Spectrophotometric Determination of Melamine in ...

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Spectrophotometric Determination of Melamine in Liquid Milk by Multivariate Second Order Calibration ARTICLE in CURRENT ANALYTICAL CHEMISTRY · JANUARY 2016 Impact Factor: 1.13

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Spectrophotometric Determination of Melamine in Liquid Milk by Multivariate Second Order Calibration Ramia Z. Al Bakain1, Yahya S. Al-Degs2,*, Amjad H. El-Sheikh2 and Sharif H. Arar1 1

Department of Chemistry, Faculty of Science, The University of Jordan, P.O. Box 11942, Amman, Jordan; 2Chemistry Department, Hashemite University, P.O. Box 150459, Zarqa13115, Jordan Abstract: Melamine was quantified in liquid milk without performing the usual liquid-liquid or solidphase extraction steps. Following a simple clean up procedure (extraction by acetonitrile/water mixture, centrifugation and filtration by 0.45 m membrane), melamine was accurately quantified in fullfat liquid milk down to 0.057 mg kg-1 using second order multivariate calibration. Second order data was created from absorbance-pH measurements (batch-experiment method) taking the advantage of acid/base properties of melamine. The study was carried out over the spectral domain 200-250 nm and pH 2.0-8.0. Although the number of pH points was lower than the spectral points (4 vs. 51), the data was satisfactorily analyzed by second order multivariate calibration methods. Quantification of melamine in spiked milk samples (0.5-2.0 mg kg-1) is carried out using unfolded partial least squares-residual bilinearization U-PLS/RBL, parallel factor analysis PARAFAC and multivariate curve resolution-alternating least squares MCR-ALS. U-PLS/RBL outperformed other methods with mean recoveries and relative errors of prediction of 100.1 (2.3), 99.3 (6.2), and 93.8 (16.4) for U-PLS/RBL, MCR-ALS, and PARAFAC, respectively. The reason for success of U-PLS/RBL is attributed to its effectiveness for resolving melamine-signal (the basic form) from the signal of milk-extract. PARAFAC and MCR-ALS showed a modest performance and this is attributed to the rank-deficiency in the collected matrices (for test samples), linear dependency (in pH-dimension), and intense spectral-overlapping in the spectral mode. The performance of the proposed method is critically compared with other published methods.

Keywords: Melamine, matrix effect, pH-spectra, PARAFAC, MCR-ALS, U-PLS/RBL. 1. INTRODCUTION Since the introduction of multivariate calibration methods in the field of analytical chemistry, the problem of signals overlapping has been reduced due to the high resolving power of various calibration methods [1, 2]. In contrast to univariate calibration, multivariate calibration correlates multiple independent variables with multiple dependent variables [3]. Methods of multivariate calibration are applied using first and second order data [2]. However, higher order data were recently reported [2, 4]. Recording a spectrum for a given sample represents a first-order data, while recording chromatograms at different wavelengths for a sample represents a second-order data [4]. Second-order multivariate analysis has been gaining popularity in analytical applications since it can compensate for the effects of uncalibrated interferences that are not considered in the initial calibration stage [2, 4, 5]. The earlier property is universally known as "second-order advantage" [4, 5]. For second-order data, a data matrix is recorded per sample and thus a three-way data is obtained by staking all matrices for a set of samples [2, 4]. Second order data are often generated by “hyphenated” instruments including liquid chromatography-mass spectrometry (LC-MS), gas *Address correspondence to this author at the Chemistry Department, Hashemite University, P.O. Box 150459, Zarqa13115, Jordan; Tel: +962 53903333; Fax: +962 53903349; E-mail: [email protected]

17-/16 $58.00+.00

chromatography-mass spectrometry (GC-MS) and high performance liquid chromatography-photodiode array detector (HPLC-DAD) [6-8]. Excitation-emission fluorescence spectra, UV/Vis absorbance-pH, absorbance-time, fluorescencepH spectra are all common forms of second order data [2, 4]. An important criterion for selecting the proper algorithm for processing second-order data and achieving second-order advantage is the trilinearity condition [9]. Generally speaking, if the three-way data is trilinear then a trilinear decomposition algorithm such as parallel factor analysis (PARAFAC) is conveniently applied [10]. When secondorder data deviates from trilinearity, then special algorithms are recommended such as multivariate curve resolutionalternating least squares (MCR-ALS) [9]. An essential condition for applying MCR-ALS is that all data matrices should be bilinear [9]. Unfolded partial least squares (UPLS) and multiway partial least squares (N-PLS) combined with residual bilinearization were found applicable for solute prediction in complex matrices and hence showing secondorder advantage [8, 10, 11]. When pH is a dimension in tridimensional data array, as the case in the current work, then two challenges would appear for second-order calibration methods: a) linear dependency due to the concentration– dependence relationship between pH-equilibrating species in the sample, and b) possible lack of reproducibility in the pH dimension from sample to sample [4, 10]. Handling the earlier analytical problems is possible by using the robust MCR-ALS, which works after matrix augmentation in the © 2016 Bentham Science Publishers

Spectrophotometric Determination of Melamine

Current Analytical Chemistry, 2016, Vol. 12, No. 1

trilinearity-breaking mode [4]. Limited studies were reported on modeling three-way data of pH-dimension, where the majority of studies concentrated on excitation-emission or HPLC-DAD data matrices [4, 7-10, 12]. Quantitative determination of three fluoroquinolone antibiotics in spiked human urine samples based on flow-injection pH-modulated synchronous fluorescence data matrices was reported [13].

method), spectral domain 200-250 nm, and concentration range 0.25 to 4.50 mg L-1. PARAFAC, U-PLS/RBL and MCR-ALS are selected to model second order data and finally quantify melamine in complex milk extract. It is known that second-order data of pH-dimension suffers from rankdeficiency and liner-dependency and the analyst should select the proper algorithm or conditions to model such data.

In recent years, more attention was given to melamine contamination and its unwanted consequences on human. Melamine is a nitrogen-rich chemical that is used to make plastic and has recently received wide spread media coverage after being discovered in some Chinese pet food [14]. Officials of the U.S. Food and Drug Administration (FDA) reported that melamine was deliberately added in pet food to fake higher protein levels. The presence of melamine and its related compounds in milk, feed, and other foods has resulted in the need for reliable methods for the detection and accurate quantification of this class of contaminants. The sample pretreatment for melamine in a complex matrix usually involves liquid extraction by a polar solvent, followed by a further clean-up with solid-phase extraction [14-16]. For milk and dairy products, defatting and protein precipitation followed by solid-phase purification are essential to get the best detection of melamine [14]. The separation and quantification of melamine are not straightforward tasks due to its high solubility in common solvents, moreover, the reported chromatographic and extraction protocols not only require sophisticated instruments but are also time-consuming and are not available in all sites. Thus, in view of the increasing adulteration of foodstuffs by melamine, it becomes a priority to develop new, simple, rapid, accurate, and cost-effective methods for quantification of melamine in different foodstuffs.

2. MATERIALS AND METHODS 2.1. Chemicals, Reagents and Instruments All chemicals employed in this work were of analytical grade and used as received. All solutions were prepared using doubly distilled water. All chemicals were purchased from Aldrich®. Acid-base reaction of melamine is shown in (Fig. 1). The stock solution of melamine (400 mg L-1) was freshly prepared using 50% (v/v) acetonitrile/water mixture and stored in a cold place. Standard working solutions of lower concentrations were prepared by appropriate dilution of the earlier stock solution using acetonitrile as diluent. A set of buffer solutions (pH 2.0, 4.0, 6.0, and 8.0) of 0.01 M were prepared from Na2HPO4 (0.01 M) and H3PO4 (0.01 M). A Cary 3E UV–Vis spectrophotometer (Varian, Australia) was used for spectral measurements. pH was measured using a digital instrument (Weilheim, Germany) combined with glass electrode (uncertainty ±0.01 unit). 2.2. Calibration Set Calibration samples at pH 2.0, 4.0, 6.0, and 8.0 were prepared from the stock solutions using buffer as diluent. The samples for spectral-analysis were prepared by mixing 2.0 mL of 0.01 M buffer with appropriate volume of melamine standard soltuion and then the final mixture was diluted with water to a final volume of 5.0 mL. Twelve standard solutions were prepared at pH 2.0, 4.0, 6.0, and 8.0 following the outlined procedure. The final concentrations of melamine in the calibration set were 0.25, 0.45, 0.85, 1.00, 1.40, 1.75, 2.00, 2.50, 3.00, 3.50, 4.00, and 4.50 mg L-1. Before spectral measurements, the pH for each solution was measured and adjusted if necessary.

Melamine is a strong UV absorber compound and absorbance is pH dependent, which opens the possibility of producing second-order data by collected UV-spectra at various pH values. In some studies, pH changes were obtained in batch experiments using different buffers [7, 11, 12]. In other studies, spectral data was conveniently recorded in flow systems in which titration was performed under identical run-torun experimental conditions [2, 9, 10, 12]. In the earlier flow studies, pH changes in the analytical system were monitored in a controlled and reproducible way [10]. Using automated flow systems, much more data points in pH dimension were measured, which offered more analytical sensitivity [2, 10].

2.3. Preparation of Milk Samples: Validation Set Ten melamine-free-liquid milk samples were purchased from different supermarkets and stored in a cold place before use. The samples have different sizes (250-500 mL) and supplied by different dealers. The samples have the same level of fat (3% by wt.) and protein (3% by wt.) as claimed by the manufacturers. All samples have a natural-taste and were produced from cow milk. A representative milk sample was generated by mixing 100 mL of each sample in a 2.0 L

In this work, second-order multivariate calibration is applied for melamine quantification in milk extract with minimum sample clean up (acetonitrile/water extraction, centrifugation and filtration by 0.45 m membrane). No liquidliquid or solid-liquid purifications are employed. The study is performed in the pH range 2.0-8.0 (using batch experiment

NH2

NH2 N

H2N

N

N

+ NH2

H2O

75

N

H+ OH-

Melamine (deprotonated form) Fig. (1). Acid-base equilibrium of melamine (pKa of conjugate acid is 5.05).

H2N

N

+

N H

NH2

Melamine (protonated form)

76 Current Analytical Chemistry, 2016, Vol. 12, No. 1

Al Bakain et al.

beaker. The final mixture was homogenized before use. Appropriate volumes were taken from the earlier mixture and directly spiked with melamine standard solution to variable levels. A 5.00 g milk sample was added to 15 mL of extractant (acetonitrile: water 1:1 ratio) and the mixture was sonicated for 3.0 min and then centrifuged at 6000 rpm for 4.0 min. The clear supernatant was filtered through 0.45 m cellulose membrane and diluted with water to a final volume of 20.0 mL in preparation for spectral measurements. Preparation of test samples for spectral analysis was carried out in a similar manner to that applied for calibration set section. The final melamine content in the spiked milk samples (12 samples) were within the range 0.50-2.00 mg kg-1. Certain samples were prepared in triplicate to assess the accuracy and precision of the proposed analytical method. The level of melamine spiking was selected to be close to the safe limit set by food standards, i.e. less than 2.5 mg kg-1 for liquid milk [14].

PARAFAC factors N is equal to the number of active components. For non-trilinear array, N is larger than the actual number of responsive species. The optimum number of PARAFAC factors is often estimated by different methods [9]. Modeling trilinear data by PARAFAC implies that a unique solution of Eq.1 is achieved and a single set of A, B, and C matrices satisfy Eq.1. For the current analytical system, loadings of B and C matrices should manifest the spectral profile and acid/base distribution of melamine over the studied experimental modes, respectively. However, scores collected in matrix A are proportional to melamine concentration in calibration samples. The most important analytical property of PARAFAC model is known as "second-order advantage", where the solute of interest is accurately quantified even in the presence of uncalibrated/unexpected constituents [2, 4, 7].

2.4. Spectral Measurements and Arrangement of Data

For some reasons, trilinear data would deviates from trilinearity making PARAFAC invalid in certain cases. When profiles of the calibrated solutes change from sample to sample, a non-trilinear data is generated. Chromatographicspectral data, pH-spectral data, and kinetic-spectral data are reported to show deviation from trilinearity [10, 12]. For non-trilinear data, an augmented matrix in the mode that breaking trilinearity (pH in our system) is created. Matrix augmentation is simply generated by unfolding data matrices in the direction of mode of interest. The augmented matrix Xaug has the dimension IKJ, where K is the number of data points in pH dimension for each of the I data matrices [4, 9].

pH-spectral data matrices were generated using simple batch experiment as outlined in the literature [7, 11]. Spectral measurements were recorded at pH 2.0, 4.0, 6.0, and 8.0 and over the spectral range 200-300 nm at interval of 1.0 nm. For each solution, a spectrum containing 51 points (analysis was limited to 200-250 nm range as will be discussed later) was obtained at each pH. Second order data of the samples was created by collecting the recorded spectra at pH 2.0, 4.0, 6.0, and 8.0 in one single matrix (i.e., data matrix per sample). Accordingly, a data matrix of 451 was obtained for each sample (4 and 51 represent pH and absorbance data points in each mode, respectively). The three-way data (I Samples– J Absorbance– K pH) were arranged as following: Calibration set: 12514 (IJK) and validation or test samples: 12514 (IJK). 2.5. MVC2 Program and Adopted Algorithms Among the second-order calibration methods, PARAFAC, U-PLS/RBL, and MCR-ALS were selected. A comprehensive review on second-order calibration methods including algorithms, applications to different matrixes and software are available in the literature [2, 4, 17]. A short summary on the algorithms is provided in this section. 2.5.1 Parallel Factor Analysis PARAFAC is often employed for modeling true trilinear three way data (or higher) and it is a generalization for principal component analysis to higher order data [2, 4]. Three way data were obtained by stacking absorbance spectra–pH data matrices for a set of calibration samples. For a trilinear three way data, following expression is presented [10]:

2.5.2. Multivariate Curve Resolution – Alternating Least Squares

N

xaug ,mj = baug ,mn c jn + emj

(2)

n=1

where xaug,mj is a generic element of X aug (m from 1 to IK), baug,mn is an element of the augmented profile for solute n in the augmented direction, cjn an element of the profile in the direction for solute n and this profile is common for all samples. emj is an element of the matrix of residuals [10]. The elements baug,mn and cjn are often collected into a matrix Baug of augmented profiles and a matrix C of normalized profiles. The bilinear decomposition of Xaug to find the proper set of Baug and C matrixes is carried out using MCRALS algorithm [9, 10]. The pure solute content is contained in the areas under each of the Baug profiles for each of the participating samples. A univariate plot is generated by regressing the areas under the profiles for a certain analyte vs. its nominal content in calibration samples [4]. MCRALS is a robust algorithm as it shows "second-order advantage" and could handle both trilinear and non-trilinear data [8].

(1)

2.5.3. Unfolded Partial Least-squares and Residual Bilinearization

where ain represents the relative concentration (also known as score) of n solute in the i-th sample, bjn and ckn are the responses (also known as loadings) in both of the instrumental modes j and k, respectively. The intercept in Eq.8 estimates the errors not accounted by PARAFAC model. Usually, loadings are collected in B and C matrices, of sizes JN and KN, respectively. For trilinear array, number of

Unfolded partial least-squares U-PLS operates in a similar way to the common PLS, except that second order data are first unfolded alone one of the data dimensions [10]. After unfolding, the conventional PLS is applied to model the unfolded spectral data and the nominal concentrations of the target solute [10]. The first step in U-PLS/RBL regression is to convert calibration matrices Xcal,i (size JK) into vectors xcal,i of dimension JK1 [9, 10]. A matrix which is suitable

N

xijk =

a

b c + eijk

in jn kn

n=1



for PLS calibration is created by placing all the columns vectors adjacent to each other [10]: XPLS = [Xcal,1 |Xcal,2 |… |Xcal,I]

(3)

XPLS is used for PLS calibration and has the dimension JKI (I = number of calibration samples). Decomposition of the earlier matrix by PLS is achieved as: XPLS = PTT + EPLS

(4)

Where P and T are the PLS-loadings and PLS-scores of XPLS, respectively. EPLS collects the residuals and has the same dimension of XPLS. T stands for matrix transpose. The P and T matrices are estimated via an iterative algorithm, whose aim is to explain the variance in XPLS as well as its covariance with the nominal solute concentration in the calibration samples [10]. The dimensions of P and T are JKA and IA, respectively, where A is the PLS-latent variables needed to build the model and often estimated by applying leave-one-out crossvalidation technique [12]. Once PLS model is created, prediction of analyte concentration (melamine in this work) in interferences-free test sample is carried out as [10]: y = v Tt

(5)

where y, v and t are solute content in new sample, vector of regression coefficients, and the score vector of test sample. t is often estimated in a separate step by projecting the unfolded matrix of test sample Xtest in the space defined by the calibration PLS loadings. However, when unexpected constituents occur in the test data matrix Xtest, the sample score vector is unsuitable for predicting analyte through Eq 5. In a separate step, RBL is used to find a filtered score vector tRBL and this achieved by modeling the residuals (the part of the test sample unexplained by PLS) assuming that they can be arranged into a bilinear matrix [10]. In fact, RBL procedure fits the sample data to the sum of two contributions: (1) the part of the test data that explained by PLS loadings, and (2) the contribution from the potential interferents that modeled by a certain number of principal components NRBL [9, 10]. tRBL, the new score vector of test sample, is obtained from the following equation [10]: Xtest = reshape(PtRBL) + BRBLTTRBL + ERBL

(6)

Where reshape stands for brining back the matrix from the vectors. BRBLTTRBL is the PCA model for the residual matrix (Xtest- reshape(PtRBL)) using NRBL principal components. The final score vector tRBL is retrieved by minimizing ERBL in Eq. 6 as outlined elsewhere [10]. Finally, the content of target analyte is estimated from Eq. 5 using tRBL. Recently, a Matlab® toolbox was provided to the analytical chemists for running second order multivariate calibration methods [17]. The toolbox, known as MVC2, is easy to implement and contain the common second order algorithms. A special routine based on the concept of net-analyte signal is available for estimation figures of merit. The program has the ability to display 3D plots beside many graphical presentations related to the calibration methods. 3. RESULTS AND DISCUSSION 3.1. Spectral Behavior of Melamine and Milk-extract Over pH It was interesting to discuss the type and number of chemical species that would be present in the current ana-

77

lytical system. Melamine possesses some basic sites (pKa for conjugated acid is 5.05 [18] and thus is expected to dissociate in water. The protonated and deprotonated forms of melamine should differ in their electronic density distribution and should have distinct dipole moment and electronic structure. Accordingly, the electronic structure will be reflected on the pH dependence of UV-spectrum of protonated and deprotonated forms in solution. The pH widow (2.0-8.0) was selected in a way so that both forms of melamine were considered. Principal component analysis (PCA) of pure melamine pH-spectral data indicated that only two components were necessary to explain the variability in the data. From the earlier discussion, one would reach to the fact that two distinct chemical species are present in the calibration samples. Before analyzing pH-spectral data by second-order multivariate calibration, diagnosis of melamine and the milk-extract at different pHs was necessary. UV-signals of melamine and milk-extract are summarized in (Fig. 2). In fact, melamine is a strong UV-absorber compound and effectively absorbs over the range 200-250 nm with an optimum wavelength for quantitative analysis centered at 238.0 nm (Fig. 2A). Light absorption of melamine was strongly dependent on pH as indicated in (Fig. 2A). Better light absorption was observed at acidic solution. As shown in (Fig. 2A), melamine exhibited different spectral behavior for protonated and deprotonated forms. An intense spectral overlap between melamine and milk extract signals was encountered where milk-extract also absorbed over the range 200-250 nm (Fig. 2B); this made univariate calibration inapplicable. Using the method of Goicoechea and Olivieri [19], the spectral overlap between melamine and milk-extract signals at pH 2.0 was more than 97%. The test samples contain, in addition to melamine, the responsive background that is derived from milk-extract. It is rather a hard task to determine the exact number of UVabsorber species in complex milk extract and weather they have equilibrium or even spectral changes at certain pH. It is highly possible that the remaining fats/proteins/vitamins in the final milk-extract are representing the main responsivecompounds. Fig. (2C) shows a typical spectrum for milkextract (derived from ten cow milk samples) and registered at the acidic and basic limits considered in this study. As indicated in (Fig. 2C), the intensity of milk-extract background increases with pH with no change in the spectral shape. Principal component analysis of the pH-spectral data matrices of milk-extract confirmed that a single mathematical component is enough to explain the variability in the data. Accordingly, the responsive background of milk extract could be considered as one distinct component. Another important point in (Fig. 2C) is the similarity between the spectral shape of milk-extract and the deprotonated form of melamine (Fig. 2A) and this will present a hard task for second-order methods as will be shown later. The best combination between spectral and pH measurements would be deduced from contour plots as shown in (Fig. 3). Fig. (3) shows the counter plot recorded over 200-300 nm and pH 2.0-8.0 for a test sample. The bar located on the right side of (Fig. 3) indicates the intensity of light absorption over the two experimental modes. Investigating con-


A

0.40

Melamine (1.1mg/l) (pH 2)

0.40

pH 8

0.30

pH 2

0.20 0.10 0.00

B

0.50

Absorbance

Absorbance

Al Bakain et al.

Milk extract (pH 2)

0.30 0.20 0.10

200

220

240

260

0.00

280

200

220

Wavelength (nm)

240

260

280

Wavelength (nm)

0.50

C

Absorbance

0.40 pH 2.0

0.30

pH 8.0

0.20 0.10 0.00

200

225

250

275

300

Wavelength (nm)

Fig. (2). UV-absorption spectra of melamine and milk-extract at different pHs: (A) standard melamine solution 1.10 mg L-1, (B) intense spectral overlap between melamine and milk-extract at pH 2, and (C) melamine-free-milk extract. 300 290

0.5

280

Wavelength (nm)

270

0.4

260 0.3

250 240

0.2

230 220

0.1

210 200

2

4

6

8

0

pH

Fig. (3). Contour plot of pH-absorbance spectra for a test sample containing 1.75 mg L-1 melamine.

tour plots is essential to adjust the domain of experimental variables before running second-order calibration. As indicated in (Fig. 3), poor light-absorption over 250–300 nm was observed at all pHs. Moreover, it is important to include all pHs in analysis because melamine and milkextract absorb over the studied wavelength range. The maximum absorption was located at 205-230 nm and around pH 6. For better sensitivity, the following ranges were included in multivariate analysis, 200-250 nm and 2.0-8.0 for spectral and pH dimensions, respectively. It should be mentioned that data points in pH and spectral modes were 4 and 51, respectively. Running second-order multivariate calibration with few data points in pH dimensions were not often reported [7, 11, 12].

3.2. Calibration by Second Order Methods For accurate melamine quantification in liquid milk, the following procedures were adopted in the literature: a) simple sample clean up or solid-phase extraction of melamine from milk extract followed by HPLC-DAD [18, 20], and b) applying second-order multivariate calibration after simple sample clean-up and avoiding laborious liquidchromatography [21]. Application of second-order calibration methods should be better due to their ability for handling unexpected interferences (a property known as secondorder advantage [4] and most importantly avoiding chromatographic methods. Three common second-order methods that showing "second-order advantage" were applied to de-



tect melamine in milk-extract: PARAFAC, MCR-ALS, and U-PLS/RBL. The earlier methods were tested for analyzing three-dimensional data array suffering from "linear dependency" and "rank-deficiency" [13].

for the first three PARAFAC factors. Accordingly, two factors appeared to be reasonable although the critical core consistency value is recommended to be 50 [7]. On other hand, the residual fit values were 0.051, 0.039, 0.026, and 0.021 and it looked that 3-factors was highly reasonable; two factors accounted for melamine pH-equilibrating and the other factor accounted for milk-extract component. The final conclusion was that for all test samples, three components were enough to explain the variability in the three-way data. The three-way arrays of calibration set and for each of the test samples were subjected to PARAFAC analysis under the following circumstances: a) initialization by best fit of several small runs, and b) applying non-negativity constraint during the least squares fit. The earlier analysis indicated that initialization by direct tri-linear decomposition was not productive and this may stem from the rank-deficiency and liner-dependency in the data (i.e., deviation from trilinearity). A representative PARAFAC output is provided in (Fig. 4).

3.3. Parallel Factor Analysis (PARAFAC)

PARAFAC-Loadings

To check if the data matrices of test samples are rankdeficient or not, principal component analysis was applied. Rank-deficiency is an important property for matrix decomposition into bilinear signals which is related (in our study) to the number of chemical components to describe the variability in the data [13]. In the current case, the analysis revealed that two components were necessary to re-construct the data, while the number of responsive components was three (acid and basic forms of melamine beside milkextract). The fact that number of responsive components was larger than what was predicted from principal component analysis indicated that pH-spectral data matrices are rankdeficient. Decomposition of three-way arrays with rankdeficient matrices was rather a hard task for PARAFAC and certain constrains was necessary during numerical analysis. In some cases, PARAFAC showed limited success for rankdeficient data and exhibited intense spectral overlapping [13]. When PARAFAC was applied for processing threeway array data, the first step was the estimation of the number of responsive components. Three procedures are often applied to find the proper number of components [4, 10, 12]: (a) the core consistency criterion, (b) monitoring the residual fit of the PARAFAC model with added factors, and (c) visual inspection of the retrieved profiles. For linear dependency along pH dimension, the second choice seemed to be more reliable [13]. As an example, when analyzing melamine, after PARAFAC processing of a three-way array composed of spiked milk sample and the calibration set, the progression of core consistency values was: 100, 22.0 and 6.8

As depicted in (Fig. 4), the spectral profiles of melamine are properly retrieved if one matches the lines in (Fig. 4A) with experimental profiles (Fig. 2A). The pH profiles shown in (Fig. 4B) are in agreement with the expected changes of melamine species over the studied pH domain. Once the analyte profiles were obtained, prediction proceeded by the usual interpolation into the pseudo-univariate calibration line generated by regressing PARAFAC-scores with nominal melamine content. The model was performed through predicting the concentration of melamine in a set of 12 samples including melamine-free milk samples. Table 1 summarizes the PARAFAC results beside other models. The analytical performance of the tested methods was assessed by estimating root mean square error (RMSEP), relative error of prediction REP%, and mean recovery. The estiA

2

0.3

79

0.2

0 200

PARAFAC-Loadings

1

3

0.1

220

240

260

280

Wavelength (nm)

300

B

0.8

1

3

2

0.6 0.4 0.2 0

2

4

pH

6

8

Fig. (4). PARAFAC processing using three factors of spiked milk sample with melamine (1.0 mg kg-1). (A) Retrieved spectral profiles and (B) retrieved pH profiles. The profiles were normalized to unit length and the components are identified as protonated melamine form (1), deprotonated melamine form (2), and milk extract (3), PARAFAC condition: non-negativity in all modes and best-fit of several trials used for initialization.


Table 1.

Al Bakain et al.

Prediction of melamine in test samples by different second-order multivariate calibration methodsa, b.

Sample

Nominal

PARAFAC

MCR-ALS

U-PLS/RBL

1

0.50

0.43

0.48

0.55

2

0.50

0.42

0.46

0.50

3

0.50

0.42

0.52

0.49

4

0.85

0.92

0.88

0.87

5

1.00

0.87

0.92

0.97

6

1.00

0.82

0.94

0.96

7

1.00

1.30

0.93

1.05

8

1.50

1.10

1.60

1.48

9

1.50

1.30

1.65

1.52

10

1.50

1.29

1.55

1.50

11

1.85

1.92

1.91

1.84

12

2.00

2.30

1.88

2.03

Statistical indicators Mean recovery (%)

93.8

99.3

100.1

REP (%)

16.4

6.2

2.3

0.021

0.003

-1

RMSEP (mg kg ) a

0.142 -1

All concentration values (means of duplicate measurements and expressed in mg kg ) are indicating the level of melamine in measuring cell. Processing test samples by PARAFAC and MCR-ALS models were carried out using three components. However, one PLS latent variable and one RBL factors were used for running U-PLS/RBL.

b

mation of the earlier parameters is well known [7, 8]. The model manifested a reasonable prediction for melamine with mean recovery and REP of 93.8 and 16.4%, respectively. However, the other two methods outperformed PARAFAC for melamine prediction in milk-extract. As mentioned earlier, the collected pH-data matrices were non-trilinear due to rank-deficiency beside linear dependency in pH-mode and this negatively affected PARAFAC performance. PARAFAC is the best choice for modeling typical trilinear three-way arrays; however, the model may show good performance under certain constraints. In addition to the earlier discussion, the high spectral overlap between the deprotonated form of melamine and the milk extract is contributed to the modest performance of the model. 3.4. Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) Before running MCR-ALS method, matrix augmentation was made in the pH dimension (trilinearity-breaking mode). This augmentation was made to compensate for the possible variation in the measured pH profiles from sample to sample and/or linear dependencies among profiles. This model, in fact, allows each sample to present its own specific profile in the augmented direction and to solve linear dependency in pH profiles. Moreover, it is known that augmentation can improve analytical sensitivity in the augmented mode [2, 4]. In this model, the data matrix of a test sample was fused with calibration matrices along pH-mode. After augmentation of

matrices, running of MCR-ALS was started with appropriate initializations non-negativity condition in both concentration and spectral modes were maintained. The number of responsive components was determined by principal component analysis of the augmented matrices. In all cases, three components were found enough to explain the variability in the data and the same results was established by PARAFAC model. Typical modeling outputs of MCR-ALS are depicted in (Fig. 5). After convergence of fitting, the retrieved spectral profiles of melamine species were found reasonable when compared to those obtained experimentally (Fig. 5A vs. Fig. 2A). It seems that the model was more effective for resolving background`s signal from basic form of melamine. The retrieved pH-profiles over the augmented data matrices are displayed in Fig. 5B. As indicated in (Fig. 5B), the contribution of extract`s signal (line 3 in Fig. 5B) has been eliminated and the lines of responsive melamine species were alternating in a systematic way over pH dimension and all samples. After completion of decomposition, interpolation in the pseudo-univariate graph was generated from MCR scores for the most sensitive species related to analyte (the protonated form in this case). The results of melamine prediction in milk-extract are shown in Table 1. It is obvious that the model outperformed PARAFAC with mean recovery and REP of 6.2% and 99.3%, respectively. It seems that augmentation along pH-dimension produced better analytical sensitivity and this option (i.e., matrix augmentation) was not affordable in PARAFAC model. Running MCR-ALS using


A


81

MCR-Loadings

0.3 0.25 0.2

2

0.15 0.1

0.05 0 200

1

3 210

220

230

240

250

260

270

280

290

300

Wavelength (nm)

MCR-Loadings

B 4

2

3

1

2

3

1 0

5

10

15

20

25

30

35

40

Augmented mode (pH-mode)

Fig. (5). Profiles retrieved by MCR-ALS (three factors involved) after processing milk sample containing 1.0 mg kg-1 melamine. (A) Spectral profiles which are identical for all test samples. (B) pH profiles for successive matrix samples (11 test samples). Profiles are identified as protonated melamine form (1), deprotonated melamine form (2), and milk extract (3).

matrices augmented in the spectral dimension was tried; however, comparable results were obtained to those given in Table 1. 3.5. Unfolded Partial Least-Squares/Residual Bilinearization (U-PLS/RBL) U-PLS/RBL algorithm was selected as it achieved second-order advantage and showed good workability for pHdata matrices [10, 13]. In this algorithm, residual bilinearization is necessary with PLS to account for uncalibrated interferences of milk extract. For U-PLS/RBL model, the common leave-one-sample cross validation procedure (Haaland and Thomas` criterion) was adopted [13, 22]. One may expect that one PLS latent variable would be enough to account for calibration samples due to the correlation existing between both forms of analyte [13]. However, the optimum number of U-PLS/RBL factors was 2. The second PLS component may be attributed to many reasons including changes in baseline or variations in light absorption for melamine species (the melamine-acid form absorbs more compared to the basic form). When applying this model to the test samples, it was necessary to assess the number of unexpected components to be employed in the RB step. This is simply achieved by analyzing the sample modeling residuals as a function of a trial number of interfering components [8]. The results confirmed that two U-PLS/RBL factors and another RB factor were necessary for melamine quantification in test samples. Only one RB factor was needed to model milk`s background. The spectrum of this new factor (not shown) is similar to that was experimentally recorded for milk-extract indicating the high calibration power of U-PLS/RBL model. Once the important parameters in the U-PLS/RBL model (i.e., number of calibration latent variables and number of RBL components) were ascertained, prediction of melamine in milk-extract was carried out and the results are shown in Table 1. The model outperformed both PARAFAC and MCR-ALS with mean recovery and REP of 100.1% and 2.3%, respectively. Furthermore, a very low RMSEP value (0.003) was reported for this method. The results reported in

this work confirmed that latent structured U-PLS/RBL is a robust method for handling second-order data of lineardependency. This was also reported in the literature, however, for different analytical systems [10, 13]. Table 2 summarizes some important figures of merit for the successful U-PLS/RBL model including: sensitivity, analytical sensitivity, limit of detection, limit of quantification, uncertainty in predicted concentrations. Table 2.

Figures of merit of the proposed analytical method (U-PLS/RBL). Figures of merit

Dynamic range (mg kg-1)

0.057-2.00 -1

Sensitivity SEN (AU kg mg )

13.4 -1

Analytical sensitivity (kg mg )

62.0

Limit of detection LOD (mg kg-1)

0.019 -1

Limit of quantification LOQ (mg kg )

0.057 -1

Uncertainty in predicted concentrations SD (mg kg )

0.020

We should mention that the earlier figures of merit were estimated individually for each test sample, therefore, an average value of each figure of merit was provided. Sensitivity was provided with U-PLS/RBL outputs. Limit of detection and limit of quantification were estimated assuming 3.3 and 10 times, respectively, the standard error for the samples of low analytes content. The reported figures of merit are excellent considering the complexity of the current analytical system; moreover, the earlier figures of merit also confirmed the suitability of U-PLS/RBL for melamine quantification down to 0.057 mg kg-1 (or 0.055 mg L-1). The accuracy and inter/intra-day precision of the proposed method were assessed by analyzing milk samples spiked with 1.0 mg kg-1. In


Al Bakain et al.

all cases, RSD of analysis was satisfactory with a range 2.34.7% confirming the reasonable performance of the proposed analytical procedure for food samples. As will be discussed in the following section, the reported figures of merit are appropriate when comparing the proposed analytical method with the previous reported methods in the literature. 3.6. Analytical Performance of the Proposed Method Many analytical methods were proposed for melamine quantification in milk and other matrices, as summarized in Table 3. The reported methods extended from laborious matrix cleaning coupled with liquid chromatography to nonTable 3.

separative ones including the powerful PLS-1. For further assessment and comparison purposes, U-PLS was critically compared with previously reported methods seen in the literature. Most of the reported methods [15, 18, 20, 21, 23-29] are sharing the initial sample treatment in which solutions of trichloroacetic acid or acetonitrile was used to extract melamine and for efficient defatting and deproteination. Two of the reported method used HCl solutions for initial sample preparation and solute extraction [27, 29]. The chromatographic methods resulted in better melamine detection; however, a preliminary purification/preconcentration step

Comparison of the analytical characteristics for the published methods with the proposed one for melamine determination in milk. Chromatographic methods

Sample Size a

Sample pre-treatment b

Pur/

DL/QL Final solvent (ml)

Analytical technique

Precon.

10 g

Extractant 100 ml 1% CCl3CO 2H

Hollow

2-ethylhexyl phosphoric

(S)

and centrifugation.

fiber-SPE

acid/6-undecanone (36 μL)

3.0 g (S)

Extractant 30 mL CH3CN,

5.0 ml (L)

centrifugation, and micro-filtration.

–

–

Spike Rec.

RSD

(%)

(%)

Ref.

HPLC-UV.

QL 10 g kg-1

89.1-102.6

3.8

[21]

HILIC-UV

DL 30 g L-1 DL

73-96

4-5.2

[22]

82.7-85.1

-

[23]

DL 15 μg kg-1

95.2-105.0

1.6- 8.0

[24]

DL 110 μg kg-1

101-103

1.5-2

[17]

(NH2-bonded- St.

50 g kg-1

Phase). 10 mL (L)

Extractant 15 mL 1% CCl3CO 2H

SPE

and 5.0 mL CH3CN. 4.0 g

–

–

–

Extractant 24 mL H2O and 1.0 ml of SPE (cation- NH4OH/methanol mixture

(S or L)

1.0 N HCl, and centrifugation.

exchanger).

(5.0 mL).

5.0 g (S)

Extractant 50 mL 1% CCl3CO 2H

SPE

Ammonia/methanol

5.0 g (S)

(titania- St. Pha.).

Extractant 5.0 mL CH3CN/H 2O, centrifugation, and micro-filtration.

5.0 g

HPLC-UV –

and 2.0 mL of 2.2% lead acetate, and

mixture (6.0 mL), micro-

centrifugation.

filtration.

Extractant 50 mL 1% CCl3CO 2H and 2.0 mL of 2.2% lead acetate, and

DL 18 g L-1 QL 100 gL-1

(6.0 mL).

Extractant 10 mL CH3CN, centrifugation, and micro-filtration.

1.0 g (S)

HPLC-UV.

NH4OH/methanol mixture

-1

HPLC-UV.

QL 340 μg kg

LC-MS/MS.

QL 4 g kg-1

61-105

1-18

[25]

DL 18 μg kg-1

90.6-99.5

2.3-2.4

[14]

89.5-98.5

1.8-3.5

[26]

98.2-100.4

0.27-6.15

[19]

98.9-100.2

1.49-2.07

[27]

94.3-110.6

4.37-6.05

[28]

98.7-110.3

3.5-4.6

HPLC-UV.

QL 60 μg kg-1

SPE

NH4OH/methanol (6.0

Capillary electrophore-

DL 120 g kg-1

(C18)

mL), micro-filtration.

sis (HPLC).

QL 370 g kg-1

centrifugation. Non-Chromatographic methods 5.0 g (S)

1.0 g

Extractant 40.0 mL CH3CN/H2O,

Rank-annihilation

DL 12 μg L-1

centrifugation, and micro-filtration.

factor analysis.

QL 40 μg L-1

Multivariate calibration

DL 1.0 mg kg-1

–

PLS-1/FTIR data.

QL 3.5 mg kg-1 QL 690 g kg-1

–

–

–

Freeze-drying and mixing with KBr. –

0.7-2.0 g

Extractant H2O, and 12 mL of 5.0 M

Derivative UV-

(S)

HCl, SiO2, and centrifugation.

Spectrophot-ometry.

QL 150 g L

Second –order Multi-

DL 19 μg kg-1

variate calibration.

QL 57 μg kg-1

–

-1

6.0 ml (L) 5.0 g

Extractant 15 mL CH3CN/H2O, centrifugation, and micro-filtration.

a

–

–

S: solid (powdered), L: liquid. This includes the employed solvent for melamine extraction from milk, defatting, and deproteination.

b


was necessary. In most of the separation-based methods, a solid-phase extraction by C18 was adopted besides the use of cation-exchanger and hollow fiber in the extraction step. Indeed, using hollow fiber in the selective melamineextraction step resulted in a better analytical detection (10 g kg-1) [23]. Detection by normal UV-detectors generated good quantification results where detection limit was 15 g kg-1 [26]. The lower detection limit (where no SPE employed) observed in the earlier study was attributed to the novel use of titania-based stationary phase [26]. Zheng and co-workers have utilized polar NH2-bonded-stationary phase for melamine detection in milk (detection limit was 50 g kg-1) without running any solid phase extraction (SPE) step [15]. The lowest quantification limit (QL) that observed in the surveyed methods was 4 g kg-1 and this is attributed to the good combination between SPE and sensitive-MS detection [27]. Although, capillary electrophoresis is not a pure chromatographic method, it demonstrated a good performance for melamine detection but with higher DL/QL than observed for common HPLC/UV or even HPLC/DAD [18, 23, 24, 26]. It is important to mention that using SPE for further matrix cleaning has resulted in lower quantification limits (due to efficient removal of co-extracted materials). The higher DL and QL reported by Filazi and co-workers is attributed to the direct analysis of milk extract without applying any matrix-cleaning procedure [18]. Interestingly and as depicted in Table 3, the non-chromatographic methods do not apply any purification/preconcentration step and manifested a stable analytical performance. One of the practical methods is reported by Jawaid and co-workers, no extraction was needed and melamine was quantified down to 690 g kg-1 [28]. Derivative UV-spectrophotometry is also recommended as a cheap substitute chromatographic method that avoids tedious sample preparation steps [29]. Liu and coworkers have analyzed rank-deficient pH-spectral data matrices (generated by scanning melamine solutions over pH range 2.0-10.0) using rank annihilation factor analysis (RAFA) to end up with a sensitive and accurate analytical method for melamine detection in milk extract [21]. In the earlier multivariate method, a QL of 40 g kg-1 and mean recovery of 99.10% (spiking level 0.28-2.6 mg mL-1) were reported. Although flow injection method is more accurate and reproducible for generating pH-spectral data, batch experiment with a few data point in pH-dimension seems to be workable for applying second order multivariate calibration. The proposed method in this work requires no liquid-liquid or solidphase extraction steps and it reports for the first time the application of PARAFAC, MCR-ALS, and U-PLS/RBL for melamine detection in liquid milk using pH-spectral data. The reported limit of quantification beside the mean recovery of the proposed method is of comparable quality to those depending on laborious matrix-cleaning and chromatographic procedures [15]. Another major aim of this work was achieved where the application of MVC2 program for trace chemical analysis was demonstrated.


cated that the combination of the unfolded version of partial least-squares with residual bilinearization was the prizewinning method for the current analytical system. Specifically, the model was able to interpret the presence of linear dependencies among component profiles, together with intense spectral overlapping between the basic form of melamine and the responsive milk-extract. The poor analytical performance of PARAFAC and MCR-ALS was attributed to rank-deficiency besides intense spectral overleaping in the spectral mode. CONFLICT OF INTEREST The authors confirm that this article content has no conflict of interest. ACKNOWLEDGEMENTS The authors acknowledge The Deanship of research and graduate studies, The Hashemite University (Zarqa, Jordan) for funding this research. The authors would like to thank Prof. A.C. Olivieri for the valuable discussion on multivariate calibration and on MVC2 program. REFERENCES [1] [2]

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