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Determination of diphenylamine residue in fruit samples using spectrofluorimetry and multivariate analysis
LWT - Food Science and Technology 54 (2013) 6e12
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Determination of diphenylamine residue in fruit samples using spectrofluorimetry and multivariate analysis Alireza Farokhcheh, Naader Alizadeh* Department of Chemistry, Faculty of Science, Tarbiat Modares University, P.O. Box 14115-175, Tehran, Iran
a r t i c l e i n f o
a b s t r a c t
Article history: Received 28 January 2013 Received in revised form 15 April 2013 Accepted 18 May 2013
Determination of diphenylamine residue in fruit samples was studied based on normal, synchronous first- and second- derivative spectrofluorimetry. These methods were performed using three of the most widely employed multivariate calibration techniques which are partial least squares, multiple linear regression and principal component regression. Eighteen combinational methods were tested to present the best model for determination of diphenylamine residue. The prediction performance of the calibration models, which was constructed on the basis of these methods, was also compared. For a range of concentrations from 10 to 100 mg kg1 of diphenylamine in the fruit prediction set, the values of root mean square error and relative error of prediction, using multiple linear regressions, were determined in the range of 3.3e4.1 mg kg1 and 6.4e8.0%, respectively. Repeatability studies were satisfactory, giving RSD% values of 1.8, 5.6, and 3.3 for apple, pear, and orange, respectively. The calibration graphs were linear in the range of 10e100 mg kg1 and detection limits were between 4 and 7 mg kg1. The presented method was successfully applied to determine diphenylamine residue in some of naturally treated fruit samples and the results indicated proper agreement with those obtained by HPLC analysis. Ó 2013 Elsevier Ltd. All rights reserved.
Keywords: Diphenylamine Food analysis Fruit samples Multivariate analysis Spectrofluorimetry
1. Introduction Pesticides are a class of chemicals which have been widely used at various stages of food crops cultivation and during their postharvest storage and play an important role in the intensification of agricultural production. Diphenylamine (DPA) is one of the most used pesticides worldwide. It is used as a pre- or postharvest scald inhibitor for some fruits include apples and pears. Its anti-scald activity is the result of its antioxidant properties, which protect the fruit skin from the oxidation products of alpha-farnesene during storage (Bramlage, 1988). Therefore, residues of DPA are often found in agricultural crops. However, the presence of pesticide residues in foods can be considered as a hazard to human health (Drzyzga, 2003; Sholberg et al., 2005). According to EU regulations in foodstuffs, the maximum residue levels (MRLs) for DPA are 5 and 10 mg kg1 for apples and pears, respectively (Drouillet-Pinard et al., 2010). Therefore, qualitative and quantitative determination of DPA in these materials is of
Abbreviations: DPA, Diphenylamine; PLS, partial least squares; MLR, multiple linear regression; PCR, principal component regression; MRLs, maximum residue levels; RCF, relative centrifugal force; PRESS, Prediction Error Sum of Squares; REP, relative error of prediction. * Corresponding author. Fax: þ98 21 82883455. E-mail address: [email protected] (N. Alizadeh). 0023-6438/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.lwt.2013.05.032
biological and environmental importance. The techniques which have been most frequently used to determine DPA levels are as follows: chromatographic (Garrido, De Alba, Jimenez, Cadado, & Folgeiras, 1998), electrochemical (Olek, 1988), ultraviolet and visible spectrophotometric (Rudell, Mattheis, & Fellman, 2005) and fluorimetric methods (Saad et al., 2004). However, chromatographic methods typically require clean-up steps after extraction in order to remove the interfering substances. On the other hand, fluorescence-based methods are highly sensitive and simple as compared to the chromatographic methods; but problems with interferences in complex food matrices can occur during analysis. This situation necessitates separation steps to better enable the analyte determination (Sena, Trevisan, & Poppi, 2006). Generally, analyte extraction and sample preparation is the most challenging and time-consuming step in the analytical process. Chemometric methods present valuable information which are extracted from multivariate data arrays and would be difficult to process if classical statistical methods are used. Fluorescence studies provide a large amount of data containing multiple parameters for which interpretation is far from obvious. In our previous articles (Aghamohammadi, Hashemi, Kram, & Alizadeh, 2007; Hashemi, Kram, & Alizadeh, 2008), the combination of synchronous fluorescence spectroscopy and multivariate calibration methods was successfully applied for determination of the aflatoxin B1 in pistachio and wheat samples, implying that the chemometric
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methods exhibit a high capability to extract valuable information in the case of complicated matrices. The multivariate calibration methods such as MLR, principal component regression analysis (PCR) and partial least squares regression analysis (PLS) have been widely used for mutual correlation of two data sets in food analysis (Ignat, Volf, & Popa, 2011; Mas, de Juan, Tauler, Olivieri, & Escandar, 2010). Multivariate calibration methods (Brereton, 2003), applied to absorptive and emissive spectral data as well as electrochemical signals, are increasingly being used for the analysis of complex biological mixtures. These methods have the advantage of using full spectral information and allow for a rapid determination of mixture components, often with no need of prior separation or sample pretreatment. Multivariate statistical methods such as PCR and PLS have been applied in the analysis of mixtures using mainly infrared, UVeVis absorption spectroscopy or fluorescence spectroscopy (Ibañez, 2008). Domínguez-Vidal et al. reported the use of chemometrics combined with fluorimetric multioptosensing for the determination of pesticides in environmental water samples (Domínguez-Vidal, Ortega-Barrales, & Molina-Díaz, 2007). In this study, the potential of combining normal, synchronous and derivative fluorimetry with multivariate methods for the quantitative analysis of DPA in fruit samples is evaluated. The combination of spectrofluorimetry with multivariate calibration is simple, fast, specific and sensitive, and may allow the development of methods for determining DPA in food products in particular those with complicated matrices. The proposed method requires no prior separation or derivatization process. The added-found method as well as naturally contaminated samples was used to validate the results. This method has been successfully applied for determination of DPA residue in fruit samples. 2. Materials and methods
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The SPSS version 10.0 software was employed for the statistical treatment of data. Moreover, a house-written visual basic program, based on the algorithms described by Brereton (2000), was used for application of the PLS multivariate calibration method. 2.3. Sample treatment Fruit samples (apple, pear, and orange) were purchased from different local markets in Tehran, Iran. An amount of 1 kg of each fruit sample was ground and homogenized using a high-speed blender. Then, 20 g portion of this sample was weighed and placed in a centrifuge tube and 5 g sodium acetate, 20 g sodium sulphate and 40 mL ethyl acetate were added as well. After that, the tube was vigorously shaken for 2 min. DPA was extracted by liquide liquid extraction (Saad et al., 2004), and then transferred to the organic phase. The extract was filtered through 0.45 mm cellulose acetate membrane filters. Finally, an extract containing the equivalent 0.5 g of sample per mL in ethyl acetate was obtained. Then, 2 mL of this extract was evaporated to dryness under a flow of nitrogen at room temperature. The dry residues were dissolved in 1 mL methanol. The obtained colloidal solution was centrifuged at 4000 rpm (the relative centrifugal force, RCF is 2634) for 2 min. In order to reduce the fluorescence background, the sample solution was diluted 15 times with methanol and then used as an analytical sample. Samples used for recovery studies and matrix background evaluation were previously tested by HPLC method and proved to be free from the investigated pesticide (DPA). The final solution was used as a sample for obtaining the fluorescence spectra. The standards and samples were protected from direct light during all the procedures. The above described method was used for preparation of blank fruit sample and spiked with a known quantity of DPA in calibration set samples and prediction set samples. It should be note that DPA is often problematic in ultratrace residue analysis due to carry-over and cross-contamination issues from other sources.
2.1. Reagents and standards 2.4. Preparation of calibration and validation sets Diphenylamine (DPA) was purchased from Merck, Germany. Other used reagents were of analytical grade. Double-distilled water was used throughout. Stock solutions of DPA at the concentration of 0.02 mol L1 were prepared in methanol and kept in the dark at 4 C. Working concentrations of DPA were prepared from the stock solutions by appropriate dilution before use. All samples prepared from fruits were filtered through 0.45 mm cellulose acetate membrane filters before further use. Fruit samples (pear, apple and orange) were purchased from markets in Tehran province (Tehran, Iran). 2.2. Apparatus The fluorescence spectra were recorded by a PerkineElmer model LS 50B spectrofluorimeter equipped with a thermostated cell compartment. The DPA fluorescence intensity was measured at the maximum emission wavelength of 365 nm after excitation of solutions at 282 nm. The scan rate of the monochromators was maintained at 50 nm min1 in recording conventional spectra and at 100 nm min1 for total synchronous fluorescence spectra. All measurements were performed in 10 mm quartz cells, at 25 0.1 C, by use of a thermostatic cell holder and a Thermomix thermostatic bath. Absorption spectra were obtained using a Sinco (model UV S-2100) UVeVis spectrophotometer. The liquid chromatographic analysis was performed on a HPLC system with UVe Vis detector, model Smartline 2500 (Knauer, Germany). A C18 column 4.6 i.d. 250 mm, 5.0 mm (Knauer, Germany) was used for separations. The mobile phase was methanol/water (70:30 v/v) and operated at 1 mL min1. The absorption detector was fixed at 282 nm and the peak area was used as the quantification parameter.
Two sets of standard solutions were prepared; the calibration and validation sets. Calibration set was used to build the calibration model while the effectiveness of the proposed model for prediction was confirmed in the validation set. Twenty six standard solutions for the calibration set and 23 solutions for the prediction set were randomly chosen, which were used to validate the calibration model. In order to make comparisons among the results of the three models, the training and prediction sets were chosen similarly for all the three models. The spectrum for each of the samples was recorded from 300 to 500 nm for normal and 270e470 nm for synchronous fluorescence spectra where the spectra are subdivided into 1.0 nm intervals. The response data of the calibration set was a matrix with 26 200 dimensions. The concentration of DPA in each analytical sample was between 10 and 100 mg kg1. 3. Results and discussion 3.1. Contour plots Fig. 1a shows the UVeVis absorption spectrum and fluorescence emission spectra of DPA in methanolic solution. It is obvious that DPA has an absorption peak at 282 nm and when excited by radiation, shows intrinsic fluorescence maximum which is located at 365 nm. Fig. 1b shows the contour plot of total synchronous fluorescence corresponding to 70 mg L1 DPA in methanolic solution. The selection of the optimum Dl and excitation wavelength for the recording synchronous fluorescence spectra, in order to perform the determination process, was examined properly by collecting
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emission maxima of 282e365 nm. In order to achieve the maximum extraction recovery, different organic solvents were examined during the experiment including ethyl acetate, acetone, ethanol, methanol and acetonitrile. The results indicated that no significant difference was detected upon examination of various solvents. However, ethyl acetate was selected as the extraction solvent because of creating less background emission in comparison with the other investigated solvents. Therefore, methanol was selected as the solvent for DPA in this research. 3.3. Matrix of calibration and selection of the spectral zones for the analysis Six different data sets, selected from the emission spectra, the synchronous spectra and their first and second derivatives, were evaluated to perform the determination. Fig. 2 shows some typical normal fluorescence, first and second derivatives of fluorescence spectra and synchronous spectra with its first and second derivatives. Since full-spectrum methods (MLR, PCR, PLS) are able to use many wavelengths, one could conclude that wavelength selection is unnecessary and all the available wavelengths are often used. The spectral regions between 300 and 500 nm for emission and between 270 and 470 nm for synchronous path were selected for the analysis. The reason for choosing those particular regions is that the maximum spectral information is available within those regions and no relevant information is obtained beyond them. MLR, PCR and PLS were employed to perform the determination. A randomly designed calibration set of 23 analytical samples with DPA in the concentration range of 10e100 mg kg1 and fruit samples matrices (prepared as described in experimental section) was used to statistically maximize the information content in the spectra. The optimum dimensionality of the PCR methods was obtained from the PRESS function (Prediction Error Sum of Squares) Eq. (1).
PRESS ¼
N X
2
ðb y i yi Þ
(1)
i¼1
Fig. 1. Absorbance (left) and fluorescence (right) spectra of 104 mol L1 DPA in methanolic solution (a), contour plots of total synchronous fluorescence of 70 mg L1 DPA in methanolic solution (b) and calibration curves of DPA in different solvents (c).
the total synchronous fluorescence spectra of analyte, in the form of a Dl-excitation matrix. The synchronous fluorescence spectra were collected by scanning the excitation wavelength between 260 and 440 nm in the wavelength interval 20e250 nm (at l increments of 5 nm), and were displayed as contour plots. The plot shows that the maximum fluorescence intensity was observed at excitation wavelength of 340 nm (Dl ¼ 80 nm). These values are in conformity with those which could be obtained from emission and excitation normal spectra (Fig. 1a). 3.2. Solvent effect on the fluorescence intensity of DPA It is known that the type of solvent could have a significant effect on fluorescence intensity. Calibration curves, which were used for determination of fluorescence sensitivity of DPA in different solvents, are shown in Fig. 1c. As can be seen, the highest level of sensitivity can be achieved in methanol media using excitation-
^i are the real concenwhere N is the number of samples, yi and y tration and the predicted concentration of sample i, respectively. In order to achieve the best predictions, it is of great importance to select how many principal components or factors to use in the calibration with the PLS and PCR algorithms. In PLS and PCR methods, PRESS was calculated by cross-validation (Esbensen, Guyot, Westad, & Houmøller, 2002; Samadi-Maybodi & Darzi, 2008) leaving out one sample at a time, to model the system. The prediction of the concentration corresponding to the sample left out was carried out by means of a model obtained using the N1 remaining samples. The minimum number of factors was chosen to build the model for which the PRESS had no significant differences for the N > 6. For example, Fig. 3a shows the PRESS diagram of PCR calibration for pear samples. As can be seen, the minimum value of PRESS was resulted using three factors for prediction in crossvalidation method. For this reason, three factors were selected as the optimum dimensionality of the PCR method. The optimum numbers of factors used for each model are shown in Table 1. A prediction set of 23 fruit analytical samples (prepared as described in sample treatment section) with known spiked amount of DPA in the range of 10e100 mg kg1 was used to evaluate the methods. The prediction ability of each method was expressed as the RMSEP which stands for root mean-square error of prediction. RMSEP is an indicator of the average error in the analysis expressed in the original measurement unit Eq. (2) (Brereton, 2003; Nepote, Damiani, & Olivieri, 2003). Another useful parameter is the REP%
A. Farokhcheh, N. Alizadeh / LWT - Food Science and Technology 54 (2013) 6e12
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Fig. 2. Normal (apples and oranges) and synchronous (pears) fluorescence spectra obtained for fruit samples. Solid line (d) and dash line (- -) show first and second derivative spectra, respectively.
Fig. 3. PRESS diagram for applying PCR on synchronous fluorescence spectra of calibration set for pears (a) and correlation between real and predicted concentrations of DPA in prediction set samples by MLR model of normal fluorescence spectra, apples (b), oranges (c) and PCR model of synchronous fluorescence spectra (d).
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Table 1 Results of root mean-square error of prediction (RMSEP) and percent of relative error of prediction (REP%) obtained for prediction set by applying different multivariate methods on normal and synchronous spectra of calibration set of fruit samples. Multivariate method
MLR
Fluorescence method
Normal
Synchronous
PCR
Normal
Synchronous
PLS
Normal
Synchronous
a b c d
D.O.a
0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2
Nb
4 4 3 4 3 3 4 5 3 3 3 3
Pear
N
Orange
N
Apple
RMSEPc
REP%d
RMSEP
REP%
RMSEP
REP%
11.2 13.2 15.5 12.6 16.8 12.2 7.4 13.7 7.9 6.2 4.1 5.8 12.2 12.8 12.2 11.2 12.5 12.0
21.5 25.4 29.8 24.2 32.4 23.4 14.2 26.3 15.2 11.8 8.0 11.2 23.5 24.6 23.4 21.6 24.1 23.0
6.2 3.3 15.1 7.2 7.6 9.0 9.1 11.4 25.2 10.0 10.4 10.5 11.3 7.4 12.7 14.2 13.5 10.2
11.8 6.4 29.1 13.8 14.7 17.4 17.5 21.9 48.4 19.2 20.0 20.1 21.7 14.3 24.4 27.2 26.0 19.7
5.6 8.7 3.3 5.6 7.6 8.3 9.7 12.8 15.1 8.7 12.2 12.7 12.3 10.5 7.4 8.0 11.9 8.7
10.8 16.8 6.4 10.8 14.6 16.0 18.6 24.5 29.1 16.7 23.4 24.4 23.7 20.2 14.2 15.4 22.9 16.7
3 5 3 5 3 5 3 3 3 4 4 4
3 4 3 5 4 3 3 3 4 4 5 3
Derivative order. The number of factors used for the modeling. Root mean-square error of prediction. Relative error of prediction.
Table 2 Comparison of the proposed method with other analytical techniques for determination of DPA in different samples. Analytical technique
Description
Sample
DLRa
LODb
Response time (min)
Recovery (%)
RSDc (%)
Ref.
Color scanner detects the product on TLC strips
The method is based on the reaction of diphenylamine with ninhydrin gives an iminium salt, on heating which formed orange color dye in acidic medium A tandem mass spectrometric method is established and mass spectrometer parameters optimized A method based on electron impact (EI)/MS/MS was developed
Apple & pear
0.3e5.1 mg mL1
0.18 mg mL1
10
96e105
<6.4
Baghel, Joshi, & Amlathe, 2012
Smokeless gunpowder
5.0e200 ng mL1
1.0 ng mL1
e
80.3 4.9
<11.3d
Tong et al., 2001
Orange & vegetables
e
0.4 ppb
12
e
e
Apple & pear
0.25e5 mg kg1
0.06 mg kg1
e
78e104
<3
Gamon, Lleo, Ten, & Mocholi, 2001 García-Reyes, Ortega-Barrales, & Molina-Díaz, 2005
Vegetable juice
e
<5 mg kg1
e
77e114 (average recovery)
<14
Nguyen, Yun, & Lee, 2009
Baby food matrix with 100% fruit content
e
1 mg kg1
e
81.5
9.5
Apple Orange Pear
12e100 mg kg1 10e85 mg kg1 15e85 mg kg1
4 mg kg1 6 mg kg1 7 mg kg1
98e102
2e6
Gilbert-López, García-Reyes, Ortega-Barrales, Molina-Díaz, & Fernández-Alba, 2007 This work
Tandem mass spectrophotometry EI/MS/MS
Single multicommuted fluorometric optosensor
LC/ESI-MS/MS
LC/ESI-TOF-MS
Spectrofluorimetry
a b c d
A single flow injection multicommuted system using solid-surface (solid support: C18 silica gel) fluorescence spectroscopy has been explored for the determination of diphenylamine in apples and pears Determination was performed using gas chromatography with mass spectrometric detection (GCeMS-SIM) and liquid chromatography electrospray ionization tandem mass spectrometry (LC/ESI-MS/MS) The developed method consists of a sample treatment step based LLE followed SPE
Multivariate calibration applied to normal and synchronous fluorescence spectrometry.
Dynamic linear range. Limit of detection. Relative standard deviation. Intra-assay precision.
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Table 3 Determination of DPA in naturally contaminated fruit samples using fluorimetry method in combination with multivariate analysis (proposed method) and HPLC. DPA concentration (mg kg1)
Spike (mg kg1)
Pears HPLC (mg kg1)
Sample 1
A.Rc (%) Sample 2
A.R (%) Sample 3
0 30 60
5.5 0.4 36.7 0.4 64.1 0.3
0 30 60
13.4 0.6 46.2 0.2 74.0 0.7
0 30 60
10.9 0.3 40.2 0.5 71.9 0.4
A.R (%)
Oranges PCR
t-value
a
e 37.1 0.3 65.3 0.6 101.48 14.4 0.5 45.5 0. 9 75.2 0.1 102.52 11.2 0.8 38.9 0.7 72.6 0.4 100.16
HPLC (mg kg1) b
1.38 3.10
e 33.1 0.6 58.9 0.4
2.22 1.32 2.94
e 30.2 0.9 57.6 0.1
0.61 2.62 2.14
e 29.7 0.6 58.4 0.2
Apples t-value
HPLC (mg kg1)
MLR
t-value
0.9 1.0
0.48 2.57
17.8 0.3 48.3 0.6 76.8 0.4
3.81 3.10 3.48
0.7 0.3
0.75 6.57d
23.5 0.3 54.2 0.7 84.5 0.2
0.8 0.5
2.77 6.75d
14.6 0.3 43.1 0.4 74.5 0.5
16.7 0.4 46.9 0.5 78.6 0.8 97.75 24.4 0.5 55.7 0.3 84.1 0.6 102.04 13.4 0.8 42.9 0.4 76.1 0.6 97.82
MLR e 32.8 57.3 98.18 e 29.7 56.4 98.13 e 28.1 60.5 99.10
2.67 3.41 1.10 2.43 0.61 3.54
a
Out of calibration range. Not detected. c Average recovery. d Statistically significant at 0.05 level and insignificant at the level of 0.01; The Critical t-value for two tail distribution student paired t-test having degree of freedom 2 and level of confidence of 95% is 4.30 and for level of confidence of 99% is 9.92. b
(percent of relative error of prediction) of the model which could be calculated by Eq. (3).
RMSEP ¼
N 1 X 2 ðb y yi Þ N i¼1 i
!0:5
N 100 1 X 2 ðb y yi Þ REP% ¼ y N i¼1 i
(2) !0:5 (3)
where y is the average analyte concentration in the calibration set. 3.4. Results for prediction set The chemometric methods were successfully applied to the analysis of DPA in fruit samples (apple, pear and orange). The results presented in Table 1 clearly indicate which models have been the most effective. Table 1 shows that, in the case of apple fruit, the prediction ability of methods including Nos. 1, 3 and 4, and in the case of orange fruit, the Nos. 1, 2, 4 and 14 among all the 18 tested methods were better than that of the other ones. The most accurate results for the above-mentioned achievements were obtained using a MLR model calculated from the first and second derivative fluorescence spectra of calibration set correlating the DPA concentration (mg kg1) of samples to fluorescence intensity. On the other hand, for analysis of DPA in pear sample, the most accurate results (Nos. 10, 11 and 12) were obtained using a MLR model calculated from the first derivative synchronous spectra. Fig. 3b shows the correlation between real and predicted concentrations of DPA in prediction set samples for apple, orange and pear, respectively. These results indicate that the model can accurately predict the DPA concentration of samples in the presence of highly interfering fruits matrices. 3.5. Figure of merits of spectrofluorimetry-multivariate for analysis of fruit samples The calibration graphs were linear in the range of 12e100,
mg kg1 and limit of detections (LODs) were between 4 and 7 mg kg1. The LODs were obtained as the sample concentration which causes a peak that is three times as high as the baseline noise level. LODs for analysis of each fruit sample are shown in Table 2
and compared with other reports in literature. Repeatability studies were satisfactory, giving RSD% values of 1.8, 5.6, and 3.3 for apple, pear, and orange, respectively. Reproducibility of the selected multivariate methods was checked by recording independent series of ten samples for each fruit on two consecutive days; when reproducibility studies were undertaken over the two sets of ten standards for each fruit on consecutive days no significant differences were found between the two sets of ten replicates at a confidence level of 95%. 3.6. Spectrofluorimetry-multivariate analysis of natural fruit samples and comparison with HPLC analysis In this work, application of the first and second derivative fluorescence spectra using MLR for the determination of DPA, in the case of orange and apple samples, is reported. It is also demonstrated that combination of the first derivative synchronous spectra and PCR can be used to determine DPA in the pear samples. DPA was analyzed in naturally treated spiked and non-spiked fruit samples. Each determination was repeated three times for all the fruit samples (spiked and non-spiked samples) and sample preparation steps were carried out separately for each run. The results of naturally treated fruit samples were compared with the HPLC method which has been performed in separated laboratories. The Student’s t-test indicates that the differences between the predicted values of concentrations are not significant. Similarly, spiked fruit samples were analyzed by synchronous and normal fluorimetry. Of interesting to note is that the DPA content in the naturally treated fruit samples was found to be in good agreement with that determined by HPLC method. These results are summarized in Table 3. 4. Conclusion DPA in fruit samples (apple, orange and pear) was determined by synchronous and normal fluorimetry in combination with MLR or PCR calibration. The proposed method exhibited a better performance when comparisons were made with the other derivative or normal fluorescence spectra. The calibration set was designed with 23 spiked samples with concentrations of DPA in the range of 10e100 mg kg1. Validation, RMSEP and REP% were also studied. The MLR models showed a good correlation between the normal fluorescence spectra and the real value of DPA content in the apple and orange samples. Moreover, in the case of pear, combination of
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PCR and synchronous fluorescence presented the best results. The results of the present study could prove that rapid synchronous and normal fluorescence measurements may be conducted directly on the fruit samples and used for quantitative determination of DPA, after an appropriate calibration, with no need for further sample preparation steps. Acknowledgments This work has been supported by grants from the Tarbiat Modares University Research Council, which is hereby gratefully acknowledged. References Aghamohammadi, M., Hashemi, J., Kram, G. A., & Alizadeh, N. (2007). Enhanced synchronous spectrofluorimetric determination of aflatoxin B1 in pistachio samples using multivariate analysis. Analytica Chimica Acta, 582(2), 288e294. Baghel, A., Joshi, S., & Amlathe, S. (2012). Journal of Chemical and Pharmaceutical Research, 4(5), 2704e2711. Bramlage, W. (1988). Apple scald, a complex problem. Post Harvest Pomology Newsletter, 6(2), 11e14. Brereton, R. G. (2000). Introduction to multivariate calibration in analytical chemistry. Analyst, 125(11), 2125e2154. Brereton, R. G. (2003). Chemometrics: Data analysis for the laboratory and chemical plant. Wiley. Domínguez-Vidal, A., Ortega-Barrales, P., & Molina-Díaz, A. (2007). Environmental water samples analysis of pesticides by means of chemometrics combined with fluorimetric multioptosensing. Journal of Fluorescence, 17(3), 271e277. Drouillet-Pinard, P., Boisset, M., Périquet, A., Lecerf, J.-M., Casse, F., Catteau, M., et al. (2010). Realistic approach of pesticide residues and French consumer exposure within fruit & vegetable intake. Journal of Environmental Science and Health Part B, 46(1), 84e91. Drzyzga, O. (2003). Diphenylamine and derivatives in the environment: a review. Chemosphere, 53(8), 809e818. Esbensen, K. H., Guyot, D., Westad, F., & Houmøller, L. P. (2002). Multivariate data analysis-in practice: An introduction to multivariate data analysis and experimental design. In Multivariate Data Analysis. Gamon, M., Lleo, C., Ten, A., & Mocholi, F. (2001). Multiresidue determination of pesticides in fruit and vegetables by gas chromatography/tandem mass spectrometry. Journal of AOAC International, 84(4), 1209e1216. García-Reyes, J. F., Ortega-Barrales, P., & Molina-Díaz, A. (2005). Rapid determination of diphenylamine residues in apples and pears with a single multicommuted fluorometric optosensor. Journal of Agricultural and Food Chemistry, 53(26), 9874e9878. Garrido, J., De Alba, M., Jimenez, I., Cadado, E., & Folgeiras, M. L. (1998). Gas chromatographic determination of diphenylamine in apples and pears: method
validation and results of Spanish official Residue Monitoring Program 1995. Journal of AOAC International, 81(3), 648e651. Gilbert-Lopez, B., García-Reyes, J. F., Ortega-Barrales, P., Molina-Díaz, A., & Fernández-Alba, A. R. (2007). Analyses of pesticide residues in fruit-based baby food by liquid chromatography/electrospray ionization time-of-flight mass spectrometry. Rapid Communications in Mass Spectrometry, 21(13), 2059e2071. Hashemi, J., Kram, G. A., & Alizadeh, N. (2008). Enhanced spectrofluorimetric determination of aflatoxin B1 in wheat by second-order standard addition method. Talanta, 75(4), 1075e1081. Ibañez, G. A. (2008). Partial least-squares analysis of time decay data for Eu(III)e tetracycline complexes: simultaneous luminescent determination of tetracycline and oxytetracycline in bovine serum. Talanta, 75(4), 1028e1034. Ignat, I., Volf, I., & Popa, V. I. (2011). A critical review of methods for characterisation of polyphenolic compounds in fruits and vegetables. Food Chemistry, 126(4), 1821e1835. Mas, S., de Juan, A., Tauler, R., Olivieri, A. C., & Escandar, G. M. (2010). Application of chemometric methods to environmental analysis of organic pollutants: a review. Talanta, 80(3), 1052e1067. Nepote, A. J., Damiani, P. C., & Olivieri, A. C. (2003). Chemometrics assisted spectroscopic determination of vitamin B6, vitamin B12 and dexamethasone in injectables. Journal of Pharmaceutical and Biomedical Analysis, 31(4), 621e627. Nguyen, T. D., Yun, M. Y., & Lee, G.-H. (2009). A multiresidue method for the determination of 118 pesticides in vegetable juice by gas chromatographye mass spectrometry and liquid chromatographyetandem mass spectrometry. Journal of Agricultural and Food Chemistry, 57(21), 10095e10101. http:// dx.doi.org/10.1021/jf902712v. Olek, M. (1988). Determination of diphenylamine residues in apples, and 4-aminobiphenyl residues in diphenylamine, by high-performance liquid chromatography and electrochemical detection. Journal of Chromatography, 447(2), 421e425. Rudell, D. R., Mattheis, J. P., & Fellman, J. K. (2005). Evaluation of diphenylamine derivatives in apple peel using gradient reversed-phase liquid chromatography with ultravioletevisible absorption and atmospheric pressure chemical ionization mass selective detection. Journal of Chromatography A, 1081(2), 202e209. Saad, B., Haniff, N. H., Idiris Saleh, M., Hasani Hashim, N., Abu, A., & Ali, N. (2004). Determination of ortho-phenylphenol, diphenyl and diphenylamine in apples and oranges using HPLC with fluorescence detection. Food Chemistry, 84(2), 313e317. Samadi-Maybodi, A., & Darzi, S. (2008). Simultaneous determination of vitamin B12 and its derivatives using some of multivariate calibration 1 (MVC1) techniques. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 70(5), 1167e1172. Sena, M. M., Trevisan, M. G., & Poppi, R. J. (2006). Combining standard addition method and second-order advantage for direct determination of salicylate in undiluted human plasma by spectrofluorimetry. Talanta, 68(5), 1707e1712. Sholberg, P., Harlton, C., Haag, P., Lévesque, C., O’Gorman, D., & Seifert, K. (2005). Benzimidazole and diphenylamine sensitivity and identity of Penicillium spp. that cause postharvest blue mold of apples using b-tubulin gene sequences. Postharvest Biology and Technology, 36(1), 41e49. Tong, Y., Wu, Z., Yang, C., Yu, J., Zhang, X., Yang, S., et al. (2001). Determination of diphenylamine stabilizer and its nitrated derivatives in smokeless gunpowder using a tandem MS method. Analyst, 126(4), 480e484.
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LWT - Food Science and Technology journal homepage: www.elsevier.com/locate/lwt
Determination of diphenylamine residue in fruit samples using spectrofluorimetry and multivariate analysis Alireza Farokhcheh, Naader Alizadeh* Department of Chemistry, Faculty of Science, Tarbiat Modares University, P.O. Box 14115-175, Tehran, Iran
a r t i c l e i n f o
a b s t r a c t
Article history: Received 28 January 2013 Received in revised form 15 April 2013 Accepted 18 May 2013
Determination of diphenylamine residue in fruit samples was studied based on normal, synchronous first- and second- derivative spectrofluorimetry. These methods were performed using three of the most widely employed multivariate calibration techniques which are partial least squares, multiple linear regression and principal component regression. Eighteen combinational methods were tested to present the best model for determination of diphenylamine residue. The prediction performance of the calibration models, which was constructed on the basis of these methods, was also compared. For a range of concentrations from 10 to 100 mg kg1 of diphenylamine in the fruit prediction set, the values of root mean square error and relative error of prediction, using multiple linear regressions, were determined in the range of 3.3e4.1 mg kg1 and 6.4e8.0%, respectively. Repeatability studies were satisfactory, giving RSD% values of 1.8, 5.6, and 3.3 for apple, pear, and orange, respectively. The calibration graphs were linear in the range of 10e100 mg kg1 and detection limits were between 4 and 7 mg kg1. The presented method was successfully applied to determine diphenylamine residue in some of naturally treated fruit samples and the results indicated proper agreement with those obtained by HPLC analysis. Ó 2013 Elsevier Ltd. All rights reserved.
Keywords: Diphenylamine Food analysis Fruit samples Multivariate analysis Spectrofluorimetry
1. Introduction Pesticides are a class of chemicals which have been widely used at various stages of food crops cultivation and during their postharvest storage and play an important role in the intensification of agricultural production. Diphenylamine (DPA) is one of the most used pesticides worldwide. It is used as a pre- or postharvest scald inhibitor for some fruits include apples and pears. Its anti-scald activity is the result of its antioxidant properties, which protect the fruit skin from the oxidation products of alpha-farnesene during storage (Bramlage, 1988). Therefore, residues of DPA are often found in agricultural crops. However, the presence of pesticide residues in foods can be considered as a hazard to human health (Drzyzga, 2003; Sholberg et al., 2005). According to EU regulations in foodstuffs, the maximum residue levels (MRLs) for DPA are 5 and 10 mg kg1 for apples and pears, respectively (Drouillet-Pinard et al., 2010). Therefore, qualitative and quantitative determination of DPA in these materials is of
Abbreviations: DPA, Diphenylamine; PLS, partial least squares; MLR, multiple linear regression; PCR, principal component regression; MRLs, maximum residue levels; RCF, relative centrifugal force; PRESS, Prediction Error Sum of Squares; REP, relative error of prediction. * Corresponding author. Fax: þ98 21 82883455. E-mail address: [email protected] (N. Alizadeh). 0023-6438/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.lwt.2013.05.032
biological and environmental importance. The techniques which have been most frequently used to determine DPA levels are as follows: chromatographic (Garrido, De Alba, Jimenez, Cadado, & Folgeiras, 1998), electrochemical (Olek, 1988), ultraviolet and visible spectrophotometric (Rudell, Mattheis, & Fellman, 2005) and fluorimetric methods (Saad et al., 2004). However, chromatographic methods typically require clean-up steps after extraction in order to remove the interfering substances. On the other hand, fluorescence-based methods are highly sensitive and simple as compared to the chromatographic methods; but problems with interferences in complex food matrices can occur during analysis. This situation necessitates separation steps to better enable the analyte determination (Sena, Trevisan, & Poppi, 2006). Generally, analyte extraction and sample preparation is the most challenging and time-consuming step in the analytical process. Chemometric methods present valuable information which are extracted from multivariate data arrays and would be difficult to process if classical statistical methods are used. Fluorescence studies provide a large amount of data containing multiple parameters for which interpretation is far from obvious. In our previous articles (Aghamohammadi, Hashemi, Kram, & Alizadeh, 2007; Hashemi, Kram, & Alizadeh, 2008), the combination of synchronous fluorescence spectroscopy and multivariate calibration methods was successfully applied for determination of the aflatoxin B1 in pistachio and wheat samples, implying that the chemometric
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methods exhibit a high capability to extract valuable information in the case of complicated matrices. The multivariate calibration methods such as MLR, principal component regression analysis (PCR) and partial least squares regression analysis (PLS) have been widely used for mutual correlation of two data sets in food analysis (Ignat, Volf, & Popa, 2011; Mas, de Juan, Tauler, Olivieri, & Escandar, 2010). Multivariate calibration methods (Brereton, 2003), applied to absorptive and emissive spectral data as well as electrochemical signals, are increasingly being used for the analysis of complex biological mixtures. These methods have the advantage of using full spectral information and allow for a rapid determination of mixture components, often with no need of prior separation or sample pretreatment. Multivariate statistical methods such as PCR and PLS have been applied in the analysis of mixtures using mainly infrared, UVeVis absorption spectroscopy or fluorescence spectroscopy (Ibañez, 2008). Domínguez-Vidal et al. reported the use of chemometrics combined with fluorimetric multioptosensing for the determination of pesticides in environmental water samples (Domínguez-Vidal, Ortega-Barrales, & Molina-Díaz, 2007). In this study, the potential of combining normal, synchronous and derivative fluorimetry with multivariate methods for the quantitative analysis of DPA in fruit samples is evaluated. The combination of spectrofluorimetry with multivariate calibration is simple, fast, specific and sensitive, and may allow the development of methods for determining DPA in food products in particular those with complicated matrices. The proposed method requires no prior separation or derivatization process. The added-found method as well as naturally contaminated samples was used to validate the results. This method has been successfully applied for determination of DPA residue in fruit samples. 2. Materials and methods
7
The SPSS version 10.0 software was employed for the statistical treatment of data. Moreover, a house-written visual basic program, based on the algorithms described by Brereton (2000), was used for application of the PLS multivariate calibration method. 2.3. Sample treatment Fruit samples (apple, pear, and orange) were purchased from different local markets in Tehran, Iran. An amount of 1 kg of each fruit sample was ground and homogenized using a high-speed blender. Then, 20 g portion of this sample was weighed and placed in a centrifuge tube and 5 g sodium acetate, 20 g sodium sulphate and 40 mL ethyl acetate were added as well. After that, the tube was vigorously shaken for 2 min. DPA was extracted by liquide liquid extraction (Saad et al., 2004), and then transferred to the organic phase. The extract was filtered through 0.45 mm cellulose acetate membrane filters. Finally, an extract containing the equivalent 0.5 g of sample per mL in ethyl acetate was obtained. Then, 2 mL of this extract was evaporated to dryness under a flow of nitrogen at room temperature. The dry residues were dissolved in 1 mL methanol. The obtained colloidal solution was centrifuged at 4000 rpm (the relative centrifugal force, RCF is 2634) for 2 min. In order to reduce the fluorescence background, the sample solution was diluted 15 times with methanol and then used as an analytical sample. Samples used for recovery studies and matrix background evaluation were previously tested by HPLC method and proved to be free from the investigated pesticide (DPA). The final solution was used as a sample for obtaining the fluorescence spectra. The standards and samples were protected from direct light during all the procedures. The above described method was used for preparation of blank fruit sample and spiked with a known quantity of DPA in calibration set samples and prediction set samples. It should be note that DPA is often problematic in ultratrace residue analysis due to carry-over and cross-contamination issues from other sources.
2.1. Reagents and standards 2.4. Preparation of calibration and validation sets Diphenylamine (DPA) was purchased from Merck, Germany. Other used reagents were of analytical grade. Double-distilled water was used throughout. Stock solutions of DPA at the concentration of 0.02 mol L1 were prepared in methanol and kept in the dark at 4 C. Working concentrations of DPA were prepared from the stock solutions by appropriate dilution before use. All samples prepared from fruits were filtered through 0.45 mm cellulose acetate membrane filters before further use. Fruit samples (pear, apple and orange) were purchased from markets in Tehran province (Tehran, Iran). 2.2. Apparatus The fluorescence spectra were recorded by a PerkineElmer model LS 50B spectrofluorimeter equipped with a thermostated cell compartment. The DPA fluorescence intensity was measured at the maximum emission wavelength of 365 nm after excitation of solutions at 282 nm. The scan rate of the monochromators was maintained at 50 nm min1 in recording conventional spectra and at 100 nm min1 for total synchronous fluorescence spectra. All measurements were performed in 10 mm quartz cells, at 25 0.1 C, by use of a thermostatic cell holder and a Thermomix thermostatic bath. Absorption spectra were obtained using a Sinco (model UV S-2100) UVeVis spectrophotometer. The liquid chromatographic analysis was performed on a HPLC system with UVe Vis detector, model Smartline 2500 (Knauer, Germany). A C18 column 4.6 i.d. 250 mm, 5.0 mm (Knauer, Germany) was used for separations. The mobile phase was methanol/water (70:30 v/v) and operated at 1 mL min1. The absorption detector was fixed at 282 nm and the peak area was used as the quantification parameter.
Two sets of standard solutions were prepared; the calibration and validation sets. Calibration set was used to build the calibration model while the effectiveness of the proposed model for prediction was confirmed in the validation set. Twenty six standard solutions for the calibration set and 23 solutions for the prediction set were randomly chosen, which were used to validate the calibration model. In order to make comparisons among the results of the three models, the training and prediction sets were chosen similarly for all the three models. The spectrum for each of the samples was recorded from 300 to 500 nm for normal and 270e470 nm for synchronous fluorescence spectra where the spectra are subdivided into 1.0 nm intervals. The response data of the calibration set was a matrix with 26 200 dimensions. The concentration of DPA in each analytical sample was between 10 and 100 mg kg1. 3. Results and discussion 3.1. Contour plots Fig. 1a shows the UVeVis absorption spectrum and fluorescence emission spectra of DPA in methanolic solution. It is obvious that DPA has an absorption peak at 282 nm and when excited by radiation, shows intrinsic fluorescence maximum which is located at 365 nm. Fig. 1b shows the contour plot of total synchronous fluorescence corresponding to 70 mg L1 DPA in methanolic solution. The selection of the optimum Dl and excitation wavelength for the recording synchronous fluorescence spectra, in order to perform the determination process, was examined properly by collecting
8
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emission maxima of 282e365 nm. In order to achieve the maximum extraction recovery, different organic solvents were examined during the experiment including ethyl acetate, acetone, ethanol, methanol and acetonitrile. The results indicated that no significant difference was detected upon examination of various solvents. However, ethyl acetate was selected as the extraction solvent because of creating less background emission in comparison with the other investigated solvents. Therefore, methanol was selected as the solvent for DPA in this research. 3.3. Matrix of calibration and selection of the spectral zones for the analysis Six different data sets, selected from the emission spectra, the synchronous spectra and their first and second derivatives, were evaluated to perform the determination. Fig. 2 shows some typical normal fluorescence, first and second derivatives of fluorescence spectra and synchronous spectra with its first and second derivatives. Since full-spectrum methods (MLR, PCR, PLS) are able to use many wavelengths, one could conclude that wavelength selection is unnecessary and all the available wavelengths are often used. The spectral regions between 300 and 500 nm for emission and between 270 and 470 nm for synchronous path were selected for the analysis. The reason for choosing those particular regions is that the maximum spectral information is available within those regions and no relevant information is obtained beyond them. MLR, PCR and PLS were employed to perform the determination. A randomly designed calibration set of 23 analytical samples with DPA in the concentration range of 10e100 mg kg1 and fruit samples matrices (prepared as described in experimental section) was used to statistically maximize the information content in the spectra. The optimum dimensionality of the PCR methods was obtained from the PRESS function (Prediction Error Sum of Squares) Eq. (1).
PRESS ¼
N X
2
ðb y i yi Þ
(1)
i¼1
Fig. 1. Absorbance (left) and fluorescence (right) spectra of 104 mol L1 DPA in methanolic solution (a), contour plots of total synchronous fluorescence of 70 mg L1 DPA in methanolic solution (b) and calibration curves of DPA in different solvents (c).
the total synchronous fluorescence spectra of analyte, in the form of a Dl-excitation matrix. The synchronous fluorescence spectra were collected by scanning the excitation wavelength between 260 and 440 nm in the wavelength interval 20e250 nm (at l increments of 5 nm), and were displayed as contour plots. The plot shows that the maximum fluorescence intensity was observed at excitation wavelength of 340 nm (Dl ¼ 80 nm). These values are in conformity with those which could be obtained from emission and excitation normal spectra (Fig. 1a). 3.2. Solvent effect on the fluorescence intensity of DPA It is known that the type of solvent could have a significant effect on fluorescence intensity. Calibration curves, which were used for determination of fluorescence sensitivity of DPA in different solvents, are shown in Fig. 1c. As can be seen, the highest level of sensitivity can be achieved in methanol media using excitation-
^i are the real concenwhere N is the number of samples, yi and y tration and the predicted concentration of sample i, respectively. In order to achieve the best predictions, it is of great importance to select how many principal components or factors to use in the calibration with the PLS and PCR algorithms. In PLS and PCR methods, PRESS was calculated by cross-validation (Esbensen, Guyot, Westad, & Houmøller, 2002; Samadi-Maybodi & Darzi, 2008) leaving out one sample at a time, to model the system. The prediction of the concentration corresponding to the sample left out was carried out by means of a model obtained using the N1 remaining samples. The minimum number of factors was chosen to build the model for which the PRESS had no significant differences for the N > 6. For example, Fig. 3a shows the PRESS diagram of PCR calibration for pear samples. As can be seen, the minimum value of PRESS was resulted using three factors for prediction in crossvalidation method. For this reason, three factors were selected as the optimum dimensionality of the PCR method. The optimum numbers of factors used for each model are shown in Table 1. A prediction set of 23 fruit analytical samples (prepared as described in sample treatment section) with known spiked amount of DPA in the range of 10e100 mg kg1 was used to evaluate the methods. The prediction ability of each method was expressed as the RMSEP which stands for root mean-square error of prediction. RMSEP is an indicator of the average error in the analysis expressed in the original measurement unit Eq. (2) (Brereton, 2003; Nepote, Damiani, & Olivieri, 2003). Another useful parameter is the REP%
A. Farokhcheh, N. Alizadeh / LWT - Food Science and Technology 54 (2013) 6e12
9
Fig. 2. Normal (apples and oranges) and synchronous (pears) fluorescence spectra obtained for fruit samples. Solid line (d) and dash line (- -) show first and second derivative spectra, respectively.
Fig. 3. PRESS diagram for applying PCR on synchronous fluorescence spectra of calibration set for pears (a) and correlation between real and predicted concentrations of DPA in prediction set samples by MLR model of normal fluorescence spectra, apples (b), oranges (c) and PCR model of synchronous fluorescence spectra (d).
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Table 1 Results of root mean-square error of prediction (RMSEP) and percent of relative error of prediction (REP%) obtained for prediction set by applying different multivariate methods on normal and synchronous spectra of calibration set of fruit samples. Multivariate method
MLR
Fluorescence method
Normal
Synchronous
PCR
Normal
Synchronous
PLS
Normal
Synchronous
a b c d
D.O.a
0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2
Nb
4 4 3 4 3 3 4 5 3 3 3 3
Pear
N
Orange
N
Apple
RMSEPc
REP%d
RMSEP
REP%
RMSEP
REP%
11.2 13.2 15.5 12.6 16.8 12.2 7.4 13.7 7.9 6.2 4.1 5.8 12.2 12.8 12.2 11.2 12.5 12.0
21.5 25.4 29.8 24.2 32.4 23.4 14.2 26.3 15.2 11.8 8.0 11.2 23.5 24.6 23.4 21.6 24.1 23.0
6.2 3.3 15.1 7.2 7.6 9.0 9.1 11.4 25.2 10.0 10.4 10.5 11.3 7.4 12.7 14.2 13.5 10.2
11.8 6.4 29.1 13.8 14.7 17.4 17.5 21.9 48.4 19.2 20.0 20.1 21.7 14.3 24.4 27.2 26.0 19.7
5.6 8.7 3.3 5.6 7.6 8.3 9.7 12.8 15.1 8.7 12.2 12.7 12.3 10.5 7.4 8.0 11.9 8.7
10.8 16.8 6.4 10.8 14.6 16.0 18.6 24.5 29.1 16.7 23.4 24.4 23.7 20.2 14.2 15.4 22.9 16.7
3 5 3 5 3 5 3 3 3 4 4 4
3 4 3 5 4 3 3 3 4 4 5 3
Derivative order. The number of factors used for the modeling. Root mean-square error of prediction. Relative error of prediction.
Table 2 Comparison of the proposed method with other analytical techniques for determination of DPA in different samples. Analytical technique
Description
Sample
DLRa
LODb
Response time (min)
Recovery (%)
RSDc (%)
Ref.
Color scanner detects the product on TLC strips
The method is based on the reaction of diphenylamine with ninhydrin gives an iminium salt, on heating which formed orange color dye in acidic medium A tandem mass spectrometric method is established and mass spectrometer parameters optimized A method based on electron impact (EI)/MS/MS was developed
Apple & pear
0.3e5.1 mg mL1
0.18 mg mL1
10
96e105
<6.4
Baghel, Joshi, & Amlathe, 2012
Smokeless gunpowder
5.0e200 ng mL1
1.0 ng mL1
e
80.3 4.9
<11.3d
Tong et al., 2001
Orange & vegetables
e
0.4 ppb
12
e
e
Apple & pear
0.25e5 mg kg1
0.06 mg kg1
e
78e104
<3
Gamon, Lleo, Ten, & Mocholi, 2001 García-Reyes, Ortega-Barrales, & Molina-Díaz, 2005
Vegetable juice
e
<5 mg kg1
e
77e114 (average recovery)
<14
Nguyen, Yun, & Lee, 2009
Baby food matrix with 100% fruit content
e
1 mg kg1
e
81.5
9.5
Apple Orange Pear
12e100 mg kg1 10e85 mg kg1 15e85 mg kg1
4 mg kg1 6 mg kg1 7 mg kg1
98e102
2e6
Gilbert-López, García-Reyes, Ortega-Barrales, Molina-Díaz, & Fernández-Alba, 2007 This work
Tandem mass spectrophotometry EI/MS/MS
Single multicommuted fluorometric optosensor
LC/ESI-MS/MS
LC/ESI-TOF-MS
Spectrofluorimetry
a b c d
A single flow injection multicommuted system using solid-surface (solid support: C18 silica gel) fluorescence spectroscopy has been explored for the determination of diphenylamine in apples and pears Determination was performed using gas chromatography with mass spectrometric detection (GCeMS-SIM) and liquid chromatography electrospray ionization tandem mass spectrometry (LC/ESI-MS/MS) The developed method consists of a sample treatment step based LLE followed SPE
Multivariate calibration applied to normal and synchronous fluorescence spectrometry.
Dynamic linear range. Limit of detection. Relative standard deviation. Intra-assay precision.
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11
Table 3 Determination of DPA in naturally contaminated fruit samples using fluorimetry method in combination with multivariate analysis (proposed method) and HPLC. DPA concentration (mg kg1)
Spike (mg kg1)
Pears HPLC (mg kg1)
Sample 1
A.Rc (%) Sample 2
A.R (%) Sample 3
0 30 60
5.5 0.4 36.7 0.4 64.1 0.3
0 30 60
13.4 0.6 46.2 0.2 74.0 0.7
0 30 60
10.9 0.3 40.2 0.5 71.9 0.4
A.R (%)
Oranges PCR
t-value
a
e 37.1 0.3 65.3 0.6 101.48 14.4 0.5 45.5 0. 9 75.2 0.1 102.52 11.2 0.8 38.9 0.7 72.6 0.4 100.16
HPLC (mg kg1) b
1.38 3.10
e 33.1 0.6 58.9 0.4
2.22 1.32 2.94
e 30.2 0.9 57.6 0.1
0.61 2.62 2.14
e 29.7 0.6 58.4 0.2
Apples t-value
HPLC (mg kg1)
MLR
t-value
0.9 1.0
0.48 2.57
17.8 0.3 48.3 0.6 76.8 0.4
3.81 3.10 3.48
0.7 0.3
0.75 6.57d
23.5 0.3 54.2 0.7 84.5 0.2
0.8 0.5
2.77 6.75d
14.6 0.3 43.1 0.4 74.5 0.5
16.7 0.4 46.9 0.5 78.6 0.8 97.75 24.4 0.5 55.7 0.3 84.1 0.6 102.04 13.4 0.8 42.9 0.4 76.1 0.6 97.82
MLR e 32.8 57.3 98.18 e 29.7 56.4 98.13 e 28.1 60.5 99.10
2.67 3.41 1.10 2.43 0.61 3.54
a
Out of calibration range. Not detected. c Average recovery. d Statistically significant at 0.05 level and insignificant at the level of 0.01; The Critical t-value for two tail distribution student paired t-test having degree of freedom 2 and level of confidence of 95% is 4.30 and for level of confidence of 99% is 9.92. b
(percent of relative error of prediction) of the model which could be calculated by Eq. (3).
RMSEP ¼
N 1 X 2 ðb y yi Þ N i¼1 i
!0:5
N 100 1 X 2 ðb y yi Þ REP% ¼ y N i¼1 i
(2) !0:5 (3)
where y is the average analyte concentration in the calibration set. 3.4. Results for prediction set The chemometric methods were successfully applied to the analysis of DPA in fruit samples (apple, pear and orange). The results presented in Table 1 clearly indicate which models have been the most effective. Table 1 shows that, in the case of apple fruit, the prediction ability of methods including Nos. 1, 3 and 4, and in the case of orange fruit, the Nos. 1, 2, 4 and 14 among all the 18 tested methods were better than that of the other ones. The most accurate results for the above-mentioned achievements were obtained using a MLR model calculated from the first and second derivative fluorescence spectra of calibration set correlating the DPA concentration (mg kg1) of samples to fluorescence intensity. On the other hand, for analysis of DPA in pear sample, the most accurate results (Nos. 10, 11 and 12) were obtained using a MLR model calculated from the first derivative synchronous spectra. Fig. 3b shows the correlation between real and predicted concentrations of DPA in prediction set samples for apple, orange and pear, respectively. These results indicate that the model can accurately predict the DPA concentration of samples in the presence of highly interfering fruits matrices. 3.5. Figure of merits of spectrofluorimetry-multivariate for analysis of fruit samples The calibration graphs were linear in the range of 12e100,
mg kg1 and limit of detections (LODs) were between 4 and 7 mg kg1. The LODs were obtained as the sample concentration which causes a peak that is three times as high as the baseline noise level. LODs for analysis of each fruit sample are shown in Table 2
and compared with other reports in literature. Repeatability studies were satisfactory, giving RSD% values of 1.8, 5.6, and 3.3 for apple, pear, and orange, respectively. Reproducibility of the selected multivariate methods was checked by recording independent series of ten samples for each fruit on two consecutive days; when reproducibility studies were undertaken over the two sets of ten standards for each fruit on consecutive days no significant differences were found between the two sets of ten replicates at a confidence level of 95%. 3.6. Spectrofluorimetry-multivariate analysis of natural fruit samples and comparison with HPLC analysis In this work, application of the first and second derivative fluorescence spectra using MLR for the determination of DPA, in the case of orange and apple samples, is reported. It is also demonstrated that combination of the first derivative synchronous spectra and PCR can be used to determine DPA in the pear samples. DPA was analyzed in naturally treated spiked and non-spiked fruit samples. Each determination was repeated three times for all the fruit samples (spiked and non-spiked samples) and sample preparation steps were carried out separately for each run. The results of naturally treated fruit samples were compared with the HPLC method which has been performed in separated laboratories. The Student’s t-test indicates that the differences between the predicted values of concentrations are not significant. Similarly, spiked fruit samples were analyzed by synchronous and normal fluorimetry. Of interesting to note is that the DPA content in the naturally treated fruit samples was found to be in good agreement with that determined by HPLC method. These results are summarized in Table 3. 4. Conclusion DPA in fruit samples (apple, orange and pear) was determined by synchronous and normal fluorimetry in combination with MLR or PCR calibration. The proposed method exhibited a better performance when comparisons were made with the other derivative or normal fluorescence spectra. The calibration set was designed with 23 spiked samples with concentrations of DPA in the range of 10e100 mg kg1. Validation, RMSEP and REP% were also studied. The MLR models showed a good correlation between the normal fluorescence spectra and the real value of DPA content in the apple and orange samples. Moreover, in the case of pear, combination of
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PCR and synchronous fluorescence presented the best results. The results of the present study could prove that rapid synchronous and normal fluorescence measurements may be conducted directly on the fruit samples and used for quantitative determination of DPA, after an appropriate calibration, with no need for further sample preparation steps. Acknowledgments This work has been supported by grants from the Tarbiat Modares University Research Council, which is hereby gratefully acknowledged. References Aghamohammadi, M., Hashemi, J., Kram, G. A., & Alizadeh, N. (2007). Enhanced synchronous spectrofluorimetric determination of aflatoxin B1 in pistachio samples using multivariate analysis. Analytica Chimica Acta, 582(2), 288e294. Baghel, A., Joshi, S., & Amlathe, S. (2012). Journal of Chemical and Pharmaceutical Research, 4(5), 2704e2711. Bramlage, W. (1988). Apple scald, a complex problem. Post Harvest Pomology Newsletter, 6(2), 11e14. Brereton, R. G. (2000). Introduction to multivariate calibration in analytical chemistry. Analyst, 125(11), 2125e2154. Brereton, R. G. (2003). Chemometrics: Data analysis for the laboratory and chemical plant. Wiley. Domínguez-Vidal, A., Ortega-Barrales, P., & Molina-Díaz, A. (2007). Environmental water samples analysis of pesticides by means of chemometrics combined with fluorimetric multioptosensing. Journal of Fluorescence, 17(3), 271e277. Drouillet-Pinard, P., Boisset, M., Périquet, A., Lecerf, J.-M., Casse, F., Catteau, M., et al. (2010). Realistic approach of pesticide residues and French consumer exposure within fruit & vegetable intake. Journal of Environmental Science and Health Part B, 46(1), 84e91. Drzyzga, O. (2003). Diphenylamine and derivatives in the environment: a review. Chemosphere, 53(8), 809e818. Esbensen, K. H., Guyot, D., Westad, F., & Houmøller, L. P. (2002). Multivariate data analysis-in practice: An introduction to multivariate data analysis and experimental design. In Multivariate Data Analysis. Gamon, M., Lleo, C., Ten, A., & Mocholi, F. (2001). Multiresidue determination of pesticides in fruit and vegetables by gas chromatography/tandem mass spectrometry. Journal of AOAC International, 84(4), 1209e1216. García-Reyes, J. F., Ortega-Barrales, P., & Molina-Díaz, A. (2005). Rapid determination of diphenylamine residues in apples and pears with a single multicommuted fluorometric optosensor. Journal of Agricultural and Food Chemistry, 53(26), 9874e9878. Garrido, J., De Alba, M., Jimenez, I., Cadado, E., & Folgeiras, M. L. (1998). Gas chromatographic determination of diphenylamine in apples and pears: method
validation and results of Spanish official Residue Monitoring Program 1995. Journal of AOAC International, 81(3), 648e651. Gilbert-Lopez, B., García-Reyes, J. F., Ortega-Barrales, P., Molina-Díaz, A., & Fernández-Alba, A. R. (2007). Analyses of pesticide residues in fruit-based baby food by liquid chromatography/electrospray ionization time-of-flight mass spectrometry. Rapid Communications in Mass Spectrometry, 21(13), 2059e2071. Hashemi, J., Kram, G. A., & Alizadeh, N. (2008). Enhanced spectrofluorimetric determination of aflatoxin B1 in wheat by second-order standard addition method. Talanta, 75(4), 1075e1081. Ibañez, G. A. (2008). Partial least-squares analysis of time decay data for Eu(III)e tetracycline complexes: simultaneous luminescent determination of tetracycline and oxytetracycline in bovine serum. Talanta, 75(4), 1028e1034. Ignat, I., Volf, I., & Popa, V. I. (2011). A critical review of methods for characterisation of polyphenolic compounds in fruits and vegetables. Food Chemistry, 126(4), 1821e1835. Mas, S., de Juan, A., Tauler, R., Olivieri, A. C., & Escandar, G. M. (2010). Application of chemometric methods to environmental analysis of organic pollutants: a review. Talanta, 80(3), 1052e1067. Nepote, A. J., Damiani, P. C., & Olivieri, A. C. (2003). Chemometrics assisted spectroscopic determination of vitamin B6, vitamin B12 and dexamethasone in injectables. Journal of Pharmaceutical and Biomedical Analysis, 31(4), 621e627. Nguyen, T. D., Yun, M. Y., & Lee, G.-H. (2009). A multiresidue method for the determination of 118 pesticides in vegetable juice by gas chromatographye mass spectrometry and liquid chromatographyetandem mass spectrometry. Journal of Agricultural and Food Chemistry, 57(21), 10095e10101. http:// dx.doi.org/10.1021/jf902712v. Olek, M. (1988). Determination of diphenylamine residues in apples, and 4-aminobiphenyl residues in diphenylamine, by high-performance liquid chromatography and electrochemical detection. Journal of Chromatography, 447(2), 421e425. Rudell, D. R., Mattheis, J. P., & Fellman, J. K. (2005). Evaluation of diphenylamine derivatives in apple peel using gradient reversed-phase liquid chromatography with ultravioletevisible absorption and atmospheric pressure chemical ionization mass selective detection. Journal of Chromatography A, 1081(2), 202e209. Saad, B., Haniff, N. H., Idiris Saleh, M., Hasani Hashim, N., Abu, A., & Ali, N. (2004). Determination of ortho-phenylphenol, diphenyl and diphenylamine in apples and oranges using HPLC with fluorescence detection. Food Chemistry, 84(2), 313e317. Samadi-Maybodi, A., & Darzi, S. (2008). Simultaneous determination of vitamin B12 and its derivatives using some of multivariate calibration 1 (MVC1) techniques. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 70(5), 1167e1172. Sena, M. M., Trevisan, M. G., & Poppi, R. J. (2006). Combining standard addition method and second-order advantage for direct determination of salicylate in undiluted human plasma by spectrofluorimetry. Talanta, 68(5), 1707e1712. Sholberg, P., Harlton, C., Haag, P., Lévesque, C., O’Gorman, D., & Seifert, K. (2005). Benzimidazole and diphenylamine sensitivity and identity of Penicillium spp. that cause postharvest blue mold of apples using b-tubulin gene sequences. Postharvest Biology and Technology, 36(1), 41e49. Tong, Y., Wu, Z., Yang, C., Yu, J., Zhang, X., Yang, S., et al. (2001). Determination of diphenylamine stabilizer and its nitrated derivatives in smokeless gunpowder using a tandem MS method. Analyst, 126(4), 480e484.