Determination of Commercial Colorants in Different Water Bodies Using Partial Least Squares Regression (PLS): A Chemometric Study

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1 JJC Jordan Journal of Chemistry Vol. 3 o.3, 008, pp Determination of Commercial Colorants in Different Water Bodies Using Partial Least Squares Regression (PLS): A Chemometric Study Yahya S. Al-Degs a *, Amjad H. El-Sheikh a, Mohammad A. Al-Ghouti b, Mahmoud S. Sunjuk a a b Chemistry Department, The Hashemite University, P.O. Box 50459, Zarqa, Jordan. Industrial Chemistry center, Royal Scientific Society, P.O.Box 438, Amman, Jordan. Received on March 6, 008 Accepted on Sept., 008 Abstract A simple analytical method for the simultaneous determination of three commercial dyes (Basic Blue 9, E-4BA, and ) in water without prior separation of the solutes is presented. Partial Least Squares Regression (PLS-) was applied for the resolution of the high spectral overlapping between dyes. At the best modeling conditions, mean recoveries and relative standard deviations (R.S.D.) for dyes prediction by PLS- were found to be 0.3 (%), 95.7 (8.4%), and 94.9 (6.4%) for Basic Blue 9, E-4BA, and, respectively. Estimated limits of detection (LOD) were 0., 0.5, 0.49 mg L - for Basic Blue 9, E-4BA, and, respectively. Finally, quantitative determination of these commercial dyes spiked in real water samples was carried out successfully by the proposed method without the need for solutes separation. Keywords: Multivariate calibration; Spectral overlap; Partial-least-squares; Commercial dyes. Introduction atural and synthetic dyes are heavily used by many industries including food, pharmaceutical, cosmetic, textile and leather industries []. The effluents of these industries are highly colored and the disposal of these wastes into natural waters causes damage to the environment [-]. Dyes can be classified into three types according to their chemical structure: cationic, nonionic, and anionic []. Anionic dyes are the direct, acidic and reactive dyes [-3]. It is difficult to remove dyes from effluents since they are stable to light, heat and oxidizing agents and are biologically non-degradable []. Some classes of dyes are harmful to aquatic life even at low concentrations [-3]. It is pointed out that less than mg L - of dye content causes obvious water coloration []. Dye concentrations of 0 mg L - up to 5 mg L - have been cited as being present in dyehouse effluents [4]. After mixing with other water streams, the concentration of dyes is further diluted. The concentration of dyes could be in the µg L - level in wastewater [5]. In response to concerns regarding the health * Corresponding author. yahya@hu.edu.jo

2 risks associated with the use of reactive dyes, an increasing number of analytical methods have been developed in recent years for their determination. Several papers described the applications of new analytical procedures for the detection and determination of dyes in various matrices such as water, wastewater, urine, fish tissue, and animal feed [3,6]. Due to their high molar absorptivities (> 0,000 M - cm - ), most dyes can be simply quantified in solution after their separation from the rest of the sample. The recommended spectrophotometric procedure is simply based on the absorbance measurements at the wavelength corresponding to the maximum absorption of the dye [7]. The spectrophotometric determination of a mixture of dyes is a hard task in analytical chemistry due to the potential spectral interferences, which results in widely overlapped absorption bands [7]. In these cases, the conventional univariate calibration method is impractical, because of the contribution of one species to the absorption signals of other species, and vice versa. Recently, method development for resolution of highly overlapped spectra has increased exceptionally, because of the availability of powerful instrumentation and robust numerical methods. For example, derivative spectrophotometry [8], the H-point standard addition method [9], chemometric methods including classical least squares (CLS), principalcomponents regression (PCR), net analyte signal (AS), and partial least squares regression (PLS) [0-], all have been frequently employed to overcome the problems of interference due to spectral overlapping. A limited number of studies have been reported on using multivariate calibration for analysis of multi-component mixtures of dyes [5,7,3]. While the use of spectrophotometric techniques is preferred because of their relatively low operation cost, their application would be limited due to their modest sensitivity [4]. In this work, simultaneous determination of Basic Blue 9, E-4BA, and in water is described. The quantification of dyes in the mixture was performed by UV-Vis spectrophotometry using PLS- method. The figures of merit such as selectivity, sensitivity, limit of detection (LOD) and analytical sensitivity of the multivariate method were calculated. Quantitative determination of commercial dyes in different water bodies was carried out using the proposed PLS- calibration method. Theoretical Background The following notations were adopted in this work, boldface capital letters for matrices, e.g., A and C, superscript t for transposed matrices (or vectors), e.g., A t, boldface small characters for vectors, e.g., v and small characters for scalars, e.g., c. stands for the Euclidian norm for the vector. Solving Beer's law for multicomponent systems using multivariate calibration The Beer s law model for m calibration standards containing l chemical components with spectra of n digitized absorbances can be presented in matrix notation as [0] : 3

3 A = CK + E Or in matrix form, A A M A m A K A A m n A K A M n M K A mn C = C M C m C KC C C M m l KC l M KC ml K K M K l K K K M l KK n KK K K n M l n E + E M E m E E E M KE m n KE n M KE Where A is the m n absorption matrix of calibration spectra, C is the m l concentration matrix of components (dyes in this study) concentrations, K is the l n matrix of absorptivity constants or simply the calibration matrix. E is the m n matrix of spectral errors that not fit by the model. The elements of K-matrix can be determined by measuring spectra of individual components; however, this dose not take into account the interactions (or overlapping) between components. In literature, there are many chemometric techniques that effectively solved Equation and found the perfect relation between the absorbance and concentration. Among these calibration methods, the most employed methods in chemometric research are Classical Least Squares (CLS), Principal Component Regression (PCR), Partial Least Squares, and Kalman Filter (KF) were [3]. Determination of dyes concentrations in synthetic and real samples using PLS method was carried out according to the theories proposed in references [0,3]. mn () () PLS- calibration method Usually, multivariate calibration methods involve a calibration step in which the relationship between spectra and components concentrations is estimated from a set of calibration samples, and a prediction step in which the results of the calibration are used to predict the components concentrations in an unknown sample spectrum [0]. Partial least squares type (PLS-) is a factor analysis method which has many of the full spectrum advantages of its classical counterpart (CLS). The method keeps the advantages of inverse calibration, allowing us to carry out the analysis for one chemical component at a time [0]. In the PLS- type, all parameters are optimized for the determination of a single analyte of interest. In PLS-, the calibration spectra can be represented as: A = TP t + E R (3) where A is an m n matrix containing the spectra of m calibration samples obtained at n wavelengths, P is a n h matrix containing the full spectrum vectors (loadings), T is an m h matrix of intensities (or scores) in the new coordinate system defined by the h loading vectors, and E R is the m n matrix of spectral residuals not fitted by the optimal PLS- model. The loading vectors contained in P are usually determined by an iterative algorithm, which also provides a set of orthogonal weight loading factors that form the n h matrix W. The mutual relationship between A, P, T and W is expressed in the following equation: T = RW (P t W) (4) 33

4 In PLS-, the matrix T is related to concentration by an inverse regression step as shown in equation 5: c k = Tv + e c (5) where c k is the m vector of the concentrations of analyte k in the calibration samples, v is the h vector of coefficients relating the scores to the concentrations and e c collects the corresponding concentration residuals. In the prediction step, the spectrum r registered for an unknown sample is transformed into the sample score t r by: t r = (W t P) W T r (6) from which the concentration can be calculated as: c k,un = t t r v (7) where v is the vector of regression coefficient of Eq. (5). Figures of merits for the analytical method The selectivity, sensitivity and limit of determination can be calculated to study the quality of a given analytical method. Selectivity, can be expressed as [5] : SEL k = s * k / s k (8) where s * k is the projection of s k (the vector of the spectral sensitivities of compound k in pure form) onto the so-called net analyte signal space [5]. Sensitivity for analyte k can be calculated using the following equation [5] : SE k = s * k (9) Another important figure of merit is the limit of detection (LOD), which can be calculated using the following equation [6] : LOD k = 3 ε / s * k (0a) where ε is a measure of the instrumental noise. Experimental Instrumentation and Software The absorbance measurements were obtained using a double beam Unicam spectrophotometer (Cary 50 UV-Vis spectrophotometer). The UV-vis spectra were recorded over the wavelength range of nm and digitized absorbances were sampled at nm intervals and then transferred to a Pentium(IV) personal computer for subsequent analysis. The data treatment was carried out using MATLAB (version 7.0). The ph measurements were made with a WTW-Inolab (Germany) ph-meter using a companied glass electrode. Chemicals and Solutions Doubly distilled water and highly pure reagents were used for all preparations of the standard and sample solutions. The selected dyes-which have a wide application in many industries-were; C.I. Basic Blue 9, C.I. E-4BA, and 34

5 C.I.. Dyes materials of analytical grade (> 99.9) were purchased from Aldrich chemicals. The chemical structures of dyes are illustrated in figure. The employed dyes were heavily used in clothes dyeing. Standard stock solutions (0 0 3 mg L - ) of each dye were prepared individually by dissolving 0.00 (±0.00) g in doubly distilled water in a 00.0 cm 3 volumetric flask. Dilute solutions were prepared by the appropriate dilution of the stock solution in doubly distilled water. The ph adjustment was made with 0.5 M HO 3 or 0.5 M aoh. The ionic strength of dye solutions was adjusted using pure acl. All used reagents were of high purity. O H SO 3 a Cl O H H SO 3 a H ao 3 S ) C.I. O H SO 3 a SO 3 a O O H C H C C H H Cl Cl ) C.I. E-4BA (CH 3 ) S + (CH 3 ) Cl 3) C.I. Basic Blue 9 (or methylene blue) Figure. The chemical structures of commercial dyes 35

6 Preparation of calibration and prediction sets In any chemometric study, two sets (or more) of solutions are prepared; the calibration set and prediction (or validation) set. Calibration set is used to build the model, while the effectiveness of the proposed model for prediction is testified in the validation set. Due to the high spectral overlap (98% overlap) between dyes a large number of calibration samples were necessary. Typical one-compound calibration experiments (univariate calibration) were carried out to establish the concentration ranges for the determination in the mixture. Solutions of absorbance values higher than were diluted. 8 binary synthetic mixtures (at ph 6.0 and ionic strength of 0.0 M acl) of dyes were prepared. From these solutions, 0 solutions were selected as the calibration set and the remaining 8 solutions were kept as validation set. The compositions of calibration and prediction sets are presented in Table. To insure collinearity in absorption matrix A, the design for calibration set was based on four-level fractional factorial designs according to Brereton s procedure [7]. The prediction set was selected randomly. For best calibration results, the spectral region within the range ( nm) was chosen. The number of experimental points (λ) per spectrum is 60 where the spectrum is subdivided into nm intervals. Within this spectral region, maximum spectral information was available. Accordingly, the dimension of calibration absorption matrix (A) is 0 60, while the dimension for calibration concentration matrix (C) is 0 3. It is advisable that the concentration ranges of dyes in the prediction samples should cover the same space as that in calibration mixtures (Table ). Table. Composition of calibration and prediction sets used for building and testing the PLS- for simultaneous determination of dyes Calibration set * o. C.I. Basic Blue 9 C.I. E-4BA C.I

7 Calibration set * o. C.I. Basic Blue 9 C.I. E-4BA C.I Prediction set ** * Constructed according to four-level fractional factorial designs according to Brereton s procedure [7]. ** Randomly constructed. Analysis of commercial dyes spiked in real water samples atural water samples including sea, river, tap, and treated wastewater were collected from different locations. Samples of water (000 ml each) were obtained directly from source and stored in polyethylene bottles at 0 o C. Prior spiking of natural samples with dyes, all samples were filtered through a cellulose membrane filter (Millipore) of 0.45 mµ pore size to remove any suspended matter. Furthermore, the ph of water samples was adjusted to 6.0 using the diluted acid. The spectra of spiked solutions were recorded over the region nm and the obtained data were digitized and analyzed by the proposed PLS- method to determine the concentration for each dye. Results and discussion Separation of dyes in water or wastewater can be carried out successfully by high performance liquid chromatography (HPLC), liquid chromatography/mass spectrometry (LC-MS), capillary electrophoresis (CE), and gas chromatography/mass spectrometry (GC-MS) [5,6]. However, chromatographic determination of dyes in a mixture takes much more time and also previous separation and clean-up steps are essential because of spectral and chromatographic overlapping. Multivariate calibration methods such as principal component regression (PCR) and partial least squares (PLS) have been applied to highly overlapped spectra or chromatograms successfully [8-9]. The advantages of applying multivariate calibration methods were to minimize or eliminate sample preparation and also to avoid a preliminary separation step in complex matrices [9-0]. 37

8 Degrees of spectral overlap and importance of multivariate calibration Figure shows the electronic absorption spectra of dyes. As can be seen, an important degree of spectral overlap occurs between studied dyes. The degree of spectral overlap can be estimated using Eq. 0b: D t ( k k ) = t t kk kk (0b) D is a convenient measure of the degree of spectral overlap []. k and k are the column vectors of individual component absorptivities. The values of absorptivities (k and k ) were previously obtained from the corresponding absorbance values at each wavelength for each dye using Beer's law..4. Absorbance Wavelength 9nm) Figure. Absorption spectra of E-4BA of 8 mg L - (), of 4 mg L - (), and Basic Blue 9 of 3 mg L -. Spectra were recorded at ph 6.0 and 0.0 M acl. The spectral overlap values between dyes were: 70, 85, and 9% between Basic Blue, Basic Blue, and Reactive Blue, respectively. Relatively speaking, Basic Blue dye has a lower spectral overlap compare to the other two dyes. The maximum overlap was observed for Brilliant Blue, which may affect the calibration ability of PLS- for determination of this dye. Due to the significant spectral overlapping between dyes, the conventional calibration procedures would have a limited application for quantitative determination. Therefore, the simultaneous determination of these dyes requires (a) the use of a separation technique before their detection, or (b) the application of a chemometric technique (as PLS-) for the resolution of the binary system. The second option was chosen, owing to its simplicity, rapidity and low cost. 38

9 Optimization of experimental conditions affecting dyes absorption Many experimental conditions may affect the absorption characteristics of dyes, among these, ph and ionic strength of solution. The influence of ph on dyes absorption intensity was studied over a wide ph range (-) and at constant solution ionic strength (0. M acl). The results obtained for Basic Blue 9 and were presented in figure 3, which shows very small variations in absorption of over the studied ph range. While, a considerable reduction in Basic Blue 9 absorption intensity beyond ph 8. E-4BA dye showed identical behavior to that observed for (data not provided). The absorption intensities of dyes (at their corresponding λ max values) were not affected by solution ionic strength over the range of 0.0 M acl. Hence, the absorption spectra for dyes mixtures (total of 40 spectra including calibration, prediction and real samples) were recorded at ph 6.0 and 0.0 M acl. 0.8 Absorbance ph Figure 3. Influence of ph on the absorption of () Basic Blue 9 (4 mg L - and 0.0 M acl) and () (4 mg L - and 0.0 M acl). The absorbance values were recorded at the λ max for each dye. Univariate calibration The linear working concentration range of each individual dye was determined. These concentration ranges were useful in building both calibration and prediction sets. The linear concentration range of each dye was determined by regressing absorbance at the corresponding λ.max against concentration. Limit of detection (LOD) was taken as 3σ/slope where σ is the standard deviation of 0 measurements of the blank. The parameters of linear regression are given in Table (). Linearity was 39

10 observed between and 30.0 mg L - for both E-4BA and Reactive Blue. Basic Blue 9 had a lower detection limit mg L - and the reported linearity range was mg L -. The selected concentration ranges of dyes used in multi-component samples (Table ) were below or in the middle of their linear calibration curves; this latter was to avoid excessive absorbance of the mixtures []. Table. Analytical characteristics for single-component determination of dyes at their corresponding visible λ max Dye λ.max Slope r Linear range LOD (nm) (mg L - ) (mg L - ) * C.I. Basic Blue C.I. E-4BA C.I * Limit of detection = 3s B /m; s B = standard deviation of blank (n = 0); m = slope of calibration. Effective calibration by PLS- and selection of the optimum number of factors (h) The optimum number of factors (h) to be used within the PLS- algorithm is an important parameter to achieve better performance in prediction. This allows modeling of the system with the optimum amount of information, avoiding overfitting [0]. To avoid overfitting, leave-one-out cross validation procedure was adopted [0]. Simply, for m calibration set (0 solutions), the models were performed on m- calibration solutions, and use this calibration to predict the concentration in the sample left out. This process was repeated 0 times until each sample has been left out once [0,8]. The selection of spectral region for numerical analysis was carried by applying a moving window strategy to the calibration set itself as outlined elsewhere [0]. The predicted concentration for each sample is then compared with the true concentration of this reference sample. PRESS (prediction error sum of squares), which measures the difference between predicted concentration and true one, is then estimated for all calibration samples in the set. PRESS value is also calculated after each increment in h as follows [8] : PRESS = m i= i, pred Ci, act ) ( C () Where m, C pred, and C act are the total number of calibration samples, predicted concentration and the actual concentration of the dye respectively. The overall effectiveness of PLS- for prediction dyes in the validation set can be determined by calculating REP (relative error of prediction) values for each analyte as follows [8] : REP% = 00 n i= ( C i, pred n i= ( C C i, act ) i, act ) / () 330

11 Where n is the number of samples in prediction set (8 in this study). The root mean squares difference (RMSD) can give an indication of the average error of each analyte in the analysis. RMSD can be calculated for each dye in prediction samples by the following equation [9] : RMSD = n n i= ( C i C, pred i, act ) SEC(P), the standard error of calibration or prediction, whatever the case, was evaluated by [] : SEC( P) = n i= ( C i, pred C i, act ) (3) (4) n The square of the correlation coefficient (r ), which reflects the goodness of fit of all data to a line can be obtained from the following equation [9,3] : r n ( Ci, pred Ci, act ) = i= (5) n ( Ci, act C) i= C is the average analyte concentration in the prediction samples. The calibration parameters obtained from the internal validation (i.e., validation for the calibration set) are presented in Table 3. This Table also shows the figures of merit for the proposed method, according to net analyte signal calculations [6]. As can be seen, the obtained latent variables (factors) were higher than 3 for all dyes, indicating that the variability sources number in the presently studied system exceeds the number of studied analytes (3 analytes). Figure 4 depicted the relation between PRESS values with the number of factors obtained from PLS- calibration. As noted from Table 3, similar spectral regions were used in PLS- calibration for E-4BA and. The perfect spectral region for Basic Blue 9 was extended from 50 to 70 nm. High prediction ability of PLS- method for all dyes in calibration samples as indicated from r and REP% values. Usually, the multivariate selectivity values ranges between zero (complete overlap between analyte and other analytes or interferences) and unity (no or small overlap between analyte and other analytes or interferences) [4]. Therefore, the lower selectivities obtained for dyes ( ) were expected due to the high spectral overlap. In their mixture, the multivariate LODs were 0., 0.5 and 0.49 for Basic Blue 9, E-4BA, and Reactive Blue, respectively. The optimized PLS- calibration method proposed for dyes was used to estimate their concentrations in synthetic mixtures (Table ). The mean recovery percentages, R.S.D., SEP, and REP% obtained for prediction set were presented in Table 4. The results were satisfactory indicating the successful application of the proposed method for simultaneous determination of the three dyes 33

12 in water. Relatively speaking, the PLS- method was more effective in internal validation compared to external validation as indicated from the values of REP% for both sets. Comparison between SEC and SEP values allows identification of an overfitting or underfitting in the model, with more or less factors than necessarily required [3]. For each dye, the magnitude of SEC and SEP were fairly similar which confirmed the perfect selection of the number of factors in both cases. As new techniques, multivariate calibration usually validated against well-developed methods like HPLC and other related methods. In a previous study, the authors analyzed five dyes using PCR calibration method and validated their results by applying HPLC as an independent analytical method [5]. In fact, separation of the current dyes-system was too hard and this was attributed to different charges of dyes (two dyes anionic and the other cationic). 30 Basic Blue 5 PRESS umber of PLS factors Figure 4. Plot of PRESS of cross-validation for dyes as a function of the number of factors (h) obtained by PLS- calibration 33

13 Table 3. Internal calibration results, statistical parameters and the figures of merit for the proposed PLS- method Calibration parameter C.I. Basic Blue 9 C.I. E-4BA C.I. Spectral region (nm) umber of factors (h) PRESS (mg L - ) RMSD (mg L - ) REP% SEC ρ SE SEL LOD * (mg L - ) * Assuming that ε = absorbance units Table 4. Statistical parameters estimated during the external validation of the PLS- calibration method proposed for dyes mixtures o. Added (mg L - ) Found by PLS- (mg L - ) Basic Blue Basic Blue Statistical analysis Average recovery±r.s.d. (n = 8) SEP REP% 0.3 () (8.4) (6.4) Determination of dyes mixtures spiked in different real water samples using PLS- calibration method Obviously, the next step was to determine how well the proposed PLS- method do when applied to the analysis of real wastewater containing dyes, considering that fact that the calibration set was designed using standards where other interferences were not accounted. As stated earlier, the main aim of this study was to develop an analytical method with a minimal sample preparation and find a simple analytical method to replace the current analytical methods [6]. Twelve ternary mixtures with variable concentrations of dyes were prepared using the natural water samples. The concentration levels of added dyes were selected to be within their linear ranges obtained from the univariate studies (Table ). The recovery values obtained in the analysis of the spectral data of the mixtures were summarized in Table 5. The recoveries obtained were satisfactory in all the samples analyzed where the values of R.S.D. were less than 0%. The prediction power of PLS- can be considered acceptable taking into account the complexity of the sample being 333

14 analyzed. The good agreement between the obtained results using PLS- and the spiked values is an indication of the effectiveness of the proposed method for simultaneous determination of Basic Blue 9, E-4BA, and in real samples. In fact, other substances that present in natural water samples which where not considered in the calibration model, e.g., Cl, SO 4, O 3, OM, etc., did not strongly interfere with dyes analysis. Table 5. Analytical results for the determination of dyes in different water bodies by UV-Vis spectrometry coupled with PLS- calibration method Water body no. Mixture Added 5 Found 5 Recovery(R.S.D.%) 6 Tap water Basic Blue (.3) 95.3 (.) 00.4 (.9) Basic Blue 3 Basic Blue River water 4 Basic Blue 5 Basic Blue 6 Basic Blue Sea water 3 7 Basic Blue 8 Basic Blue 9 Basic Blue Textile treated 0 Basic Blue wastewater 4 Basic Blue Basic Blue (.6) 0.3 (.) 00. (.6) 99.6 (.) 98.3(.8) 96.8 (.5) 95.6(.) 9 (.5) 99.6 (5.) 93.7 (0.6) 00.4(3.6) 0.3 (5.0) 94.7(.5) 94.8(.6) 95.6(3.8) 94.(7.) 99.0 (9.6) 96.7 (5.) 05.0(8.7) 00.8(5.) 04.7(3.5) 0(8.5) 95.6(5.9) 96.3(6.5) 04.4(.5) 90.0(5.0) 98.3(9.5) 89.3(5.) 94.(9.5) 9.(0.0) 90.3(5.0) 03.(9.9) 0.(9.7) 4.6. The employed tap water contains (in mg L - ): [Cl ] = 30, [SO 4 ] = 75, [O 3 ] = 5, [F ] = 0., total hardness (as CaCO 3 ) = 400, and organic matter = < 5.. The employed river water contains: [Cl ] = 0. M, total hardness (as CaCO 3 ) = 680 mg L -, and alkalinity (as CaCO 3 ) = 75 mg L - 3. The employed sea water contains: [Cl ] = 0.45 M, total hardness (as CaCO 3 ) = 850 mg L -, alkalinity (as CaCO 3 ) = 0 mg L - 4. The employed textile treated wastewater contains: total hardness (as CaCO 3 ) = 550 mg L -. [SO 4 ] = 0 mg L -, [O 3 ] = 45 mg L -, BOD = 3 mg L -, COD = 0 mg L Expressed in mg L Relative standard deviation (n = 3). 334

15 Conclusions The proposed method was shown to be a rapid, easy and of low cost for the quantification of three commercial dyes (two anionic dyes and one cationic) in water using UV-Vis spectrophotometry and PLS- calibration. It could be used for the screening of these dyes in water (e.g., in situ analyses) or as a quantification method in cases where the chromatographic ones cannot be implemented owing to cost limitations, lack of analytical instrumentation. Quantitative determination of the three dyes in real water samples was carried out successfully by the proposed method with recoveries ranging between with R.S.D. values 0% Acknowledgements The financial support from the Hashemite university/deanship of academic research is gratefully acknowledged. References [] Konduru, R.; Viraraghavan, T., Dye removal using low cost adsorbents., Wat. Sci. Technol., 997, 36, 89. [] Lambert, S.; Graham,.; Sollars, C.; Fowler, G., Evaluation of inorganic adsorbents for the removal of problematic textile dyes and pesticides., Wat. Sci. Technol., 997, 36, 73. [3] Mottaleb, M.; Littlejohn, D., Application of an HPLC-FTIR modified thermospary interface for analysis of dye samples, Ana. Sci., 00, 7, 49. [4] O eill, C.; Hawkes, F.; Hawkes, D.; Lourenço,.; Pinheiro H.; Delée, W., Colour in textile effluents sources, measurement, discharge consents and simulation: a review J. Chem. Technol. Biotechnol., 999, 74, 009. [5] Şahin, S.; Demir, C.; Güçer, Ş, Simultaneous UV vis spectrophotometric determination of disperse dyes in textile wastewater by partial least squares and principal component regression, Dyes and Pigments, 006, 73, 368. [6] Hansa, A.; Pillay, V.; Buckley, C., Analysis of reactive dyes using high performance capillary electrophoresis. Wat Sci. Technol., 999, 39, 69. [7] Peralta-Zamora, P., Kunz, A., agata,., Poppi, R. (998) Spectrophotometric determination of organic dye mixtures by using multivariate calibration Talanta, 47, 77. [8] evado, J.; Cabanillas, C.; Salcedo, A., Simultaneous spectrophotometric determination of three food dyes by using the first derivative of ratio spectra, Talanta, 995, 4, 043. [9] Reig, F.; Falcó, P., H-point standard additions method. Part. Fundamentals and application to analytical spectroscopy., Analyst, 988, 3, 0. [0] Haaland, D.; Thomas, E., Partial least-squares methods for spectral analyses.. Relation to other quantitative calibration methods and the extraction of qualitative information., Anal. Chem., 988, 60,93. [] Geladi, P., Chemometrics in spectroscopy. Part. Classical chemometrics., Spectrochimica Acta B., 003, 58, 767. [] Hemmateenejad, B.; Safarpour, M.; Mehranpour, A., et analyte signal artificial neural network (AS A) model for efficient nonlinear multivariate calibration., Anal. Chem. Acta, 005, 535, 75. [3] López-de-Alba, P.; López-Martínez, L.; Cerdá, V.; De-León-Rodríguez, L., Simultaneous determination of tartrazine, sunset yellow and allura red in commercial soft drinks by multivariate spectral analysis., Quimica Analitica, 00, 0, 63. [4] López-de-Alba, P.; López-Martínez, L.; De-León-Rodríguez, L., Simultaneous determination of synthetic dyes tartrazine, allura red and sunset yellow by differential pulse polarography and partial least squares. A multivariate calibration method., Electroanalysis, 00, 4, 97. [5] Lorber, A., Error Propagation and figure of merit for quantification by solving matrix equations., Anal. Chem., 986, 58, 67. [6] Espinosa-Mansilla, A.; Muñoz de la Peña, A.; Salinas, F.; González Gómez, D., Partial least squares multicomponent fluorimetric determination of fluoroquinolones in human urine samples., Talanta, 004, 6, 853. [7] Brereton, R., Multilevel multifactor designs for multivariate calibration, Analyst, 997,,

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