- Email: [email protected]
Application of silver nanoparticles and principal component-artificial neural network models for simultaneous determination of levodopa and benserazide hydrochloride by a kinetic spectrophotometric method
Spectrochimica Acta Part A 82 (2011) 25–30
Contents lists available at ScienceDirect
Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy journal homepage: www.elsevier.com/locate/saa
Application of silver nanoparticles and principal component-artificial neural network models for simultaneous determination of levodopa and benserazide hydrochloride by a kinetic spectrophotometric method J. Tashkhourian a,∗ , M.R. Hormozi-Nezhad b,c,∗ , J. Khodaveisi d a
Department of Chemistry, College of Sciences, Shiraz University, Shiraz 71454, Iran Department of Chemistry, Sharif University of Technology, Tehran 11155-9516, Iran c Institute for Nanoscience and Nanotechnology, Sharif University of Technology, Tehran, Iran d Department of Chemistry, Faculty of Sciences, Persian Gulf University, Bushehr 75169, Iran b
a r t i c l e
i n f o
Article history: Received 6 April 2011 Received in revised form 29 May 2011 Accepted 13 June 2011 Keywords: Kinetic methods Levodopa Benserazide hydrochloride Silver nanoparticles Artificial neural network
a b s t r a c t A multicomponent analysis method based on principal component analysis-artificial neural network model (PC-ANN) is proposed for the simultaneous determination of levodopa (LD) and benserazide hydrochloride (BH). The method is based on the reaction of levodopa and benserazide hydrochloride with silver nitrate as an oxidizing agent in the presence of PVP and formation of silver nanoparticles. The reaction monitored at analytical wavelength 440 nm related to surface plasmon resonance band of silver nanoparticles. Differences in the kinetic behavior of the levodopa and benserazide hydrochloride were exploited by using principal component analysis, an artificial neural network (PC-ANN) to resolve concentration of analytes in their mixture. After reducing the number of kinetic data using principal component analysis, an artificial neural network consisting of three layers of nodes was trained by applying a back-propagation learning rule. The optimized ANN allows the simultaneous determination of analytes in mixtures with relative standard errors of prediction in the region of 4.5 and 6.3 for levodopa and benserazide hydrochloride respectively. The results show that this method is an efficient method for prediction of these analytes. © 2011 Elsevier B.V. All rights reserved.
1. Introduction Levodopa [(−)3-(3,4-dihydroxylphenyl)-l-alanine] (LD) is an important neurotransmitter, which has been used for the treatment of neural disorders such as Parkinson’s disease. The cause of this disease is the significant depletion of dopamine [1]. After its oral administration, levodopa is absorbed through the bowel and converted into dopamine by decarboxylase. Hence, levodopa can relax the symptoms of Parkinson’s disease and also decrease muscular rigidity, and tremor. Unfortunately, conversion of levodopa into dopamine in the rest of the body can cause unwanted side effects such as nausea and palpitations. Benserazide hydrochloride (BH) is used in combination with the levodopa to prevent this happening. It blocks the conversion of levodopa to dopamine in the body and so prevents these side effects [2,3]. With due attention to this instance simultaneous determination of this drug is very impor-
∗ Corresponding authors at: Department of Chemistry, College of Sciences, Shiraz University, Shiraz 71454, Iran. Tel.: +98 711 2284822; fax: +98 711 2286008. E-mail addresses: [email protected] (J. Tashkhourian), [email protected] (M.R. Hormozi-Nezhad). 1386-1425/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.saa.2011.06.014
tant and several methods have also been reported for simultaneous determination of levodopa and benserazide hydrochloride [4,5]. The kinetic methodology based on the difference of reaction rates, especially in cases where the analytes react with a common reagent, is an effective way to analyze several analytes simultaneously [6], and it has made a great improvement by using the chemometric procedures [7–9]. Chemometric methods based on factor analysis and artificial intelligence, including principal component regression (PCR), partial least squares (PLS) and artificial neural networks (ANNs), have found increasing applications for multicomponent kinetic determination [10–18]. These methods make it possible to eliminate or reduce the effects of the analyte–analyte interaction, the synergistic effect (non-additively of reaction rates), the multi-step process and any other unknown nonlinearity. The use of neural networks in chemometrics has increased during the last decades [19–21]. Artificial neural network (ANN) is nonlinear computational tools suitable to a great host of practical application due to their flexibility and adaptability. Their ability to handle non-linearities makes them a valuable contribution to the discipline. It has been demonstrated that it is possible to obtain excellent results in multivariate calibration problems using ANN [22,23] and also application of ANN model with data
26
J. Tashkhourian et al. / Spectrochimica Acta Part A 82 (2011) 25–30
Scheme 1. Reduction process of the silver salt by the levodopa.
pretreatment method, such as normalization [24] and compressing data into scores with the use of principal component analysisartificial neural network (PC-ANN) as input data to quantify mixtures in different kinetic situations has been reported [25,26]. Metal nanomaterials such as silver nanoparticles (AgNPs) have been found wide applications for analytical purposes due to the unique optical and electric properties. Owing to the collective oscillations of conducting electrons under the excitation of a light beam [27] known as localized surface plasmon resonance (LSPR) which depends on the size, shape and dielectric environment of nanoparticles, noble metal nanoparticles exhibit a strong UV–Vis absorption band. For this reason AgNPs have bright color in the visible spectral range concerning their SPR absorption and light scattering. Therefore, recently there has been high interest in applications of metal nanoparticles [28–34] and among them AgNPs in the field of surface plasmon resonance spectroscopy [35–38] and light scattering detection [39]. In this work, a new method for simultaneous determination of the levodopa and benserazide hydrochloride proposed with the aid of the chemometric approach of principal component-artificial neural network (PC-ANN) models. The method is based on the reaction of levodopa and benserazide hydrochloride with the oxidizing agent (silver nitrate) in the presence of polyvinylpyrrolidone (PVP) and formation of silver nanoparticles in a slightly basic medium and spectrophotometrically monitoring the changes of SPR band at maximum wavelength of silver nanoparticles (440 nm) vs. time (Scheme 1). The aim of this study as well as simultaneous determination of the levodopa and benserazide hydrochloride in their mixture is the representation ability of Ag nanoparticles for determination of important analyte. 2. Experimental 2.1. Apparatus and software The UV–Vis absorbance spectra were recorded on a PerkinElmer (Lambda25) spectrophotometer and 1.0 cm glass cell was used. Measurements of pH were made with a Denver Instrument Model 270 pH meter equipped with a Metrohm glass electrode. Computational work was performed with Winnn32 software as a multilayer feed forward ANN with the back propagation of error learning algorithm. The principal component analysis (PCA) process was performed by MATLAB (Mathworks, version 6.1) programs, and the scores of the PCA running were fed into ANN program as inputs. 2.2. Chemical and reagents All chemicals used were of analytical reagent grade and the solutions were prepared with deionized water. Levodopa (LD), benserazide hydrochloride (BH), silver nitrate and polyvinylpyrrolidone
(PVP) by average mol. wt. 10,000 were purchased from Merck and Fluka. All other common laboratory chemicals were of the best grade available and were used without further purification. All solutions were used within 1 h after preparation, and the experiments were performed at the ambient temperature (25 ± 2 ◦ C). Stock solutions of AgNO3 (0.01 M) were prepared by dissolving 0.085 g AgNO3 in deionized water and diluting to 50 mL. A stock solution of polyvinylpyrrolidone (PVP) (0.4 g/L) was prepared daily by dissolving 0.01 g of PVP (Merck) in water and diluting to 25 mL. Fresh 0.05 M solution of levodopa and benserazide hydrochloride were prepared daily by dissolving the reagent in deionized water. 2.3. General procedure In 5 mL volumetric flasks, 0.7 mL of PVP 0.4 (g/L), 1 mL of NaOH 0.001 mol/L, different concentrations of the analyte and 1 mL of AgNO3 0.01 M added to obtain the reasonable solution, then it was mixed slowly and transferred into a 1 cm spectrophotometric cell to record the absorbance vs. time. It should be noted that the order of the addition of the reagents is very important. The absorbance was measured at 440 nm that is max of silver nanoparticles surface plasmon resonance peak at this condition, against a reagent blank. 3. Result and discussion 3.1. Kinetic study of reaction between LD and BH and Ag+ ion Nanoparticles made of silver and gold have been the focus of research for many decades due to their intriguing optical properties. The systems in this study consist of an aqueous AgNO3 solution that includes polyvinylpyrrolidone (PVP), as stabilizer, at an alkaline medium. Levodopa and benserazide hydrochloride act as effective reducing agents for reduction of silver metal salt (Ag+ ) to the AgNPs without added any seeds. In the absence of reducing agents, there is no absorption peak in visible region (380–700 nm). Upon addition of analyte which act as reducing agent silver ions reduced to silver nanoparticles, then the absorbance characteristic to the plasmon of the Ag-NPs is observed. Fig. 1 shows the absorption spectra of the Ag nanoparticles plasmon that produced by the levodopa analytes against the reagent blank. Preliminary studies on the kinetic behavior of these compounds showed differences in their reactivity. As shown in Fig. 2 the reaction of benserazide hydrochloride with silver ions is faster than it is with levodopa, and is almost completed within 30 min, and at the same time, there is also some difference between the kinetic runs of levodopa and benserazide. It is these differences that give the possibility for simultaneous kinetic determination of these analytes in their mixtures. It should be noted that in the reaction, the analytes are limiting reagents in compare with silver ions and Ag+ ions were not totally converted into AgNPs. As it is obvious from Fig. 2, the
J. Tashkhourian et al. / Spectrochimica Acta Part A 82 (2011) 25–30
27
0.9
1
B
0.9
0.8 0.8
Absorbance
Absorbance
0.7 0.6 0.5
0.7 A
0.6
0.5
0.4 0.3
0.4
0.2
0.3
0.1
0
0.02
0 400
300
500
600
700
800
Fig. 1. Absorbance spectra of AgNPs formed by levodopa under the optimum conditions.
A 0.6
Absorbance
0.08
0.1
0.12
Fig. 3. Effect of PVP concentration on the surface plasmon band intensity of the AgNPs. Conditions: AgNO3 (2 ×10−3 M), PVP (0.01–0.1 g/L), NaOH (0.2 mM), levodopa (9.6 × 10−6 M) (A) and benserazide hydrochloride (6.4 × 10−6 M) (B).
basic modes: electrostatic and steric stabilization [34]. Electrostatic stabilization is caused by the columbic repulsion between particles, caused by the electrical double layer formed by ions adsorbed at the particle surface (e.g., sodium citrate) and the corresponding counter ions. Steric stabilization is achieved because of the coordination of sterically demanding organic molecules and polymers that act as protective shields on the metallic surface (e.g., PVP). In this study PVP and sodium citrate were selected as the stabilizer for preventing of silver nanoparticles agglomeration in which the sensitivity of method was better when we used PVP in compare to sodium citrate. Effect of PVP concentration was studied and the variation of absorbance at 440 nm that is max surface plasmon peak of the AgNPs as a function of the concentration of PVP is shown in Fig. 3. The results show that the maximum intensity was obtained at 0.06 g/L PVP for different analytes.
0.7
B
0.5
0.06
PVP concentration (g/L)
900
Wavelength (nm)
0.04
0.4 0.3 0.2 0.1
3.3. Effect of pH 0 0
10
20
30
Time (min) Fig. 2. Kinetic curves corresponding to the absorbance changes of the levodopa and benserazide hydrochloride stimulated growth of AgNPs. Conditions: AgNO3 (2 × 10−3 M), PVP (0.06 g/L), NaOH (0.2 mM), levodopa (A) and benserazide hydrochloride (B).
absorbance changes of the levodopa and benserazide hydrochloride stimulated growth of AgNPs almost complete and reaches a plateau. Also the spectra of the AgNPs formed upon treatment with different concentrations of the analytes for a fixed time interval shows that there was not any remarkable red shift of the surface plasmon extinction peak with time [36]. Therefore, the remained silver ions cannot affect the particle size of the formed AgNPs with time. 3.2. Effect of stabilizer type and concentration An important issue in the preparation of metal nanoparticles is the choice of the capping agent used to protect or stabilize the nanoparticle colloidal metals from agglomeration. Size and morphologies of nanoparticles are depending significantly on capping materials. Nanoparticles stabilization achieves according to the two
Since H+ was produced from the reaction between silver ions and analyte, influence of pH on Ag+ reduction by analyte is expected and consumption of H+ can promote silver ion reduction. In spite of in the presence of PVP, H(PVP)+ complex compound can produce and facilitated silver nanoparticles formation reaction the effect of NaOH concentration on silver nanoparticles surface plasmon peak intensity was investigated and the results were shown in Fig. 4. Since the buffered condition failed to obtain silver nanoparticles NaOH was added for provide enough alkalinity. As it is seen, the signal increases up to a known concentration of NaOH then decreases, which might be due to the Ag2 O formation. Thus, a concentration of 0.2 mM NaOH was selected as the optimum NaOH concentration. 3.4. Synergistic effect As Fig. 5 shows the kinetic curve of Ag+ after reaction with the binary mixture of the analyte (curve A) has a lower overall intensity than the one computed from the sum of the kinetic curve of Ag+ after reaction with the individual analyte (curve B). Thus there is a factor(s), which is not accounted for by the simple Beer’s and absorbance additively laws and ANN models can be used for solving this problem and nonlinearity due to synergistic effect to resolve mixtures. Non-linear multivariate regression is one of the fields where the artificial neural networks are most commonly applied
28
J. Tashkhourian et al. / Spectrochimica Acta Part A 82 (2011) 25–30
1.2
Table 1 Features the analytical characteristics of the method.
Absorbance
1 0.8
Levodopa
Benserazide hydrochloride
Linear range (M) Calibration equationa
1.6 × 10−7 –1.0 × 10−5 y = 83,205x + 0.0123 R2 = 0.994 0.9 × 10−8 3.4–5.8
8 × 10−7 –1.12 × 10−5 y = 57.92x + 0.1144 R2 = 0.997 3.4 × 10−7 2.3–3.6
LODb (M) %RSD
A
a
0.6
b
Unit of concentration in calibration equations are mM. Theoretical detection limit (blank plus three times its S.D.).
B 0.4
3.6. ANN modeling
0.2 0 0.00
0.10
0.20
0.30
0.40
NaOH concentration (mM) Fig. 4. Dependence of the surface plasmon band intensity on the concentrations of NaOH, AgNO3 (2 × 10−3 M), PVP (0.06 g/L), NaOH, (0.05–0.3 mM), levodopa (9.6 × 10−6 M) (A) and benserazide hydrochloride (1.28 × 10−5 M) (B).
1.4 B 1.2 1 Absorbance
Parameters
A
0.8 0.6 0.4
Multilayer feed-forward networks with the back-propagation learning algorithm are the most popular network architecture. Its basic theory and application to chemical problems can be found in the literature [41,42]. In this work, the SPR absorbance data vs time for 30 min. were treated with back propagation ANN, and the output of the network was the calculated concentrations of levodopa and benserazide hydrochloride related to the SPR absorbance data. A set of sample solutions with different concentrations of analytes was prepared and measurements were carried out under the optimum conditions. In order to select the mixtures that provide more information using a few experimental trials from calibration set, 20 standard mixtures of analytes were prepared for the training (calibration) set. Another 10 synthetic mixtures were used for verification of the calibrations (prediction or test set). The calibration set was used for construction and optimization of ANN model. The prediction set was employed as an independent test set to evaluate the predictive ability of the model. Table 2 shows the composition of the calibration and prediction samples. The neural network performs a nonlinear iterative fit of data. During the training process (i.e. calibration) the weights are iteratively calculated in order to minimize the sum of squared difference between the known concentrations and the calculated concentrations. The iteration would be finished when the error of prediction reached a minimum. The kinetic data obtained from experiments were processed by ANN with error back-propagation as training scheme and generalized delta rule for weighting.
0.2 0 0
10
20
30
Time (min) Fig. 5. Kinetic curve of Ag+ after reaction with the binary mixture of the analytes (A) and sum of the kinetic curve of Ag+ after reaction with the individual analytes (B).
[19,40]. In the other hand with proper training, ANNs can accurately model the presence of synergistic effect and avoid the potential loss of kinetic data for mixtures resulting from too short induction periods, outliers, small differences in the rate constants, and so on. 3.5. Analytical parameters Under the optimum conditions, the calibration curves were linear in the ranges 1.6 × 10−7 –1.0 × 10−5 M for levodopa and 8 × 10−7 –1.12 × 10−5 M for benserazide respectively. Limit of detections were obtained 0.9 × 10−8 M for LD and 3.4 × 10−7 M for BH and features the analytical characteristics of the method for individual analyte was shown in Table 1.
Table 2 Composition of the calibration and prediction sample sets. Sample
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Calibration
Prediction
Levodopa (M)
Benserazide hydrochloride (M)
Levodopa (M)
Benserazide hydrochloride (M)
8.0 12.0 12.0 6.4 6.4 6.4 4.8 4.8 4.8 4.8 3.2 3.2 3.2 1.6 1.6 1.6 1.6 6.4 8.0 6.4
1.60 8.0 6.4 9.6 4.8 1.6 9.6 4.8 3.2 1.6 9.6 8.0 6.4 9.6 6.4 4.8 1.6 4.0 4.0 5.6
8.0 8.0 12.0 12.0 6.4 6.4 4.8 4.8 3.2 3.2 – – – – – – – – – –
6.4 4.8 4.8 3.2 8.0 3.2 8.0 6.4 4.8 1.6 – – – – – – – – – –
J. Tashkhourian et al. / Spectrochimica Acta Part A 82 (2011) 25–30 Table 3 Optimized parameters used for construction of ANN models.
3.7. Reducing the number of data The number of elements of input vectors is one of the factors, which determine the number of weights that should be adjusted in the training process. Decreasing the data volume before using ANN for nonlinear multivariate calibration was suggested as a preprocessing step in many of the previous studies [43]. So before building the ANN models, the original kinetic data for the levodopa and benserazide hydrochloride mixtures were subjected to principal component analysis and decomposed to PC scores, which are then submitted as input data for the input layer of ANN. Reducing the number of input data without losing information decreases the effect of change in getting accurate adjustments of weights. Also, when the PCA data reduction procedure is applied prior to the construction of the nonlinear ANN model, its dimensional effect is to increase the numerical stability of the model construction process and reduce the amount of colinearity between variables [44]. The optimum numbers of scores for each analyte are determined based on the prediction ability of constructed network for a set of independent sample. 3.8. Network optimization The back propagation ANN methodology has several empirically determined parameters. These include: when to stop training (i.e. the number of epochs or the convergence criterion), the number of hidden units, and the learning rate and momentum terms. In this study, all parameters for the network models were tested and then chosen for inclusion in the analytical model corresponds to the minimum value of the relative standard error (RSE%). The RSE for a single component in mixtures can be formulated as
N RSE (%) = 100 ×
j=1
(Cˆ i − Ci )
N
i=1
(Ci )
2
29
2
0.5
Benserazide hydrochloride
Levodopa
Benserazide hydrochloride
Input nodes Output nodes Hidden nodes Learning rate Momentum Number of iteration Hidden layer transfer function Output layer transfer function (PCS)
5 1 5 0.92 0.03 11000 Sigmoid Sigmoid 5
4 1 4 0.86 0.05 13000 Sigmoid Sigmoid 4
Table 4 Prediction results hydrochloride. Sample
1 2 3 4 5 6 7 8 9 10 RSE%
For the constructed model, four general statistical parameters were selected to evaluate the prediction ability of the model for simultaneous determination of levodopa and benserazide hydrochloride. For this case, the predicted concentrations of each sample in calibration step were compared with the actual con-
mixtures
of
Levodopa
and
Benserazide
Found
Levodopa (M)
Benserazide hydrochloride (M)
Levodopa (M)
Benserazide hydrochloride (M)
6.4 8.0 3.2 8.0 5.6 3.2 5.6 3.2 5.6 3.2
5.6 5.6 5.6 8.8 8.8 1.6 4.0 4.0 5.6 8.8
6.4 8.4 3.0 8.5 5.6 3.2 5.9 3.2 5.4 3.3 4.5%
5.0 5.7 5.6 8.3 9.3 1.5 4.2 4.0 5.4 8.8 6.3%
centrations. The first statistical parameter is the root mean square difference (RMSD).
RMSD =
2 1 ˆ (Ci − Ci ) n n
0.5 (2)
i=1
This parameter is an expression of the average error in the analysis for each component in training samples. The second statistical parameter was the relative error of prediction (REP).
100 REP (%) = c¯
2 1 ˆ (Ci − Ci ) n n
0.5 (3)
i=1
That shows the predictive ability of each component. The predictive applicability of a regression model is described in various ways. The most general expression is the standard error of prediction (SEP) and standard error of calibration denoted by SEC.
SEP (SEC) =
n (ˆc i=1 i
− ci )
n−1
2
0.5 (4)
The square of the correlation coefficient (R2 ), which is indicated the quality of fit of all the data to a straight line is calculated for the checking of each.
n 2 (ˆci − c¯ ) R = ni=1 2 2
i=1
3.9. Statistical parameters
synthetic
Added
(1)
and designated as RSEC%, and RSEP% for the calibration, and prediction sets, respectively. PC-ANN architectures were constructed using a different number of PCs with one bias node for the input layer and the number of PCs to be included in the PC-ANN model was investigated. The RSEP% for each analyte reaches to a minimum when the number of PCs was 5 for levodopa and 4 for benserazide hydrochloride. In order to determine the optimum number of hidden nodes, a series of different topologies was used, in which the number of nodes was varied from 1 to 15. Each topology was repeated five times to avoid random correlation due to the random initialization of the weights. According to its generalization ability on the test set, the RSEP% of the test set of each topology was calculated. It was found that the lowest RSEP% value obtained when the number of hidden units was 5 for levodopa and 4 for benserazide. All other parameters of ANN with back-propagation learning algorithm including number of iterations, momentum, learning rate and transfer functions were optimized using minimum RSEP% values of the test set during the training process. The construction of optimized ANN model is summarized in Table 3. The results obtained for validation samples are given in Table 4. The reasonable relative errors for each analyte indicate the accuracy of the proposed method.
for
(ci − c¯ )
(5)
The statistical results (RMSD, REP, SEP, R2 ) are summarized in Table 5. 3.10. Analytical application The proposed method was successfully applied to the simultaneous determination of LD and BH in human serum. For the
30
J. Tashkhourian et al. / Spectrochimica Acta Part A 82 (2011) 25–30
Table 5 Statistical parameters obtained by applying the ANN methods.
References
Parameter
Levodopa
Benserazide hydrochloride
RMSD REP (%) SEP R2
0.033 0.75 0.056 0.997
0.058 0.99 0.073 0.994
Table 6 Prediction results for determination of BH and LD using the optimized neural network model. Sample
1 2 3 4 5
Spiked
Found
Levodopa (M)
Benserazide hydrochloride (M)
Levodopa (M)
Benserazide hydrochloride (M)
8.00 6.40 1.60 4.80 3.20 RSE (%)
9.60 3.20 1.60 6.40 1.60
8.07 6.4 1.63 4.75 3.23 5.3%
9.65 3.2 1.52 6.42 1.57 7.1%
determination of analyte in human serum, a certain amount of each the compounds was spiked into the sample without further treatment and their concentrations were determined by the proposed methods. The results are given in Table 6. The results show that there is a satisfactory agreement between the declared analyte content and the determined value. Therefore, the proposed method could be effectively used for the determination of LD and BH in real samples such as human serum. 4. Conclusion In this paper, we have investigated the chemistry, analytical methodology and the chemometrics interpretation of the results for the determination of levodopa and benserazide hydrochloride in synthetic and pharmaceutical samples by the kinetic–spectrophotometric method. The models were validated with the use of synthetic binary mixtures of levodopa and benserazide hydrochloride and the figures of merit, RSE%, were evaluated. The aims were: (1) to carry out a simultaneous determination of the levodopa and benserazide hydrochloride in biological samples, (2) representation ability of nanoparticles for determination of important analyte. Finally, the most successful BP-ANN calibration model was applied for the prediction of the levodopa and benserazide hydrochloride in synthetic and pharmaceutical samples with satisfactory results.
[1] A.R. Gennaro, Remington’s Pharmaceutical Sciences, 15th ed., Mach Publishing, Easton, PA, 1975, p. 858. [2] K. Ghose, Drugs Today 10 (1985) 463. [3] S.C. Sweetman (Ed.), Martindale: The Complete Drug Reference, 33rd, Pharmaceutical Press, London, UK, 2002, p. 1160. [4] J. Karpínska, J. Smyk, E. Wołyniec, Spectrochim. Acta A 62 (2005) 213. [5] K.L. Marques, J.L. Santos, J.A. Lopes, J.L. Lima, Anal. Sci. 24 (2008) 985. [6] D. Perez-Bendito, Analyst 115 (1990) 689. [7] S.R. Crouch, Anal. Chim. Acta 283 (1993) 453. [8] M. Otto, Analyst 115 (1990) 685. [9] T.F. Cullen, S.R. Crouch, Mikrochim. Acta 126 (1997) 1. [10] G. Absalan, M. Nekoeinia, Anal. Chim. Acta 531 (2005) 293. [11] Y. Ni, Z. Qi, S. Kokot, Chemometr. Intell. Lab. Syst. 82 (2006) 241. [12] Y. Ni, P. Qiu, S. Kokot, Anal. Chim. Acta 516 (2004) 7. [13] Y. Ni, C. Huang, S. Kokot, Anal. Chim. Acta 480 (2003) 53. [14] Y. Ni, C. Huang, S. Kokot, Chemometr. Intell. Lab. Syst. 71 (2004) 177. [15] Y. Ni, P. Qiu, S. Kokot, Anal. Chim. Acta 537 (2005) 321. [16] A. Safavi, O. Moradlou, S. Maesum, Talanta 62 (2004) 51. [17] I.A. Pettas, M.I. Karayannis, Anal. Chim. Acta 491 (2003) 219. [18] A. Safavi, H. Abdollahi, M.R. Hormozi Nezhad, Talanta 59 (2003) 515. [19] F. Marini, R. Bucci, A.L. Magri, A.D. Magri, Microchim. J. 88 (2008) 178. [20] G. Kateman, Chemometr. Intell. Lab. Syst. 19 (1993) 135. [21] J.R.M. Simits, W.J. Melssen, L.M.C. Buydens, G. Kateman, Chemometr. Intell. Lab. Syst. 22 (1994) 165. [22] P.J. Gemperline, J.R. Long, V.G. Gregoriou, Anal. Chem. 63 (1991) 2313. [23] C. Borggard, H.H. Thodberg, Anal. Chem. 64 (1992) 545. [24] P. Geladi, B.R. Kowalski, Anal. Chim. Acta 185 (1986) 19. [25] M. Blanco, J. Coello, H. Iturriaga, S. Maspoch, M. Redon, Anal. Chem. 67 (1995) 4477. [26] M. Blanco, J. Coello, H. Iturriaga, S. Maspoch, M. Redon, N. Villegas, Analyst 121 (1996) 395. [27] A.J. Haes, R.P. Van Duyne, J. Am. Chem. Soc. 124 (2002) 10596. [28] G. Kalyuzhny, M. Schneeweiss, A. Shanzer, A. Vaskevich, I. Rubinstein, J. Am. Chem. Soc. 123 (2001) 3177. [29] M. Malinsky, K. Kelly, G. Schatz, R. Van Duyne, J. Am. Chem. Soc. 123 (2001) 1471. [30] M. Reza Hormozi Nezhad, M. Alimohammadi, J. Tashkhourian, S. Mehdi Razavian, Spectrochim. Acta A 71 (2008) 199. [31] H. Park, J. Yoon, K. Kim, Langmuir 22 (2006) 1626. [32] Y. Yang, L. Xiong, J. Shi, M. Nogami, Nanotechnology 17 (2006) 2670. [33] I. Tanahashi, F. Yamazaki, K. Hamada, Chem. Lett. 35 (2006) 454. [34] S. Malynych, G. Chumanov, J. Opt. A: Pure Appl. Opt. 8 (2006) S144. [35] R. Jin, Y.C. Cao, E. Hao, G.S. Meˇıtraux, G.C. Schatz, C.A. Mirkin, Nature 425 (2003) 478. [36] M.R. Hormozi Nezhad, J. Tashkhourian, J. Khodaveisic, J. Iran. Chem. Soc. 7 (2010) S83. [37] M.R. Hormozi Nezhad, J. Tashkhourian, J. Khodaveisic, M.R. Khoshi, Anal. Methods 2 (2010) 1263. [38] J. Ling, Y. Sang, C.Z. Huang, J. Pharm. Biomed. Anal. 47 (2008) 860. [39] L.P. Wu, Y.F. Li, C.Z. Huang, Q. Zhang, Anal. Chem. 78 (2006) 5570. [40] J. Zupan, J. Gasteiger, Anal. Chim. Acta 248 (1991) 1. [41] J. Zupan, J. Gasteiger, Neural Network for Chemists: An Introduction, VCH, Weinheim, 1993. [42] F. Despangne, D.L. Massart, Analyst 123 (1998) 157R. [43] P.J. Gemperline, Chemometr. Intell. Lab. Syst. 15 (1992) 115. [44] S. Sekulic, M.B. Seasholtz, Z. Wang, B.R. Kowalski, S.E. Lee, B.R. Holt, Anal. Chem. 65 (1993) 835A.
Contents lists available at ScienceDirect
Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy journal homepage: www.elsevier.com/locate/saa
Application of silver nanoparticles and principal component-artificial neural network models for simultaneous determination of levodopa and benserazide hydrochloride by a kinetic spectrophotometric method J. Tashkhourian a,∗ , M.R. Hormozi-Nezhad b,c,∗ , J. Khodaveisi d a
Department of Chemistry, College of Sciences, Shiraz University, Shiraz 71454, Iran Department of Chemistry, Sharif University of Technology, Tehran 11155-9516, Iran c Institute for Nanoscience and Nanotechnology, Sharif University of Technology, Tehran, Iran d Department of Chemistry, Faculty of Sciences, Persian Gulf University, Bushehr 75169, Iran b
a r t i c l e
i n f o
Article history: Received 6 April 2011 Received in revised form 29 May 2011 Accepted 13 June 2011 Keywords: Kinetic methods Levodopa Benserazide hydrochloride Silver nanoparticles Artificial neural network
a b s t r a c t A multicomponent analysis method based on principal component analysis-artificial neural network model (PC-ANN) is proposed for the simultaneous determination of levodopa (LD) and benserazide hydrochloride (BH). The method is based on the reaction of levodopa and benserazide hydrochloride with silver nitrate as an oxidizing agent in the presence of PVP and formation of silver nanoparticles. The reaction monitored at analytical wavelength 440 nm related to surface plasmon resonance band of silver nanoparticles. Differences in the kinetic behavior of the levodopa and benserazide hydrochloride were exploited by using principal component analysis, an artificial neural network (PC-ANN) to resolve concentration of analytes in their mixture. After reducing the number of kinetic data using principal component analysis, an artificial neural network consisting of three layers of nodes was trained by applying a back-propagation learning rule. The optimized ANN allows the simultaneous determination of analytes in mixtures with relative standard errors of prediction in the region of 4.5 and 6.3 for levodopa and benserazide hydrochloride respectively. The results show that this method is an efficient method for prediction of these analytes. © 2011 Elsevier B.V. All rights reserved.
1. Introduction Levodopa [(−)3-(3,4-dihydroxylphenyl)-l-alanine] (LD) is an important neurotransmitter, which has been used for the treatment of neural disorders such as Parkinson’s disease. The cause of this disease is the significant depletion of dopamine [1]. After its oral administration, levodopa is absorbed through the bowel and converted into dopamine by decarboxylase. Hence, levodopa can relax the symptoms of Parkinson’s disease and also decrease muscular rigidity, and tremor. Unfortunately, conversion of levodopa into dopamine in the rest of the body can cause unwanted side effects such as nausea and palpitations. Benserazide hydrochloride (BH) is used in combination with the levodopa to prevent this happening. It blocks the conversion of levodopa to dopamine in the body and so prevents these side effects [2,3]. With due attention to this instance simultaneous determination of this drug is very impor-
∗ Corresponding authors at: Department of Chemistry, College of Sciences, Shiraz University, Shiraz 71454, Iran. Tel.: +98 711 2284822; fax: +98 711 2286008. E-mail addresses: [email protected] (J. Tashkhourian), [email protected] (M.R. Hormozi-Nezhad). 1386-1425/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.saa.2011.06.014
tant and several methods have also been reported for simultaneous determination of levodopa and benserazide hydrochloride [4,5]. The kinetic methodology based on the difference of reaction rates, especially in cases where the analytes react with a common reagent, is an effective way to analyze several analytes simultaneously [6], and it has made a great improvement by using the chemometric procedures [7–9]. Chemometric methods based on factor analysis and artificial intelligence, including principal component regression (PCR), partial least squares (PLS) and artificial neural networks (ANNs), have found increasing applications for multicomponent kinetic determination [10–18]. These methods make it possible to eliminate or reduce the effects of the analyte–analyte interaction, the synergistic effect (non-additively of reaction rates), the multi-step process and any other unknown nonlinearity. The use of neural networks in chemometrics has increased during the last decades [19–21]. Artificial neural network (ANN) is nonlinear computational tools suitable to a great host of practical application due to their flexibility and adaptability. Their ability to handle non-linearities makes them a valuable contribution to the discipline. It has been demonstrated that it is possible to obtain excellent results in multivariate calibration problems using ANN [22,23] and also application of ANN model with data
26
J. Tashkhourian et al. / Spectrochimica Acta Part A 82 (2011) 25–30
Scheme 1. Reduction process of the silver salt by the levodopa.
pretreatment method, such as normalization [24] and compressing data into scores with the use of principal component analysisartificial neural network (PC-ANN) as input data to quantify mixtures in different kinetic situations has been reported [25,26]. Metal nanomaterials such as silver nanoparticles (AgNPs) have been found wide applications for analytical purposes due to the unique optical and electric properties. Owing to the collective oscillations of conducting electrons under the excitation of a light beam [27] known as localized surface plasmon resonance (LSPR) which depends on the size, shape and dielectric environment of nanoparticles, noble metal nanoparticles exhibit a strong UV–Vis absorption band. For this reason AgNPs have bright color in the visible spectral range concerning their SPR absorption and light scattering. Therefore, recently there has been high interest in applications of metal nanoparticles [28–34] and among them AgNPs in the field of surface plasmon resonance spectroscopy [35–38] and light scattering detection [39]. In this work, a new method for simultaneous determination of the levodopa and benserazide hydrochloride proposed with the aid of the chemometric approach of principal component-artificial neural network (PC-ANN) models. The method is based on the reaction of levodopa and benserazide hydrochloride with the oxidizing agent (silver nitrate) in the presence of polyvinylpyrrolidone (PVP) and formation of silver nanoparticles in a slightly basic medium and spectrophotometrically monitoring the changes of SPR band at maximum wavelength of silver nanoparticles (440 nm) vs. time (Scheme 1). The aim of this study as well as simultaneous determination of the levodopa and benserazide hydrochloride in their mixture is the representation ability of Ag nanoparticles for determination of important analyte. 2. Experimental 2.1. Apparatus and software The UV–Vis absorbance spectra were recorded on a PerkinElmer (Lambda25) spectrophotometer and 1.0 cm glass cell was used. Measurements of pH were made with a Denver Instrument Model 270 pH meter equipped with a Metrohm glass electrode. Computational work was performed with Winnn32 software as a multilayer feed forward ANN with the back propagation of error learning algorithm. The principal component analysis (PCA) process was performed by MATLAB (Mathworks, version 6.1) programs, and the scores of the PCA running were fed into ANN program as inputs. 2.2. Chemical and reagents All chemicals used were of analytical reagent grade and the solutions were prepared with deionized water. Levodopa (LD), benserazide hydrochloride (BH), silver nitrate and polyvinylpyrrolidone
(PVP) by average mol. wt. 10,000 were purchased from Merck and Fluka. All other common laboratory chemicals were of the best grade available and were used without further purification. All solutions were used within 1 h after preparation, and the experiments were performed at the ambient temperature (25 ± 2 ◦ C). Stock solutions of AgNO3 (0.01 M) were prepared by dissolving 0.085 g AgNO3 in deionized water and diluting to 50 mL. A stock solution of polyvinylpyrrolidone (PVP) (0.4 g/L) was prepared daily by dissolving 0.01 g of PVP (Merck) in water and diluting to 25 mL. Fresh 0.05 M solution of levodopa and benserazide hydrochloride were prepared daily by dissolving the reagent in deionized water. 2.3. General procedure In 5 mL volumetric flasks, 0.7 mL of PVP 0.4 (g/L), 1 mL of NaOH 0.001 mol/L, different concentrations of the analyte and 1 mL of AgNO3 0.01 M added to obtain the reasonable solution, then it was mixed slowly and transferred into a 1 cm spectrophotometric cell to record the absorbance vs. time. It should be noted that the order of the addition of the reagents is very important. The absorbance was measured at 440 nm that is max of silver nanoparticles surface plasmon resonance peak at this condition, against a reagent blank. 3. Result and discussion 3.1. Kinetic study of reaction between LD and BH and Ag+ ion Nanoparticles made of silver and gold have been the focus of research for many decades due to their intriguing optical properties. The systems in this study consist of an aqueous AgNO3 solution that includes polyvinylpyrrolidone (PVP), as stabilizer, at an alkaline medium. Levodopa and benserazide hydrochloride act as effective reducing agents for reduction of silver metal salt (Ag+ ) to the AgNPs without added any seeds. In the absence of reducing agents, there is no absorption peak in visible region (380–700 nm). Upon addition of analyte which act as reducing agent silver ions reduced to silver nanoparticles, then the absorbance characteristic to the plasmon of the Ag-NPs is observed. Fig. 1 shows the absorption spectra of the Ag nanoparticles plasmon that produced by the levodopa analytes against the reagent blank. Preliminary studies on the kinetic behavior of these compounds showed differences in their reactivity. As shown in Fig. 2 the reaction of benserazide hydrochloride with silver ions is faster than it is with levodopa, and is almost completed within 30 min, and at the same time, there is also some difference between the kinetic runs of levodopa and benserazide. It is these differences that give the possibility for simultaneous kinetic determination of these analytes in their mixtures. It should be noted that in the reaction, the analytes are limiting reagents in compare with silver ions and Ag+ ions were not totally converted into AgNPs. As it is obvious from Fig. 2, the
J. Tashkhourian et al. / Spectrochimica Acta Part A 82 (2011) 25–30
27
0.9
1
B
0.9
0.8 0.8
Absorbance
Absorbance
0.7 0.6 0.5
0.7 A
0.6
0.5
0.4 0.3
0.4
0.2
0.3
0.1
0
0.02
0 400
300
500
600
700
800
Fig. 1. Absorbance spectra of AgNPs formed by levodopa under the optimum conditions.
A 0.6
Absorbance
0.08
0.1
0.12
Fig. 3. Effect of PVP concentration on the surface plasmon band intensity of the AgNPs. Conditions: AgNO3 (2 ×10−3 M), PVP (0.01–0.1 g/L), NaOH (0.2 mM), levodopa (9.6 × 10−6 M) (A) and benserazide hydrochloride (6.4 × 10−6 M) (B).
basic modes: electrostatic and steric stabilization [34]. Electrostatic stabilization is caused by the columbic repulsion between particles, caused by the electrical double layer formed by ions adsorbed at the particle surface (e.g., sodium citrate) and the corresponding counter ions. Steric stabilization is achieved because of the coordination of sterically demanding organic molecules and polymers that act as protective shields on the metallic surface (e.g., PVP). In this study PVP and sodium citrate were selected as the stabilizer for preventing of silver nanoparticles agglomeration in which the sensitivity of method was better when we used PVP in compare to sodium citrate. Effect of PVP concentration was studied and the variation of absorbance at 440 nm that is max surface plasmon peak of the AgNPs as a function of the concentration of PVP is shown in Fig. 3. The results show that the maximum intensity was obtained at 0.06 g/L PVP for different analytes.
0.7
B
0.5
0.06
PVP concentration (g/L)
900
Wavelength (nm)
0.04
0.4 0.3 0.2 0.1
3.3. Effect of pH 0 0
10
20
30
Time (min) Fig. 2. Kinetic curves corresponding to the absorbance changes of the levodopa and benserazide hydrochloride stimulated growth of AgNPs. Conditions: AgNO3 (2 × 10−3 M), PVP (0.06 g/L), NaOH (0.2 mM), levodopa (A) and benserazide hydrochloride (B).
absorbance changes of the levodopa and benserazide hydrochloride stimulated growth of AgNPs almost complete and reaches a plateau. Also the spectra of the AgNPs formed upon treatment with different concentrations of the analytes for a fixed time interval shows that there was not any remarkable red shift of the surface plasmon extinction peak with time [36]. Therefore, the remained silver ions cannot affect the particle size of the formed AgNPs with time. 3.2. Effect of stabilizer type and concentration An important issue in the preparation of metal nanoparticles is the choice of the capping agent used to protect or stabilize the nanoparticle colloidal metals from agglomeration. Size and morphologies of nanoparticles are depending significantly on capping materials. Nanoparticles stabilization achieves according to the two
Since H+ was produced from the reaction between silver ions and analyte, influence of pH on Ag+ reduction by analyte is expected and consumption of H+ can promote silver ion reduction. In spite of in the presence of PVP, H(PVP)+ complex compound can produce and facilitated silver nanoparticles formation reaction the effect of NaOH concentration on silver nanoparticles surface plasmon peak intensity was investigated and the results were shown in Fig. 4. Since the buffered condition failed to obtain silver nanoparticles NaOH was added for provide enough alkalinity. As it is seen, the signal increases up to a known concentration of NaOH then decreases, which might be due to the Ag2 O formation. Thus, a concentration of 0.2 mM NaOH was selected as the optimum NaOH concentration. 3.4. Synergistic effect As Fig. 5 shows the kinetic curve of Ag+ after reaction with the binary mixture of the analyte (curve A) has a lower overall intensity than the one computed from the sum of the kinetic curve of Ag+ after reaction with the individual analyte (curve B). Thus there is a factor(s), which is not accounted for by the simple Beer’s and absorbance additively laws and ANN models can be used for solving this problem and nonlinearity due to synergistic effect to resolve mixtures. Non-linear multivariate regression is one of the fields where the artificial neural networks are most commonly applied
28
J. Tashkhourian et al. / Spectrochimica Acta Part A 82 (2011) 25–30
1.2
Table 1 Features the analytical characteristics of the method.
Absorbance
1 0.8
Levodopa
Benserazide hydrochloride
Linear range (M) Calibration equationa
1.6 × 10−7 –1.0 × 10−5 y = 83,205x + 0.0123 R2 = 0.994 0.9 × 10−8 3.4–5.8
8 × 10−7 –1.12 × 10−5 y = 57.92x + 0.1144 R2 = 0.997 3.4 × 10−7 2.3–3.6
LODb (M) %RSD
A
a
0.6
b
Unit of concentration in calibration equations are mM. Theoretical detection limit (blank plus three times its S.D.).
B 0.4
3.6. ANN modeling
0.2 0 0.00
0.10
0.20
0.30
0.40
NaOH concentration (mM) Fig. 4. Dependence of the surface plasmon band intensity on the concentrations of NaOH, AgNO3 (2 × 10−3 M), PVP (0.06 g/L), NaOH, (0.05–0.3 mM), levodopa (9.6 × 10−6 M) (A) and benserazide hydrochloride (1.28 × 10−5 M) (B).
1.4 B 1.2 1 Absorbance
Parameters
A
0.8 0.6 0.4
Multilayer feed-forward networks with the back-propagation learning algorithm are the most popular network architecture. Its basic theory and application to chemical problems can be found in the literature [41,42]. In this work, the SPR absorbance data vs time for 30 min. were treated with back propagation ANN, and the output of the network was the calculated concentrations of levodopa and benserazide hydrochloride related to the SPR absorbance data. A set of sample solutions with different concentrations of analytes was prepared and measurements were carried out under the optimum conditions. In order to select the mixtures that provide more information using a few experimental trials from calibration set, 20 standard mixtures of analytes were prepared for the training (calibration) set. Another 10 synthetic mixtures were used for verification of the calibrations (prediction or test set). The calibration set was used for construction and optimization of ANN model. The prediction set was employed as an independent test set to evaluate the predictive ability of the model. Table 2 shows the composition of the calibration and prediction samples. The neural network performs a nonlinear iterative fit of data. During the training process (i.e. calibration) the weights are iteratively calculated in order to minimize the sum of squared difference between the known concentrations and the calculated concentrations. The iteration would be finished when the error of prediction reached a minimum. The kinetic data obtained from experiments were processed by ANN with error back-propagation as training scheme and generalized delta rule for weighting.
0.2 0 0
10
20
30
Time (min) Fig. 5. Kinetic curve of Ag+ after reaction with the binary mixture of the analytes (A) and sum of the kinetic curve of Ag+ after reaction with the individual analytes (B).
[19,40]. In the other hand with proper training, ANNs can accurately model the presence of synergistic effect and avoid the potential loss of kinetic data for mixtures resulting from too short induction periods, outliers, small differences in the rate constants, and so on. 3.5. Analytical parameters Under the optimum conditions, the calibration curves were linear in the ranges 1.6 × 10−7 –1.0 × 10−5 M for levodopa and 8 × 10−7 –1.12 × 10−5 M for benserazide respectively. Limit of detections were obtained 0.9 × 10−8 M for LD and 3.4 × 10−7 M for BH and features the analytical characteristics of the method for individual analyte was shown in Table 1.
Table 2 Composition of the calibration and prediction sample sets. Sample
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Calibration
Prediction
Levodopa (M)
Benserazide hydrochloride (M)
Levodopa (M)
Benserazide hydrochloride (M)
8.0 12.0 12.0 6.4 6.4 6.4 4.8 4.8 4.8 4.8 3.2 3.2 3.2 1.6 1.6 1.6 1.6 6.4 8.0 6.4
1.60 8.0 6.4 9.6 4.8 1.6 9.6 4.8 3.2 1.6 9.6 8.0 6.4 9.6 6.4 4.8 1.6 4.0 4.0 5.6
8.0 8.0 12.0 12.0 6.4 6.4 4.8 4.8 3.2 3.2 – – – – – – – – – –
6.4 4.8 4.8 3.2 8.0 3.2 8.0 6.4 4.8 1.6 – – – – – – – – – –
J. Tashkhourian et al. / Spectrochimica Acta Part A 82 (2011) 25–30 Table 3 Optimized parameters used for construction of ANN models.
3.7. Reducing the number of data The number of elements of input vectors is one of the factors, which determine the number of weights that should be adjusted in the training process. Decreasing the data volume before using ANN for nonlinear multivariate calibration was suggested as a preprocessing step in many of the previous studies [43]. So before building the ANN models, the original kinetic data for the levodopa and benserazide hydrochloride mixtures were subjected to principal component analysis and decomposed to PC scores, which are then submitted as input data for the input layer of ANN. Reducing the number of input data without losing information decreases the effect of change in getting accurate adjustments of weights. Also, when the PCA data reduction procedure is applied prior to the construction of the nonlinear ANN model, its dimensional effect is to increase the numerical stability of the model construction process and reduce the amount of colinearity between variables [44]. The optimum numbers of scores for each analyte are determined based on the prediction ability of constructed network for a set of independent sample. 3.8. Network optimization The back propagation ANN methodology has several empirically determined parameters. These include: when to stop training (i.e. the number of epochs or the convergence criterion), the number of hidden units, and the learning rate and momentum terms. In this study, all parameters for the network models were tested and then chosen for inclusion in the analytical model corresponds to the minimum value of the relative standard error (RSE%). The RSE for a single component in mixtures can be formulated as
N RSE (%) = 100 ×
j=1
(Cˆ i − Ci )
N
i=1
(Ci )
2
29
2
0.5
Benserazide hydrochloride
Levodopa
Benserazide hydrochloride
Input nodes Output nodes Hidden nodes Learning rate Momentum Number of iteration Hidden layer transfer function Output layer transfer function (PCS)
5 1 5 0.92 0.03 11000 Sigmoid Sigmoid 5
4 1 4 0.86 0.05 13000 Sigmoid Sigmoid 4
Table 4 Prediction results hydrochloride. Sample
1 2 3 4 5 6 7 8 9 10 RSE%
For the constructed model, four general statistical parameters were selected to evaluate the prediction ability of the model for simultaneous determination of levodopa and benserazide hydrochloride. For this case, the predicted concentrations of each sample in calibration step were compared with the actual con-
mixtures
of
Levodopa
and
Benserazide
Found
Levodopa (M)
Benserazide hydrochloride (M)
Levodopa (M)
Benserazide hydrochloride (M)
6.4 8.0 3.2 8.0 5.6 3.2 5.6 3.2 5.6 3.2
5.6 5.6 5.6 8.8 8.8 1.6 4.0 4.0 5.6 8.8
6.4 8.4 3.0 8.5 5.6 3.2 5.9 3.2 5.4 3.3 4.5%
5.0 5.7 5.6 8.3 9.3 1.5 4.2 4.0 5.4 8.8 6.3%
centrations. The first statistical parameter is the root mean square difference (RMSD).
RMSD =
2 1 ˆ (Ci − Ci ) n n
0.5 (2)
i=1
This parameter is an expression of the average error in the analysis for each component in training samples. The second statistical parameter was the relative error of prediction (REP).
100 REP (%) = c¯
2 1 ˆ (Ci − Ci ) n n
0.5 (3)
i=1
That shows the predictive ability of each component. The predictive applicability of a regression model is described in various ways. The most general expression is the standard error of prediction (SEP) and standard error of calibration denoted by SEC.
SEP (SEC) =
n (ˆc i=1 i
− ci )
n−1
2
0.5 (4)
The square of the correlation coefficient (R2 ), which is indicated the quality of fit of all the data to a straight line is calculated for the checking of each.
n 2 (ˆci − c¯ ) R = ni=1 2 2
i=1
3.9. Statistical parameters
synthetic
Added
(1)
and designated as RSEC%, and RSEP% for the calibration, and prediction sets, respectively. PC-ANN architectures were constructed using a different number of PCs with one bias node for the input layer and the number of PCs to be included in the PC-ANN model was investigated. The RSEP% for each analyte reaches to a minimum when the number of PCs was 5 for levodopa and 4 for benserazide hydrochloride. In order to determine the optimum number of hidden nodes, a series of different topologies was used, in which the number of nodes was varied from 1 to 15. Each topology was repeated five times to avoid random correlation due to the random initialization of the weights. According to its generalization ability on the test set, the RSEP% of the test set of each topology was calculated. It was found that the lowest RSEP% value obtained when the number of hidden units was 5 for levodopa and 4 for benserazide. All other parameters of ANN with back-propagation learning algorithm including number of iterations, momentum, learning rate and transfer functions were optimized using minimum RSEP% values of the test set during the training process. The construction of optimized ANN model is summarized in Table 3. The results obtained for validation samples are given in Table 4. The reasonable relative errors for each analyte indicate the accuracy of the proposed method.
for
(ci − c¯ )
(5)
The statistical results (RMSD, REP, SEP, R2 ) are summarized in Table 5. 3.10. Analytical application The proposed method was successfully applied to the simultaneous determination of LD and BH in human serum. For the
30
J. Tashkhourian et al. / Spectrochimica Acta Part A 82 (2011) 25–30
Table 5 Statistical parameters obtained by applying the ANN methods.
References
Parameter
Levodopa
Benserazide hydrochloride
RMSD REP (%) SEP R2
0.033 0.75 0.056 0.997
0.058 0.99 0.073 0.994
Table 6 Prediction results for determination of BH and LD using the optimized neural network model. Sample
1 2 3 4 5
Spiked
Found
Levodopa (M)
Benserazide hydrochloride (M)
Levodopa (M)
Benserazide hydrochloride (M)
8.00 6.40 1.60 4.80 3.20 RSE (%)
9.60 3.20 1.60 6.40 1.60
8.07 6.4 1.63 4.75 3.23 5.3%
9.65 3.2 1.52 6.42 1.57 7.1%
determination of analyte in human serum, a certain amount of each the compounds was spiked into the sample without further treatment and their concentrations were determined by the proposed methods. The results are given in Table 6. The results show that there is a satisfactory agreement between the declared analyte content and the determined value. Therefore, the proposed method could be effectively used for the determination of LD and BH in real samples such as human serum. 4. Conclusion In this paper, we have investigated the chemistry, analytical methodology and the chemometrics interpretation of the results for the determination of levodopa and benserazide hydrochloride in synthetic and pharmaceutical samples by the kinetic–spectrophotometric method. The models were validated with the use of synthetic binary mixtures of levodopa and benserazide hydrochloride and the figures of merit, RSE%, were evaluated. The aims were: (1) to carry out a simultaneous determination of the levodopa and benserazide hydrochloride in biological samples, (2) representation ability of nanoparticles for determination of important analyte. Finally, the most successful BP-ANN calibration model was applied for the prediction of the levodopa and benserazide hydrochloride in synthetic and pharmaceutical samples with satisfactory results.
[1] A.R. Gennaro, Remington’s Pharmaceutical Sciences, 15th ed., Mach Publishing, Easton, PA, 1975, p. 858. [2] K. Ghose, Drugs Today 10 (1985) 463. [3] S.C. Sweetman (Ed.), Martindale: The Complete Drug Reference, 33rd, Pharmaceutical Press, London, UK, 2002, p. 1160. [4] J. Karpínska, J. Smyk, E. Wołyniec, Spectrochim. Acta A 62 (2005) 213. [5] K.L. Marques, J.L. Santos, J.A. Lopes, J.L. Lima, Anal. Sci. 24 (2008) 985. [6] D. Perez-Bendito, Analyst 115 (1990) 689. [7] S.R. Crouch, Anal. Chim. Acta 283 (1993) 453. [8] M. Otto, Analyst 115 (1990) 685. [9] T.F. Cullen, S.R. Crouch, Mikrochim. Acta 126 (1997) 1. [10] G. Absalan, M. Nekoeinia, Anal. Chim. Acta 531 (2005) 293. [11] Y. Ni, Z. Qi, S. Kokot, Chemometr. Intell. Lab. Syst. 82 (2006) 241. [12] Y. Ni, P. Qiu, S. Kokot, Anal. Chim. Acta 516 (2004) 7. [13] Y. Ni, C. Huang, S. Kokot, Anal. Chim. Acta 480 (2003) 53. [14] Y. Ni, C. Huang, S. Kokot, Chemometr. Intell. Lab. Syst. 71 (2004) 177. [15] Y. Ni, P. Qiu, S. Kokot, Anal. Chim. Acta 537 (2005) 321. [16] A. Safavi, O. Moradlou, S. Maesum, Talanta 62 (2004) 51. [17] I.A. Pettas, M.I. Karayannis, Anal. Chim. Acta 491 (2003) 219. [18] A. Safavi, H. Abdollahi, M.R. Hormozi Nezhad, Talanta 59 (2003) 515. [19] F. Marini, R. Bucci, A.L. Magri, A.D. Magri, Microchim. J. 88 (2008) 178. [20] G. Kateman, Chemometr. Intell. Lab. Syst. 19 (1993) 135. [21] J.R.M. Simits, W.J. Melssen, L.M.C. Buydens, G. Kateman, Chemometr. Intell. Lab. Syst. 22 (1994) 165. [22] P.J. Gemperline, J.R. Long, V.G. Gregoriou, Anal. Chem. 63 (1991) 2313. [23] C. Borggard, H.H. Thodberg, Anal. Chem. 64 (1992) 545. [24] P. Geladi, B.R. Kowalski, Anal. Chim. Acta 185 (1986) 19. [25] M. Blanco, J. Coello, H. Iturriaga, S. Maspoch, M. Redon, Anal. Chem. 67 (1995) 4477. [26] M. Blanco, J. Coello, H. Iturriaga, S. Maspoch, M. Redon, N. Villegas, Analyst 121 (1996) 395. [27] A.J. Haes, R.P. Van Duyne, J. Am. Chem. Soc. 124 (2002) 10596. [28] G. Kalyuzhny, M. Schneeweiss, A. Shanzer, A. Vaskevich, I. Rubinstein, J. Am. Chem. Soc. 123 (2001) 3177. [29] M. Malinsky, K. Kelly, G. Schatz, R. Van Duyne, J. Am. Chem. Soc. 123 (2001) 1471. [30] M. Reza Hormozi Nezhad, M. Alimohammadi, J. Tashkhourian, S. Mehdi Razavian, Spectrochim. Acta A 71 (2008) 199. [31] H. Park, J. Yoon, K. Kim, Langmuir 22 (2006) 1626. [32] Y. Yang, L. Xiong, J. Shi, M. Nogami, Nanotechnology 17 (2006) 2670. [33] I. Tanahashi, F. Yamazaki, K. Hamada, Chem. Lett. 35 (2006) 454. [34] S. Malynych, G. Chumanov, J. Opt. A: Pure Appl. Opt. 8 (2006) S144. [35] R. Jin, Y.C. Cao, E. Hao, G.S. Meˇıtraux, G.C. Schatz, C.A. Mirkin, Nature 425 (2003) 478. [36] M.R. Hormozi Nezhad, J. Tashkhourian, J. Khodaveisic, J. Iran. Chem. Soc. 7 (2010) S83. [37] M.R. Hormozi Nezhad, J. Tashkhourian, J. Khodaveisic, M.R. Khoshi, Anal. Methods 2 (2010) 1263. [38] J. Ling, Y. Sang, C.Z. Huang, J. Pharm. Biomed. Anal. 47 (2008) 860. [39] L.P. Wu, Y.F. Li, C.Z. Huang, Q. Zhang, Anal. Chem. 78 (2006) 5570. [40] J. Zupan, J. Gasteiger, Anal. Chim. Acta 248 (1991) 1. [41] J. Zupan, J. Gasteiger, Neural Network for Chemists: An Introduction, VCH, Weinheim, 1993. [42] F. Despangne, D.L. Massart, Analyst 123 (1998) 157R. [43] P.J. Gemperline, Chemometr. Intell. Lab. Syst. 15 (1992) 115. [44] S. Sekulic, M.B. Seasholtz, Z. Wang, B.R. Kowalski, S.E. Lee, B.R. Holt, Anal. Chem. 65 (1993) 835A.