analysis wereprepared in the same way as for model EDTA–metal mixtures.
All experiments were done in triplicate; the relative error inoptical density measurements was 3%. The tables report meanvalues together with relative error or condence interval calcu-lated with a probability p ¼ 0.95.
Chemometrics
The ICA algorithms used have MATLAB v. 7.0 interfaces (TheMath Works, Natick, MA, USA) and are available for free at thereferenced websites: MILCA,39 SIMPLISMA,40 JADE41 andRADICAL.42 For MCR-ALS we applied the PLS-Toolbox v. 5.2(Eigenvector Research, Wenatchee, WA, USA). A practical userguide for MCR-ALS can be found in ref. 43. In our research weused spectra resolved by MILCA as initial estimates for MCR-ALS. During the ALS optimization, we applied non-negativityconstraints to model the shapes of both spectra and concen-tration proles. Computational time in all cases was below 15minutes per system (mixture) including preparation of thesamples.
To characterize similarity between experimental and calcu-lated matrixes we apply the Amari index:36
Perr ¼ 1
2N
XNi; j¼1
��pij��maxkjpikj þ
��pij��maxk
��pkj��!
� 1
where pij ¼ (A�1A)ij.The Amari index iterates to zero, when the recovered
concentrations differ from the true ones only in scaling andpermutation of components, and it increases as the quality ofdecomposition becomes poor. Thus, small values of the Amariindex are desirable. In practice, we nd that good decomposi-tion quality roughly corresponds to Amari indices P < 0.05,whereas P > 0.2 generally characterizes unacceptably poorperformance.
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To assess the similarities between the normalized resolvedspectra and the original experimental (pure) spectra, we use thecorrelation coefficient (R) scaled to the [0,1] range (MATLABimplementation). Throughout the manuscript we used not-squared R values, so that the correlation coefficient of 0.99means 99% of spectral similarity.
For partial least squares (PLS) and principal componentregression (PCR) calculations we used Unscrambler v 10.0.1(Camo Soware AS, Oslo, Norway). To construct the models,training spectral sets were created (147 model mixtures for therst group of metals (Ni–Fe–Cu–Co–Zn) and 48 mixtures ofplatinummetals (Ir–Rh–Ru–Pt–Pd)). The remaining 10 mixturesfor both sets were used as independent sets to test the cali-bration (validation sets). The sample grouping was done byrandomisation in such a way that low, medium and highconcentrations were evenly distributed between the two setswith the most extreme observations in the calibration set. Theoptimal number of factors, indicated by the lowest predictionerror, was selected for all PLS and PCR models.
Results and discussionModel mixtures of metals
Spectroscopic simultaneous determination of metals with theaim of chemometrics could be feasible using either absorbanceof metal salts or their metal–ligand complexes. Therefore, wesuggest three different cases where multivariate techniques canbe used for metal analysis:
1. usage of the self-absorption of metals (which would be themost desirable case as no additional sample preparation isrequired);
2. usage of the absorption of metal-derivatization reagentcomplexes with the same composition for all metals, when onlyone complex is formed;
3. usage of the formation of metal-derivatization reagentcomplexes with different compositions for different analytes,when more than one complex for some metals can be formed(depending on the nature of the metal and experimentalconditions).
The last two cases could be used for analysis of metals, whichdo not possess UV-VIS absorption properties. We leave outanother opportunity for metal determination based on kineticreaction proles44,45 outside the scope of the present work,because this procedure is denitely more time-consuming and,thus, eliminates the main advantage of chemometrics –
simplicity and speed of analysis.Multicomponent mixtures of platinum metals can be a good
example considering the rst and simplest group of oursystems. One common feature of this group of metals is highintensities of the absorption bands in the UV region, whichallows one to analyze small amounts of platinum metals.However, the absorption spectra of platinum metal saltsstrongly overlap in the 200–400 nm range (Fig. 1a). To assess theapplicability of ICA algorithms, several multicomponentmixtures containing platinummetals in different concentrationratios have been studied. As an example, Fig. 1b showsabsorption spectra of Pt and Pd that were decomposed by the
Fig. 1 (a) Experimental spectra of mixtures consisting of Pt and Pd: relative concentrations investigated are 1 : 1 (1), 2 : 1 (2), 1 : 4 (3), where 1 corresponds to 1.9 �10�5 M; (c) experimental spectra of complexes of EDTA (0.1 M) with Cu, Ni and Co: relative concentrations investigated are 5 : 10 : 10 (1), 3.3 : 2 : 15 (2), 10 : 10 : 3 (3),where 1 corresponds to 1.0� 10�3 M; (e) experimental spectra of complexes of PAR (6.0� 10�5 M) with Fe, Co and Zn: relative concentrations investigated are 1 : 1 : 1(1), 6 : 4 : 2 (2), 1 : 6 : 2 (3), 2 : 3 : 6 (4), 1 : 1 : 8 (5), where 1 corresponds to 1.0 � 10�6 M. Cell pathlength was set to 1 cm. (b), (d) and (f) normalized resolved spectra.Correlation coefficients for each resolved spectra with “ground truth” are given in brackets in all figures (method MILCA).
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MILCA algorithm. The relative error in absorption peak loca-tions is about 1 nm. Values of correlation coefficients (0.98 forPt and 0.95 for Pd) indicate that identication of components isperformed with a high level of condence.
To assess the possibility of chemometric methods for theanalysis of metals that form complexes of the same composition(independent of the nature of the metal and experimentalconditions), we studied several model mixtures consisting ofmetal–EDTA complexes in the UV and visible regions. Ourchoice of EDTA is explained by the fact that this reagent iswidely used in analytical practice because it forms stablecomplexes with more than 40 metals.46 We tested the applica-bility of the ICA algorithms for qualitative and quantitativeanalysis of multicomponent model mixtures containing up to 5metals. As an example, experimental spectra and the results of
2764 | Anal. Methods, 2013, 5, 2761–2772
qualitative analysis of the ternary Ni–Co–Cu system are shownin Fig. 1c and d. However, EDTA–metal complexes have smallmolar absorption coefficients in the visible range and ourmethod would have a lack of sensitivity when used for analysisof authentic samples. In the UV region (200–400 nm), molarabsorption coefficients of complexones are much higher, andthis, therefore, will improve the sensitivity of our method. Theonly disadvantage would be that the spectrum of underivatizedEDTA should be also resolved by ICA techniques, whichincreases the number of ICs to be found. We show this possi-bility later in the example of multimineral supplementsanalysis.
The third and most challenging opportunity in metal anal-ysis by the spectral decomposition is when several complexes ofdifferent compositions could be formed. We consider the
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complexation of PAR with transition metal ions.47 By this reac-tion, intensely colored and well-soluble 1 : 2 or 1 : 1 complexeswith high absorption coefficients are formed.47–49 However, thepH value has a drastic effect on the resolution, shape, intensityand stability of the spectra of PAR–metal complexes.47–49 Duringpreliminary experiments we concluded that pH 6.2 is optimumin our case, because at this pH a single form of PAR (HR�)dominates, complexes with all studied metals are formed (Fe,Cu, Co, Zn and Ni) and the absorption spectra of the solutionsare stable. As an example, the absorption spectra of a threecomponent metal solution (Co–Fe–Zn) aer addition of PARsolution are shown in Fig. 1e. It can be seen that the spectraoverlapped signicantly, and quantitative estimations cannotbe carried out successfully by conventional calibrationmethods. The spectra resolved by the MILCA algorithm and theexperimental spectra are almost identical, which was numeri-cally conrmed by the correlation coefficient values (Fig. 1e).The spectrum of underivatized PAR has to be also resolved.
The simultaneous determination of Hg(II), Pb(II), Zn(II) andCd(II) also with PAR as a reagent using PCR, PLS and iterativetarget transformation factor analysis (ITTFA) was recentlyproposed.29 Savitzky–Golay and Direct Orthogonal SignalCorrelation lters were used as preprocessing methods toincrease the accuracy of metal quantication. The method wasnot applied to the analysis of authentic samples although this isa crucial part of the validation of multivariate calibrationmodels. Furthermore, we have recently shown that for ICAmodeling of UV-VIS spectra smoothing is not necessary as itdoes not improve quantication results and, thus, we do notneed preprocessing of spectral data in our case.50
We have also noticed that at our working pH (6.2), on thecontrary to other metals, Zn forms two complexes with PAR witha concentration ratio of approximately 1 : 2 in solution.48 Inorder to examine the inuence of this observation on thedecomposition performance, we investigated several multi-component mixtures containing Zn. During spectral decompo-sition, the number of ICs was assumed to be equal either to thenumber of metals (plus the spectrum of PAR itself), or one IC
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more when two Zn complexes were considered. In the rst case,we resolved one “combined” spectrum for both Zn complexes astheir concentration ratio is constant at the given pH. In thesecond case, we extracted spectral proles and concentrationsfor both complexes. We found out that the relative errors ofquantication are comparable in both cases. However, for ourpurposes we decided to resolve one IC per metal as theminimum number of ICs is required for the spectral modelling.
To evaluate the linear behavior of each metal or metal–reagent complex, individual calibration curves were constructedusing the absorbance values at lmax vs. metal ion concentra-tions. The linear ranges are 3.8 � 10�6–6.0 � 10�4 M for Pt,1.9 � 10�5–3.0 � 10�4 M for Pd, 7.1 � 10�6–1.0 � 10�4 M for Ir,6.2 � 10�6–1.3 � 10�4 M for Rh, 1.3 � 10�5–2.5 � 10�4 M forRu; 2.0 � 10�5–1.0 � 10�3 M for Ca, 8.0 � 10�5–1.0 � 10�3 Mfor Mg, 5.0 � 10�5–5.0 � 10�4 M for Mn, 1.0 � 10�6–1.0 �10�4 M for Co, 2.0 � 10�5–6.0 � 10�4 M for Cu, 5.0 � 10�5–
1.0� 10�3 M for Zn (EDTA complexes); 2.2� 10�6–3.6� 10�5 Mfor Fe, 1.6 � 10�6–3.2 � 10�5 M for Ni, 1.2 � 10�6–3.0 � 10�5 Mfor Co, 6.0 � 10�7–6.0 � 10�5 M for Cu, and 8.0 � 10�7–3.0 �10�5 M for Zn (PAR complexes) (R2 > 0.99 in all cases).
Other reagents (except PAR and EDTA) have been used inmulticomponent metal analysis by applying chemometrictechniques. This includes Chrome Azurol S for determination ofFe(III) and Al(III),44,51 salicyluorone (SAF) for Al(III), Mn(II) andCo(II),52 morin for Fe(III), Al(III) and V(V)53 or ammonium pur-purate (murexide) for Co(II), Ni(II) and Cu(II).54 However, thenumber of analyzed metals is restricted to two–three ions in allcases. We believe that PAR and EDTA are better suited aschromotropic derivatization reagents in chemometric analysisbecause they react with many metal ions and, therefore, trulymulticomponent analysis (up to 5 metals) can be achieved. ICAis the rst technique so far that allows simultaneous quanti-cation of such a number of components.
Next, it is necessary to provide a comparison of different ICAalgorithms to choose the most appropriate one for metal anal-ysis. To do this, a large number of multicomponent mixtures(consisting of up to ve metals) have been analyzed by different
ICA techniques (MILCA, SIMPLISMA, JADE and RADICAL). Theresults of qualitative (correlation coefficient) and quantitative(Amari indices) analysis for some of them are present in Table 1.Although for simple two component mixtures all ICA methodsshowed comparable performance, it was found that the MILCAmethod is superior in more complex cases. Our resultconrmed previous investigation, where the MILCA methodwas proved as superior for spectroscopic analysis of organicsubstances in the UV-VIS region.35–38 For the MILCA method inall investigated cases, the correlation coefficients are not lessthan 0.9 and Amari indices showed good concentration recov-eries (Table 1). Thus, our results indicate that ICA as a spectraldecomposition technique is suitable for metal analysis in theform of pure salts or complexes even in cases when a consid-erable spectral overlap exists.
It is known that relative concentrations obtained by ICA canbe transferred in common concentration units, when a prioriinformation exists.37 This possibility was shown on the exampleof ve-component Zn–Co–Cu–Ni–Fe mixtures (PAR complexes).The concentrations were recovered using a known totalconcentration of metals in the mixtures and the relativeconcentrations resolved by ICA. Quantitative analysis is repor-ted in Table 2. The average recoveries were found to be 97% forFe(III), 98% for Co(II), 104% for Cu(II), 106% for Zn(II) and 88%for Ni(II) and in general varied between 60% and 120% (theseextreme values were observed only in single instances).
Table
2Quan
titative
analysisoffive-componen
tmixturesZn
(II)–Co(II)–Cu(II)–Ni(I
I)–Fe
(III)bytheMILCAalgorithm
(n¼
Mixture
Fe( II
I)Co(
II)
Cu(
II)
Add
edFo
und
Recovery[%
]Add
edFo
und
Recovery[%
]Add
edFo
und
11.0
0.90
�0.22
902.5
2.4�
0.1
960.50
0.50
�0.07
20.50
0.50
�0.12
100
1.8
1.9�
0.1
106
0.90
1.0�
0.1
30.50
0.50
�0.06
100
0.50
0.45
�0.13
902.3
2.5�
0.4
41.0
0.90
�0.15
901.0
1.1�
0.1
110
1.0
1.0�
0.1
50.50
0.50
�0.08
100
1.0
1.2�
0.2
120
0.50
0.50
�0.06
62.3
2.0�
0.4
870.50
0.42
�0.13
840.90
1.0�
0.1
71.0
1.1�
0.2
110
0.50
0.40
�0.14
800.50
0.50
�0.04
Average
9798
Inuences of different factors on decomposition performance
In the previous section we have shown that ICA algorithms aresuitable for the simultaneous analysis of metals in complexmixtures. The next step is the examination of different factorsthat may affect the results. The overall quality of experimentaldata has signicant inuence. It was previously found that theoptimum conditions for the best decomposition results are themedium speed of spectral acquisition and 1 nm spectral reso-lution for the Shimadzu UV1800.37 We have chosen theseexperimental conditions to evaluate other factors. All spectrawere evaluated by the MILCA method as the best choice forcurve resolution of metal spectra.
The rst important issue to be considered is the concentra-tion ratios of substances in multicomponent mixtures. For thispurpose, experimental absorption spectra of the Fe–Cu system(PAR complexes) were registered with different concentrationratios (ranging from 1 : 10 to 10 : 1). Calculated Amari indiceswere in the range of 0.04–0.13, which means that simultaneousquantication of these ions in binary mixtures is possible in theconcentration range of (2–20) � 10�6 M with a relative errorbelow 10%. The same conclusions were made for Pt–Pdmixtures: simultaneous quantitative analysis is possible in theconcentration range of (1.8–25)� 10�5 M even for mixtures withextreme concentration ratios. Thus, ICA can be efficientlyemployed in analysis of systems with varying concentrations ofmetals. A straightforward limitation would be that themeasured signal has to be above the instrumental noise.
Another characteristic is the degree of spectral overlap of thespectra of individual compounds to be analyzed. We have
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This journal is ª The Royal Society of Chemistry 2013
investigated this issue based on the decomposition results ofdifferent binary mixtures (Table 3). Obviously, in cases whenthere is little dependence between components (which usuallymeans little spectral overlap) the quantitative results are clearlybetter. But this does not imply that one could not analyze systemswith a strong spectral overlap as for example in the Co–Cu system(correlation coefficient is 0.95), for which an Amari index of 0.12was obtained. However, in case of Rh–Ir mixtures, the individualspectra show 99% overlap, which results in a sharp increase ofthe Amari index (Table 3). Therefore, in future experiments, thesum of concentrations for these two metals has been quantied.Notably, identication of metals in mixtures is performed withhigh probability in all cases we studied (R > 0.94). A satisfactoryresolution for this system was obtained regardless of the highoverlap of mixture signals (Table 3).
For PAR–metal complexes, the effect of the reagent concen-tration (excess/lack of the stoichiometric amount) was also testedfor binary Fe–Cu mixtures. It was found that, ranging from asmall lack of PAR (5 : 6) to its small excess (3 : 1), the relativeerror of spectroscopic determination does not exceed 15%. Incontrast to classical photometricmethods, where a comparativelyhigh derivatization reagent excess should be used, the bestdecomposition results could be obtained either using stoichio-metric ratios or, alternatively, taking the reagent in a little excess.The relative error of quantication clearly increases in the case ofa lack of reagent due to various stability constants of complexes.On the other hand, high concentrations of underivatized PARalso prevent accurate determination. Our data are in accordancewith the previous study, where only a slight excess of the reagentconcentration is required for chemometric analysis of complexeswith comparably large formation constants.53
Table 3 Influence of spectral overlap of individual spectra on the decompositionperformance (algorithm MILCA)
Mixture Metal
Correlationcoefficientbetweenexperimentalspectra
Correlationcoefficientbetweenexperimentaland resolvedspectra Amari index
This journal is ª The Royal Society of Chemistry 2013
Another parameter to be evaluated is the number ofcomponents. Obviously, the higher the number of componentsin the mixture, the lower performance of the ICA algorithms.36
We have found, however, that ICA techniques are able tosuccessfully recover up to seven ICs (for example, the spectrumof the derivatization reagent and six spectra of complexes), andthe Amari indices never exceed 0.15.
Comparison between ICA and classical multivariateregression techniques (PLS and PCR)
Multivariate regression methods (PLS, PCR) are recommendedsolutions to the problem of strong spectral overlapping.2,55–59
Therefore, we have chosen these well-established methods asalternative chemometric techniques to evaluate the perfor-mance of ICA based methods.
PLS and PCR models were constructed separately for the twogroups of studied metals Ir–Rh–Ru–Pt–Pd and Zn–Co–Cu–Ni–Fe. Parameters of the best-tting PLS and PCRmodels are listedin Table 4. The optimum spectral regions were selected formultivariate calibration models: 235–350 nm for platinummetals and 220–600 nm for the second studied group. For Ir andFe, PCR and PLS gave comparable results in terms of R2, RMSEvalues of calibration and validation though PLS required a lowernumber of signicant factors with respect to PCR (Table 4). Inall other cases, the PLS method is superior to PCR; this is a well-recognized characteristic of PLS with respect to PCR.60 PLSmodels with comparably high correlations were obtained for Fe,Rh and Ir (R2 > 0.90). The correlations for Co, Cu, Zn and Ru alsoappear to be adequate for a screening procedure (R2 are in therange of 0.85–0.89). Inadequate PLS models were obtained forNi, Pt and Pd, for which the best correlation coefficients are lessthan 0.80.
To verify the applicability of our constructed regressionmodels as well as to compare them with ICA techniques, inde-pendent test sets of mixtures (n¼ 10) were analyzed both by PLSand MILCA (Table 5). For Zn–Co–Cu–Ni–Fe mixtures bothmethods showed comparable performance though ICA isadditionally applicable for analysis of Ni. In the case of plat-inum metals, however, there are signicant differences in theperformance of themethods (Table 5). TheMILCAmethod givesgood results for simultaneous determination of all platinummetals except Ir and Rh (for which only the sum of concentra-tions could be obtained, see previous section). On the otherhand, PLS provides good quantitative results for these twometals, being, at the same time, unable to determine Pt and Pd(Table 5). For such a case, when all ve metals have to besimultaneously analyzed, the combination of two chemometricmethods (ICA and PLS) is required.
Analysis of authentic samples
The nal goal of any method development is its application foranalysis of authentic complex matrices. For this purpose, ICAwas applied to the analysis of platinum concentrates and mul-timineral supplements.
Platinum concentrates. First, we performed qualitative andquantitative spectroscopic analysis of platinum concentrates
PC-1, PC-2, PC-3, PC-31 and PC-5. Fig. 2a shows the absorptionspectra of platinum concentrates in the 230–330 nm region.Spectra of individual metals (Pt, Rh, Ir and Ru) were extractedand the values of correlation coefficients were not less than 0.95for all compounds (Fig. 2b). The concentrations of compoundsin platinum concentrates were obtained within 16% relativeerror with respect to the declared amounts (Table 6). Further-more, to conrm the ICA results, the spectra were analyzed byour developed PLS models (Table 6). The relative errors of PLSquantication were 13%, 14% and 16% for Ir, Ru and Rh
Table 5 Analysis of multicomponent mixtures by MILCA and PLS methods
a Average values are given in brackets. b No adequate PLS model wasconstructed. c Simultaneous determination of Ir and Rh.
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respectively. The results of the two methods are consistent witheach other.
Platinum metals are usually accompanied by other transi-tion metals in platinum concentrates and their analysis wouldbe an additional challenge for ICA techniques. Using oursample preparation, several metals can be additionally found inltrates aer treatment with nitric acid. Therefore, to determinePd, Cu, Ni and Fe we measured the absorption spectra ofltrates with the addition of PAR. DuringMILCA decompositionwe resolved the spectrum of PAR, its complexes with Ni, Cu, Fe,Pd and the “generalized” spectrum of other matrix componentsas on IC. The results of quantitative analysis are shown inTable 6.
Multimineral supplements. Multimineral supplementsusually contain also a bundle of other compounds such asvitamins in multivitamin/multimineral combination prepa-rations in addition to the metals of interest. Previously, weapplied ICA techniques to the determination of vitamins inmultivitamin preparations.37 We expand the applicability ofthe ICA technique to the analysis of the mineral content inthis matrix, which is important for labelling and controlpurposes. There are some papers that deal with determina-tion of selected metals in supplements, where simple samplepreparation is required (only addition of proper buffer solu-tion to powdered samples).61,62 We decided to follow thisexperimental strategy to avoid laborious steps such asmineralization, where signicant loss of the examinedcomponents could occur. In this case we used complexationwith EDTA as PAR does not react with some elements ofinterest (Ca and Mg).
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Fig. 3 (a) Experimental absorption spectrum of themultimineral supplement “Complivit” (1). Other lines show experimental spectra of “Complivit”with addedMg (2),Ca (3), Mn (4), Zn (5) and Cu (6). Metals were spiked at the concentration of 2.0 � 10�4 M in all cases. Cell pathlength was set to 1 cm. (b) Normalized resolved spectrawith the correlation coefficient values for each resolved compound are given in brackets in all figures (method MILCA).
Fig. 2 (a) Experimental absorption spectra of the investigated platinum concentrates. Cell pathlength was set to 1 cm; (b) normalized resolved spectra with thecorrelation coefficients values for each resolved compound are given in brackets in all figures (method MILCA).
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a The multimineral supplement does not contain the specied metal according to the label.
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For analysis, we recorded the absorption spectra of thesupplements without and with the addition of EDTA and stan-dard additions of the studied metal solutions in alkaline media(pH 10). As an example, Fig. 3a shows the experimental absorp-tion spectra of a multimineral supplement “Complivit” withoutandwith the addition of standard solutions ofmetals. Fig. 3b andTable 7 contain both quantitative and qualitative results ofanalysis of selected compounds. Obviously, absorption spectraare separated into independent components and concentrationratios coincide well with the labelled amounts. MCR-ALS wasapplied to the same spectra. The relative error of determinationby both methods is practically the same and in most cases doesnot exceed 10%. To make a nal conrmation, two investigatedsupplements were analyzed by X-ray uorescence spectroscopyfor Zn, Mn and Cu (Table 7).63 The results obtained by these twoindependent methods are consistent with each other.
Previously, the metal ions Co(II), Ni(II), Cu(II), Fe(III) and Cr(III)were analyzed by a spectrophotometric method with EDTA as achromogenic reagent and different chemometric methods(classical least squares (CLS), PCR, PLS, and articial neuralnetworks (ANN)).17 Although the proposed method gave goodquantitative results even for authentic industrial samples, itcannot be applied for mixtures with a lower metal content, dueto low extinction coefficients of EDTA–metal complexes in the370–760 nm range. In our paper we have expanded the scope ofquantitative UV-VIS analysis down to the small metal contentsfound in food supplements.
Conclusions
Different ICA techniques (MILCA, SIMPLISMA, JADE andRADICAL) have been extensively used to provide decomposition(curve resolution) of spectra of complex metal mixtures. Wehave also expanded the range of successful ICA applications inspectroscopic analysis as, to the best of our knowledge, therewas no research dealing with the application of ICA algorithmsto the analysis of such systems. Results of the qualitative and
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quantitative analysis of our systems denitely showed thepossibility and prospects of using such algorithms for analysisof authentic samples containing metals (platinum concentratesand multimineral supplements). Multivariate statistics opens awindow into exploring practically every metal-containingsample because the analysis can be performed using theabsorption properties of the metals themselves as well as oftheir complexes with organic reagents.
Our results show that ICA decomposition is comparable andin some cases outperforms specialized chemometric algorithms(PLS, PCR) and performs almost as good as MCR-ALS. Addi-tionally, we should emphasize that ICA methods do not requirea priori information (such as calibration sets for PLS and PCR orinitial estimates for ALS); the only inputs are experimentalspectral data for mixtures. However, the disadvantage is thatevery analyzed system requires a separate ICA treatment. This,of course, increases analysis time and requires a specialist fordata analysis. We do hope that such chemometric methods willbe introduced in standard soware packages for spectrometersand the analysis can be even done on-line on the analyzer. Thefact that the soware algorithms described in the paper arefreely available in the internet and do not need license costs forimplementation should facilitate efforts in this direction.
Our methodology has clear disadvantages in comparisonwith more sensitive ICP-MS, where a broader range of metalscould be quantied with no need for multivariate methods.However, UV-VIS spectroscopy is still widely preferred due to itssimplicity, rapidity and low analysis costs. In such cases ICA is auseful addition to spectroscopic measurements to providequalitative and quantitative analysis of a bundle of metals in asingle analytical run. The main advantage of UV-VIS spectros-copy combined with multivariate techniques and, in particular,ICA is the speed of the method, as the separation step could beavoided.
In conclusion, the proposed method is a promising analyt-ical tool to improve the productivity and reliability of determi-nation of metals in food control and industrial laboratories.
This journal is ª The Royal Society of Chemistry 2013
This methodology is especially applicable for developingcountries that cannot afford expensive analytical instrumentssuch as ICP-MS.
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