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Microchemical Journal 149 (2019) 104042
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Pesticide residues in fruits and vegetables: High-order calibration based on spectrofluorimetric/pH data
T
Ariana Paula Pagani, Gabriela Alejandra Ibañez
⁎
Departamento de Química Analítica, Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario, and Instituto de Química Rosario (IQUIR), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Suipacha 531, Rosario S2002LRK, Argentina
ARTICLE INFO
ABSTRACT
Keywords: Pesticides Third-order multivariate analysis Fluorescence excitation–emission matrices pH-gradient U–PLS/RTL
The aim of the present work was to quantify simultaneously four pesticides, thiabendazole, fuberidazole, carbaryl and naphthyl acetic acid, in vegetables and fruits, measuring excitation–emission fluorescence matrices in a flow injection system with double pH–gradient. The generated third–order data were processed by applying multivariate calibration procedure unfolded partial least squares coupled to residual trilinearization (U–PLS/ RTL), permitting overcome the severe spectra overlapping among the analytes and also with the background of real samples. The vegetable tissues were fortified at concentration levels taking into account the maximum residue level, acquiring good recoveries for all analytes, with values between 80 and 115%. The developed method enabled determining the analytes in complex samples (lettuce, pear, orange and mushroom) with a simple extraction step with ethyl acetate, in an appropriate analysis time and without sophisticated instruments.
1. Introduction A pesticide is defined as any substance or mixture of substances intended for preventing, destroying, repelling, or mitigating any pests (insects, mites, nematodes, weeds, rats, etc.). Based on their targeted pests, they can be categorized into insecticides, herbicides, fungicides and various other substances [1]. They provide a great benefit in ensuring plentiful crops, reducing the cost of food for domestic use and export. However, pesticide residues in fruits and vegetables are among the primary sources of pesticide exposure through diet, producing several diseases (cancer, chronic obstructive pulmonary disease, birth defects, infertility) and more damages to human health [2]. Thus, monitoring these compounds is extremely important to ensure that only permitted levels of pesticide are consumed. The maximum residue limits (MRLs) for pesticides in each particular food have been regulated by different organizations, such as the Codex Alimentarius Commission [3], the European Union [4], and the United Nations Food and Agriculture Organization [5], among others. Determinations of pesticide residues mostly involve two main steps: extraction of analytes from the bulk of the matrix and clean–up of the analytes from any co–extractives presence in the matrix. Many options have been proposed for the pre–treatment and extraction of pesticide residues in food, such as solid–liquid extraction (SLE) or liquid–liquid extraction (LLE), QuEChERS (quick, easy, cheap, effective, rugged and
⁎
safe, for its acronym in English), extraction with supercritical fluids or pressurized liquids, as well as solid phase microextraction. In the case of LLE or SLE, the process usually involves homogenizing the sample with pure or mixture organic solvents, depending on the matrix/analyte combination. A review of the existing literature indicates that among the solvents most commonly used in the analysis of pesticide residues are ethyl acetate, acetonitrile, hexane and acetone [6,7]. Pesticide detection has traditionally been carried out by employing conventional chromatographic techniques, including high–performance liquid chromatography (HPLC) [8], gas chromatography (GC) [9] and mass spectrometry (MS) [10,11]. Although these techniques offer powerful trace analysis with excellent sensitivity and high reproducibility, they present many drawbacks, such as sophisticated equipment, time consuming, tedious sample preparation and purification steps. Recently, another alternative analytical techniques have also been published, such as immunochemical assays [12,13], capillary electrophoresis [14], and the use of biosensors [15]. Considerable endeavours have been devoted to investigating alternative strategies for analyzing pesticides in a facile, speedy, sensitive, selective, accurate, and user–friendly manner [7]. Fluorescence spectroscopy is a versatile analytical tool mainly because of its high sensitivity, and its selectivity when considering the spectral shapes produced by means of the analysis of single chemical component solutions. In addition, the spectrofluorimetric equipment is relatively easier to run and its cost is so much
Corresponding author. E-mail address: [email protected] (G.A. Ibañez).
https://doi.org/10.1016/j.microc.2019.104042 Received 4 April 2019; Received in revised form 25 June 2019; Accepted 25 June 2019 Available online 27 June 2019 0026-265X/ © 2019 Elsevier B.V. All rights reserved.
Microchemical Journal 149 (2019) 104042
A.P. Pagani and G.A. Ibañez
cheaper than other more sophisticated instruments. Although the number of pesticides exhibiting an intrinsic fluorescence is limited, there are various kinds of derivatization techniques such as photochemical, chemical and electrochemical methods, through which the analyte with no or weak native fluorescence can converse into strongly fluorescent species [16]. Thus, fluorescence methods open another opportunity for trace analysis of pesticides. However, one of the main difficulties in the use of spectroscopic techniques in the field of quantitative analysis of multicomponent systems, such as pesticides multiresidues in complex samples, is the spectral overlap between compounds as well as the serious interferences of background signal. In this context, chemometric offers efficient alternatives to achieve the required selectivity to perform the analysis, avoiding experimental manipulations of the sample. Second–order calibration is a well–known method, which allows accurate quantification of analytes in samples with unexpected components that are potential interferences, achieving the “second–order advantage” [17]. Additionally, higher calibration methods enable better performance since they introduce more analytical information, attaining an increase in the predictive ability of the models, a high tolerance for heavily collinear data, and an improvement in sensitivity and selectivity [18]. In recent years, a series of new third–order calibration methods have been developed to resolve third–order data of complex samples [17,19]. For pesticides determination in different samples, spectroscopic signal in function of time (elution or kinetic time) combined with chemometric tools have been used, e.g. chromatographic UV–Vis/ fluorescence emission spectra second–order data [20–22], excitation–emission matrices (EEMs) modulated by hydrolysis time third–order data [23]. The main goal of this work is to develop a method for the simultaneous determination of four pesticides, thiabendazole (TBZ), fuberidazole (FBZ), carbaryl (CBL) and naphthyl acetic acid (NAA) in different vegetables and fruits (see Table 1), employing fluorescence EEMs as a function of pH third–order data. As in previous works [24,25], the spectroscopic data were collected using a fast–scan spectrofluorometer and the pH gradient was generated by a flow injection system. Although, the data arrays were analyzed with different third–order algorithms, successful results were obtained using unfolded partial minimum squares (U–PLS) combined with residual trilinealization (RTL) [17,26]. This algorithm was able to overcome the challenges that may be caused by the pH gradient generated through flow methodology, and by the strong superposition of both emission and excitation spectra exhibit within the compounds and the
matrix signal. After a simple extraction procedure, the proposed method was successfully applied to the quantification of the studied pesticides in lettuce, pear, orange and mushroom. 2. Experimental 2.1. Chemicals and reagents All experiments were performed with analytical grade chemicals. The following solutions were employed: acetic acid (HAc) 1.0 × 10−2 mol L−1, prepared from commercial HAc (Merck, Darmstadt, Germany); sodium hydroxide (NaOH) 1.0 × 10−3 mol L−1 obtained by dilution of Sörensen solution prepared from commercial NaOH (Analar, Poole, England). Ultrapure water provided by a MilliQ purification system was used. Methanol and ethyl acetate (Merck, Darmstadt, Germany) were HPLC grade. Carbaryl, thiabendazole, fuberidazole and naphthyl acetic acid were purchased from Sigma (Seelze, Germany). Stock solutions (all 500 mg L−1) were prepared by dissolving the exact amount of the corresponding compound in methanol. These solutions were stored at 4 °C and were stable for at least a month. Methanolic working solutions of 20.0 mg L−1 were daily prepared. 2.2. Instrumental Fluorescence measurements were performed using a fast scanning Cary Eclipse spectrofluorometer of Agilent (Santa Clara, California, United States), equipped with two Czerny–Turner monochromators and a xenon flash lamp, and connected to a microprocessor in a PC by a serial interface IEE 488 (GBIP). The flow injection system consisted on a Gilson Minipuls Evolution peristaltic pump (Gilson, Middleton, WI, USA) for the propulsion of the carrier solution and a dual proportional Upchurch injection valve (Upchurch scientific, Oak Harbor, WA, USA) for manual injection. The flow was driven into a quartz Hellma flow cell model 176.752–QS, 25 μL inner volume, 1.5 mm optical path length (Hellma, Müllheim, Germany). Temperature of the cell holder was controlled by means of a Lauda Alpha A12 thermostatic bath (Frankfurt, Germany). While the sample was flowing through the measurement cell, EEMs were collected according the following parameters: excitation wavelength range, 210–310 nm each 4 nm; emission wavelength range, 320–410 nm each 4 nm. Detecting voltage was set at 750 V and the slit widths were fixed both at 5 nm for the excitation and emission
Table 1 Pesticides under study. NAME
SPECIFIC ACTION
DISSOCIATION CONSTANTS
MRLs
Thiabendazole TBZa
Fungicide
pKa1 ~ 0.5 pKa2 = 4.8 pKa3 = 11.3
Lettuce
Pear
Orange
Mushroom
50
4,000
7,000
10,000
Fuberidaloze FBZa
Fungicide
pKa1 = 5.0 pKa2 = 11.7
Carbaryl CBLb
Insecticide
pKa = 12.02
50
10
10
50
10,000
12,000
10,000
10
Naphthyl acetic acid NAAa
Growth regulator
60
150
100
60
a b
STRUCTURE
pKa = 4.24
MRLs values (μg kg-1) were taken from European Commission–pesticides database [4]. MRLs values (μg kg-1) were taken from FAO [5]. 2
Microchemical Journal 149 (2019) 104042
A.P. Pagani and G.A. Ibañez
2.4. Calibration and validation samples A 21–sample calibration set was prepared by duplicate as follows: seventeen samples had concentrations provided by a small central composite design and the remaining four solutions contained only one of the studied analytes at the high level concentrations (see Table S1, Supplementary material). The extreme concentrations for the design were as follows: 0–80 μg L−1 for FBZ, 0–400 μg L−1 for TBZ, 0–500 μg L−1 for NAA and 0–800 μg L−1 for CBL (five concentration levels for each pesticide). The ranges were selected taking into account both lineal range and the fluorescent signal of each studied compound. Validation samples (11 samples by duplicate) were prepared containing the analytes at random concentrations within the respective calibration ranges. Calibration and validation solutions were obtained starting from appropriate volumes of methanolic working solutions (20.0 mg L−1) of the four analytes, simultaneously placed in two 20.00 mL volumetric flask. The methanolic aliquots were evaporated to dryness under a stream of nitrogen, and finally completed to the mark with HAc 1.0 × 10−2 mol L−1 to be employed as carrier, or with NaOH 1.0 × 10−3 mol L−1 to be injected into the flow system. 2.5. Sample processing of vegetal tissues Samples of romaine lettuce (Lactuca sativa), orange (Citrus sinensis), pear (Pyrus communis), and mushroom (Agaricus bisporus) were purchased from local commercial markets. Tissue samples were chopped and crushed for 5 min (pear and orange were crushed with peel). Representative 15.000 g portion was accurately weighed and placed in a 15 mL centrifuge tube. Samples were spiked with the assayed analytes by adding the appropriate amount of methanolic stock solutions, in order to obtain concentrations near or below the corresponding MRL in each vegetable. Samples were allowed to stand for 20 min in the dark at room temperature before extraction with 7.00 mL of ethyl acetate. After ultrasonic sonication for 20 min, the mixture was centrifuged at 15,000 rpm for 10 min. Two aliquots of the organic phase (both 3.00 mL) were transferred into two 10.00 mL flasks and evaporated to dryness under a stream of nitrogen. The evaporated residues were resuspended with HAc 1.0 × 10−2 mol L−1 to be employed as carrier, or with NaOH 1.0 × 10−3 mol L−1 to be injected into the flow system. All solutions were sonicated for 15 min for complete redissolution and filtered with 0.2 μm pore–size nylon membrane.
Fig. 1. Fluorescence spectra of pesticides (A) excitation and (B) emission for TBZ (cyan), NAA (red), FBZ (dark blue) and CBL (dark green); at different pH values: pH = 2 in solid line, pH = 7 in dotted line and pH = 11 in dashed line for all the analytes. Inset Emission spectra of CBL (dark green) at different pH values and its metabolite naphthol (pink).
monochromators. A scanning speed of 19,200 nm/min was employed, registering a complete EEM in about 20 s, and 30 successive matrices for each sample in a total time of 10 min. The third–order array of 26 × 24 × 30 data points was saved in ASCII format for ensuing manipulation with the multivariate programs. For tissue process, an ultrasonic bath Cole Palmer 8820 (Niles IL, USA) and a high–speed microcentrifuge 2012 (Dragon–Lab, Beijing, China) were employed.
2.6. Software
2.3. Flow injection methodology
3.1. U–PLS/RTL
With the aim of generating the double pH gradient inside the flow stream, the alkaline sample was injected into the acid sample used as carrier. The peristaltic pump was used for the propulsion of the acid sample solution (HAc 1.0 × 10−2 mol L−1) fixing the flow rate at 0.15 mL min−1. After the alkaline sample (NaOH 1.0 × 10−3 mol L−1) was manually injected (1.00 mL), the flow was sent to the spectrofluorimeter flow cell, passing first through a Teflon tube mixer (10 cm length, 1.5 mm i.d.) and then, through a Teflon reactor (total length 4 m, 0.5 mm i.d.) (see Fig. S1, Supplementary material). EEMs collection under the instrumental conditions detailed in Section 2.2 was done 7 min after the sample injection.
Calibration with the U–PLS algorithm implies building a classical PLS model after unfolding the calibration data into vectors, without including data for the unknown sample [29]. The Ical third–order calibration data arrays (size J × K × L, where J, K and L are the numbers of data points in each measuring mode) are thus transformed into uni–dimensional arrays (J × K × L × 1 vectors), and a PLS model is built using this data together with the vector of calibration concentrations y (size Ical × 1). This provides a set of loadings P and weight loadings W (both of size JKL × A, where A is the number of latent factors), as well as regression coefficient v (size A × 1). The parameter A can be select by techniques such as leave–one–out cross–validation [30].
The routines employed to perform the calculations described in this work are written in MATLAB 7.0. All algorithms were implemented using the graphical interface of the MVC3 (Multivariate Calibration for third–order) toolbox [27], which is freely available on the Internet [28]. 3. Theory
3
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Fig. 2. Contour plots of (A) EEMs–pH third–order data for a solution containing the four pesticides under study. Each contour plot corresponds to a point in the pH dimension, representing the evolution along the pH gradient; (B) Contour plots of EEMs for tissue samples after pre–treatment, collected in the aforementioned spectral regions. Each contour plot corresponds to the first pH sensor; tissues are free of pesticides (see text).
If no unsuspected interferences occur in the test sample, v can be employed to estimate the analyte concentration. On the other hand, if unexpected constituents occur in the test sample, the residuals of the U–PLS prediction step will be abnormally large in comparison with the typical instrumental noise level and the sample scores are unsuitable for analyte prediction. This situation can be handled by a procedure called residual trilinearization (RTL), based on a Tucker3 decomposition that models the interferent effects in a flexible manner using a latent structure [26]. The number of interferents Ni can be assessed by comparing the final residuals su with the instrumental noise level. Typically, a plot of su computed for trial number of components will show decreasing values, starting at the residual of prediction before RTL (sp) when the number of components is equal to A, until it stabilizes at a value compatible with the experimental noise, allowing to locate the correct number of components. For a single unexpected component, this analysis is straightforward, and provides the corresponding interferents profiles in the three modes. For additional unexpected constituents, however, the retrieved profiles no longer resemble true spectral or pH profiles. Moreover, in this latter case, several different Tucker3 models could in principle be constructed, because the number of loadings may be different in each mode.
concentrations in the flow stream and achieving higher sensitivity. The methodology allows the generation of a double pH gradient: pH increases from the front of the flow reaching its maximum value at the center of the alkaline bolus, and then progressively decreases to reach its baseline value at the tail. The system has the complexity of evolution over time, so a high scanning rate and a very slow flow during the data collection were employed with the purpose of minimizing changes in the concentrations of the constituents as recording a complete EEM (about 20 s), while the pH evolves in the flow stream. The generated pH gradient in the system described in Section 2.2, was initially evaluated in a spectrophotometric flow cell using acid–base indicators with different pKa values (between 4 and 12), as previously discussed in a recent work [31]. According to the spectral changes obtained, we could infer that the proposed methodology generates a gradual and wide pH gradient, which would encompass a range of about pH 4 to 11. Pesticides under study are characterized by native fluorescence, and both UV–Vis and fluorescent spectral characteristics are pH dependent. Fluorescence of studied compounds was evaluated at different pH values, as shown in Fig. 1. As can be seen, FBZ and TBZ exhibit a high spectral overlap in emission mode (Fig. 1B), being the excitation mode the most selective (Fig. 1A). For NAA and CBL the emission spectra in acid medium are very similar, showing a slightly difference in their excitation spectra. In general, no spectral maxima shifts are observed when going from acid to alkaline pH values, just variations in fluorescence intensities. In the conversion of protoned into deprotoned forms, fluorescence decreases for FBZ and TBZ, whereas it increases for NAA. In the case of CBL, a noticeably decrease in the fluorescence emission intensity at 370 nm is observed, as well as the appearance of a maximum at 470 nm when the pH change from acid to alkaline values. Since its pKa = 12 (see Table 1) is not reached in the generated pH gradient (about 4 to 11), the spectral changes could not be justified based on the presence of acid–base equilibrium species. Hence, it is necessary to bear in mind the hydrolysis mechanism that this insecticide goes through in an alkaline
4. Results and discussion 4.1. General considerations Different experimental flow injection conditions were tested (i.e., composition and concentration of the solutions used in the injection, reactor lengths, injection loops and flow rate) in order to obtain a gradual pH variation, leading to suitable pH profiles. Each of the studied samples was diluted with HAc 1.0 × 10−2 mol L−1 and used as the carrier stream. The composition of the injected sample was identical to that of the carrier, except that the dilution was carried out with NaOH 1.0 × 10−3 mol L−1. This mode of generating the pH gradient has already been employed [24,25], preventing changes in total analyte 4
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A.P. Pagani and G.A. Ibañez
4.2. Calibration and validation samples In a first stage, EEMs–pH were recorded for calibration samples to build a four–way calibration model. Subsequently, recoveries and figures of merit were calculated using validation samples at several concentration levels in order to evaluate the predictive ability of the proposed methodology. In view of the results obtained in previous works [24,25] in which the pH gradient was generated by a slow and well synchronized flow injection method, validation samples were in principle evaluated with PARAFAC [35,36]. However, in this work this algorithm leads to poor predictions, maybe associated with loss of quadrilinearity in the data array [31]. In the system under study, the extensive spectral overlapping exhibited by the analytes both in excitation as in emission mode may preclude the decomposition of the three–way data in physically reasonable profiles, explaining the failure of trilinear models such as PARAFAC. This data can be conveniently analyzed by more flexible algorithms, such as U–PLS that can take into account quadrilinearity deviations, as suggested by Escandar and Olivieri in their review describing the road map for multiway calibration models [37]. Therefore, the data arrange was analyzed using unfolded PLS. The number of calibration PLS latent variables was determined using leave–one–out cross validation (CV) method described by Haaland and Thomas [30]. In general, the number of CV estimated latent variable should be similar to the known number of sources of variation in the calibration data, or probably larger if there are additional phenomena to be modeled. This study yielded an optimum value of four latent variables for FBZ, TBZ and NAA, consistent with a system containing four emitting constituents. However, for CBL an extra latent variable is required to explain the variance of the data. In this case, PLS probably takes into account additional factors of the signal of analytes that produce spectral variation, especially because for CBL there is a hydrolysis mechanism associated with pH, which may not be fully reproducible from sample to sample. Predicted vs. nominal concentration plot for validation samples was constructed (Fig. 3A), and a good correlation was observed for all pesticides. The elliptical joint confidence region (EJCR) statistical test was performed to prove the accuracy of the predictions. This test consists in verifying if the ideal point (slope = 1, intercept = 0) is included in the elliptical region of mutual confidence of the slope and intercept in the predicted vs. nominal concentration plot. Fig. 3B shows that the theoretically expected point lies inside the elliptical regions for all the analytes, indicating the accuracy of the proposed methodology.
Fig. 3. (A) Plots for predicted concentrations of TBZ (cyan), NAA (red), FBZ (dark blue) and CBL (dark green) as a function of the nominal values in the validation test samples; (B) the corresponding elliptical joint regions at 95% confidence level. The black point marks the theoretical (intercept = 0, slope = 1) point.
medium [32,33]. This fact can explain both the fluorescence decrease and the band observed at 470 nm consistent with naphthol, the main metabolite of CBL (Fig. 1B). In general, within pesticides, insecticides are more severely affected than herbicides and fungicides by hydrolysis mechanisms, having pH and temperature a direct impact on the kinetic reaction [34]. The stability in alkaline medium of all the pesticides under study was tested by following of fluorescence emission of the four analytes as a function of time at 20 °C. It was found that only CBL undergoes hydrolysis, immediately generating naphthol under working conditions, whereas FBZ, TBZ and NAA were stable over time in alkaline medium. Fig. 2A represents the third–order data for a solution containing the four pesticides under study, showing the evolution of EEMs with the spectral variations generated by the double pH gradient. The spectral changes observed are associated with the injection of the alkaline bolus into the carrier acid flow (both containing samples in identical composition), going from acid pH (sensor 1) to alkaline pH (sensors 15 to 20), and returning to the acid pH of the carrier sample (sensor 25). The spectral ranges were restricted in order to include enough information to perform the analysis and to avoid both Rayleigh and Raman scattering, as well as second harmonic from the diffraction grating.
4.3. Real samples The usefulness of the proposed method was tested by quantifying the studied pesticides in vegetal tissues (pear, orange, mushroom, lettuce). Even though the evaluated vegetables belong to regions of intensive agriculture, where agrochemicals are profusely used, samples did not contain pesticides at levels higher that the detection limits. Therefore, spiked samples were prepared with the analytes at different concentration levels and a recovery study was carried out in order to assert the accuracy of the developed methodology [38]. In order to minimize sample manipulation and the consumption of organic solvents, a simple extraction with pure ethyl acetate was proposed (no clean–up step was used). The same methodology was used for the different matrices. Fig. 2B shows the contour plots of EEMs for tissues samples, representing only the first pH sensor since no significant spectral changes are observed along the pH mode. As can be seen, vegetal tissue samples under study, even after pre–treatment, present an important spectral overlapping with the analytes, becoming essential the second–order advantage to separate pesticides signal from those of the signal background. In order to remove the matrix contribution, the complementary technique known as residual trilinearization (RTL) was used together with U–PLS to accurately quantify the 5
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Table 2 Recovery study of pesticides in tissue samples using U–PLS/RTL. FBZa
LETTUCE
ORANGE
PEAR
MUSHROOM
texperimental tcritical a b
TBZa
NAAa
CBLa
Added
Found
R%b
Added
Found
R%b
Added
Found
R%b
Added
Found
R%b
28.6 66.6 19.0 19.0 38.1 42.8 33.3 38.1 na 18.6 na 9.3 na na 27.9 37.2 na – 13.3 20.0 26.7 26.7 66.7 46.7 53.3 40.0 13.3 30.7 53.3 26.7 40.0 20.0 50.7 46.7 86.7 60.0 66.7 26.7
28.6 63.8 17.1 19.0 40.0 40.0 35.2 38.1 nd 19.5 nd 10.2 nd nd 30.7 35.0 nd – 13.3 18.7 22.7 26.7 61.3 48.0 50.7 40.0 13.3 29.3 54.7 25.3 40.0 18.7 48.0 48.0 88.0 56.0 68.0 29.3 1.89 2.04
100 96 90 100 105 93 106 100 – 104 – 110 – – 110 94 – – 100 93 85 100 92 102 95 100 100 96 102 95 100 93 95 103 101 93 102 110
57.1 85.7 na na 47.6 47.6 76.2 95.2 95.2 279 238 93 257 66.6 147 65 190 133 307 200 267 267 400 467 293 na 293 240 133 200 293 133 267 333 467 333 400 400
61.9 88.5 nd nd 41.9 46.6 73.3 78.1 104.7 251 238 84 238 68.5 142 75 190 147 267 200 240 227 333 467 293 nd 306 213 133 173 280 112 253 333 467 333 400 347 1.43 2.03
108 103 – – 88 98 96 82 110 90 100 90 93 102 96 115 100 110 87 100 90 85 83 100 100 – 104 89 100 87 96 84 95 100 100 100 100 87
95.2 47.6 66.6 na na na 142 95.2 47.6 372 66.6 232 142 47.6 279 325 133 400 333 533 467 640 667 533 66.7 na 160 na 400 na na 267 133 333 627 na na 200
114 47.6 63.8 nd nd nd 142 85.7 42.8 362 66.6 242 152 42.8 270 325 133 400 333 493 467 640 667 507 53.3 nd 160 nd 400 nd nd 267 146 293 640 nd nd 226 1.5 2.05
120 100 96 – – – 100 90 90 98 100 104 106 90 97 100 100 100 100 92 100 100 100 95 80 – 100 – 100 – – 100 110 88 102 – – 113
na na 238 171 124 124 476 314 190 232 428 93 285 238 186 279 238 133 400 200 800 267 400 333 533 267 80.0 na 267 na na 600 800 na 467 na na 133
nd nd 247 171 124 124 485 304 228 232 400 93 314 257 177 260 257 127 400 226 840 267 360 333 493 280 72.0 nd 267 nd nd 600 826 nd 413 nd nd 120 0.36 2.04
– – 104 100 100 100 102 97 120 100 93 100 110 108 95 94 108 95 100 113 105 100 90 100 92 105 90 – 100 – – 100 103 – 88 – – 90
Concentrations are given in μg kg−1; na: not added, nd: values below the corresponding LOD. Recovery (R%) is calculated as R% = C found/C added * 100 only when the analytes were added.
analytes. The estimation of the number of potentially interfering components or RTL components was done by considering the progression of the residual fit as the number of RTL components is increased (see Section 3). The residual fits after U–PLS/RTL analysis (7.0–8.5 units) stabilized at the order of the calibration residues (7.2–7.6 units), and considerably smaller than the residues before RTL (15.0–33.0 units), requiring only an extra latent variable to “clean” the EEMs data from the native signal of tissue samples. This indicates that U–PLS/RTL considers in all cases the matrix signal as containing a single mathematical component. Moreover, reasonable match was found between the spectral profiles retrieved by the program after RTL convergence in each mode with those corresponding to the background of the different tissue matrices (see Fig. S2, Supplementary material). Prediction of the analytes content free of the interference is possible once the PLS scores for each of the tissue samples are corrected with the RTL procedure. Table 2 shows the recoveries obtained confirming the good results achieved using the proposed strategy, with values between 93 and 106% for FBZ, 87–115% for TBZ, 80–120% for NNA, 89–113% for CBL. Recovery was calculated only when the analyte was added. Nevertheless, the non-added analytes were also predicted, obtaining values below the limits of detection corresponding to each matrix (predicted concentrations close to zero). The t-test was used to evaluate
Table 3 Statistical results using U–PLS/RTL for pesticides in validation samples and spiked tissue samples.
Validation samples Calibration range (μg L-1) SEN (μg L-1) γ (μg -1 L) LOD (μg L-1) LOQ (μg L-1) RMSEP (μg L-1)a REP %b Spiked vegetables tissuesc SEN (μg L-1) γ (μg -1 L) LOD (μg L-1) LOQ (μg L-1) RMSEP (μg L-1)a REP %b
FBZ
TBZ
NAA
CBL
0-80 46.5 5.4 0.7-5 2-13 1.3 3.4
0-400 9.5 1.2 3-20 9-40 17 8.5
0-500 7.6 1.0 4-10 10-50 14 5.6
0˗800 3.9 0.55 7-30 30-80 28 7.1
23 2.6 2-6 5-15 3.2 8.1
3.1 0.4 5-30 15-50 20 9.1
3.2 0.4 10-20 30-80 17 6.3
3.6 0.4 8-40 40-90 30 8.1
a
RMSEP: root mean square error of prediction. REP %: relative error of prediction, expressed with respect to the average concentration of the calibration samples. c The values correspond to the concentrations obtained after processing the samples (see Experimental section). b
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whether the obtained recoveries do not differ significantly from 100%. The calculated t values for all analytes were less than the critical value of t, suggesting that the developed method provides recoveries not different from 100, for a significance level of 0.05.
Appendix A. Supplementary data
4.4. Figures of merit
References
Figures of merit are important components of the analytical validation process, in order to establish detection capabilities and to compare the relative performances with different methodologies. Table 3 summarizes the figures of merit for the validation and real samples processed by U–PLS/RTL. Sensitivity (SEN) was calculated using equations found in the literature for third–order data [39], based on the relation of input noise in an analytical system. Analytical sensitivity (γ) was calculated from the ratio between sensitivity and instrumental noise, expressed in units on μg−1 L. The instrumental noise considered for this case was 7.2 AFU. Finally, the limits of detection (LODs) and the limits of quantitation (LOQs) were calculated in units of μg L−1 for each analyte, following a rigorous approach recommended by the International Union of Pure and Applied Chemistry (IUPAC) [40]. The values for tissue samples are in the final conditions of measurements, and they show a good precision and an appropriate sensitivity. In general, the complexity of real samples can justify the unfavourable changes in the statistical results with respect to the validation samples. Despite this, the results were very good, validating the effectiveness of RTL to separate the contribution of the components not modeled in the calibration step, restoring selectivity and rendering good predictions. Taking into account the tissue samples procedure, the resulting calculated LODs expressed in μg kg−1 are: 3–7; 6–34; 11–22; 9–45 for FBZ, TBZ, NAA and CBL, respectively. Likewise, the LOQs are calculated in the tissue samples expressed in μg kg−1: 6–16; 17–56; 34–90; 45–98 for FBZ, TBZ, NAA and CBL, respectively. According to the previous results, the proposed methodology allowed the determination of the studied compounds in fruits and vegetables with concentrations near the maximum permissible residue limits (see Table 1), for each of the analyzed matrices. It is noteworthy that sensitivity can be improved, if required, by employing a protocol that includes a higher sample amount and a small extraction volume. Moreover, it would be possible to extend the application of this methodology to other vegetable matrices.
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Okonkwo, Recent approaches to improving selectivity and sensitivity of enzyme–based biosensors for organophosphorus pesticides: a review, Talanta 155 (2016) 289–304. [16] J. Nie, B. Li, Y. Zhang, Z. Yi, Z. Cai, High–order calibration for the spectrofluorimetric determination of pesticides based on photochemical derivatization. A solution of the problems of inner–filter effects and matrix interferences in complex environmental water, Chemometr. Intell. Lab. Syst. 156 (2016) 36–53. [17] A. Olivieri, G. Escandar, Practical Three-way Calibration, First ed., Elsevier, UK, 2014. [18] A.C. Olivieri, Analytical advantages of multivariate data processing. One, two, three, infinity? Anal. Chem. 80 (2008) 5713–5720. [19] G.M. Escandar, H.C. Goicoechea, A. Muñoz de la Peña, A.C. Olivieri, Second– and higher–order data generation and calibration: a tutorial, Anal. Chim. Acta 806 (2014) 8–26. [20] J.A. Arancibia, A.C. Olivieri, V. Boeris, Determination of five pesticides in juice, fruit and vegetable samples by means of liquid chromatography combined with multivariate curve resolution, Anal. Chim. Acta 814 (2014) 23–30. [21] J.A. Arancibia, G.M. Escandar, Second–order chromatographic photochemically induced fluorescence emission data coupled to chemometric analysis for the simultaneous determination of urea herbicides in the presence of matrix co–eluting compounds, Anal. Methods (15) (2014) 5503–5511. [22] M. Carabajal, J.A. Arancibia, G.M. Escandar, A green–analytical chemistry method for agrochemical–residue analysis in vegetables, Microchem. J. 128 (2016) 34–41. [23] P. Santa–Cruz, A. García–Reiriz, Application of third–order multivariate calibration algorithms to the determination of carbaryl, naphthol and propoxur by kinetic spectroscopic measurements, Talanta 128 (2014) 450–459. [24] A. Pagani, G. Ibañez, Second–order multivariate models for the processing of standard–addition synchronous fluorescence pH data. Application to the analysis of salicylic acid and its major metabolite in human urine, Talanta 122 (2014) 1–7. [25] A. Pagani, G. Ibañez, Four–way calibration applied to the processing of pH–modulated fluorescence excitation–emission matrices. Analysis of fluoroquinolones in the presence of significant spectral overlapping, Microchem. J. 132 (2017) 211–218. [26] J.A. Arancibia, A.C. Olivieri, D. Bohoyo Gil, A. Espinosa Mansilla, I. Durán Merás,
Supplementary data to this article can be found online at https:// doi.org/10.1016/j.microc.2019.104042.
5. Conclusion The simultaneous determination of four pesticides in fruits and vegetables was achieved employing a four–way calibration model based on EEMs as pH function. U–PLS/RTL algorithm was decisive to achieve enough selectivity, resolving the high degree of spectral overlapping and rendering good results, even in the presence of an interfering signal from the background in real samples. Therefore, EEMs modulated by pH gradient in combination with chemometric tools, offer a new perspective for the quantification of pesticides for food control in a reasonable time, using non–sophisticated instrument, a simple sample pre–treatment, and reducing the use of environmentally dangerous organic solvents. Declaration of Competing Interest Authors declare no conflict of interest. Acknowledgments The authors gratefully acknowledge the Universidad Nacional de Rosario, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) and Agencia Nacional de Promoción Científica y Tecnológica (ANPCyT, Project PICT 2016-1122) for financially supporting this work. 7
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A. Muñoz de la Peña, Trilinear least–squares and unfolded–PLS coupled to residual trilinearization: new chemometric tools for the analysis of four–way instrumental data, Chemometr. Intell. Lab. Syst. 80 (2006) 77–86. S.J. Mazivila, S.A. Bortolato, A.C. Olivieri, MVC3_GUI: a MATLAB graphical user interface for third–order multivariate calibration. An upgrade including new multi–way models, Chemometr. Intell. Lab. Syst. 173 (2018) 21–29. IQUIR–CONICET, http://www.iquir-conicet.gov.ar/descargas/mvc3.rar , Accessed date: June 2019. S. Wold, P. Geladi, K. Esbensen, J. Øhman, Multi–way Principal Components and PLA, (1987). D.M. Haaland, E.V. Thomas, Partial least–squares methods for spectral analyses. Relation to other quantitative calibration methods and the extraction of qualitative information, Anal. Chem. 60 (1988) 1193–1202. A. Pagani, G. Ibañez, Analytical approach for the simultaneous determination of quinolones in edible animal products. Eling pH–modulated fluorescence excitation–emission matrices modeling pH–modulated fluorescence excitation–emission matrices four–way arrays, Talanta 192 (2019) 52–60. D. Hawker, Kinetics of carbaryl hydrolysis: an undergraduate environmental chemistry laboratory, J. Chem. Educ. 92 (2015) 1531–1535. A.S. Gunasekara, A.L. Rubin, K.S. Goh, F.C. Spurlock, R.S. Tjeerdema, Environmental fate and toxicology of carbaryl, Rev. Environ. Contam. Toxicol. 196
(2008) 95–121. [34] C. Cruces Blanco, F. Garcia Sanchez, A spectrophotometric and spectrofluorimetric study of the chemical behavior of the insecticide carbaryl: ground and excited state pk values of its metabolite l–naphthol, J. Photochem. Photobiol. A Chem. 42 (1988) 357–373. [35] A. Smilde, R. Bro, P. Geladi, Multi–way Analysis: Applications in the Chemical Sciences, Wiley, Chichester, 2004. [36] R. Bro, Review on multi–way analysis in chemistry–2000–2005, Crit. Rev. Anal. Chem. 36 (2006) 279–293. [37] G.M. Escandar, A.C. Olivieri, A road map for multi-way calibration models, Analyst 142 (2017) 2862–2873. [38] M. Thompson, S.L.R. Ellison, R. Wood, Harmonized guidelines for single laboratory validation of methods of analysis, Pure Appl. Chem. 74 (2002) 835–855. [39] R.M. Maggio, P. Damiani, A. Olivieri, Four–way kinetic–excitation–emission fluorescence data processed by multi–way algorithms. Determination of carbaryl and 1–naphthol in water samples in the presence of fluorescent interferents, Anal. Chim. Acta 677 (2010) 97–107. [40] F. Allegrini, A.C. Olivieri, Multi–way figures of merit in the presence of heteroscedastic and correlated instrumental noise: unfolded partial least–squares with residual multi–linearization, Chemometr. Intell. Lab. Syst. 158 (2016) 200–209.
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Pesticide residues in fruits and vegetables: High-order calibration based on spectrofluorimetric/pH data
T
Ariana Paula Pagani, Gabriela Alejandra Ibañez
⁎
Departamento de Química Analítica, Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario, and Instituto de Química Rosario (IQUIR), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Suipacha 531, Rosario S2002LRK, Argentina
ARTICLE INFO
ABSTRACT
Keywords: Pesticides Third-order multivariate analysis Fluorescence excitation–emission matrices pH-gradient U–PLS/RTL
The aim of the present work was to quantify simultaneously four pesticides, thiabendazole, fuberidazole, carbaryl and naphthyl acetic acid, in vegetables and fruits, measuring excitation–emission fluorescence matrices in a flow injection system with double pH–gradient. The generated third–order data were processed by applying multivariate calibration procedure unfolded partial least squares coupled to residual trilinearization (U–PLS/ RTL), permitting overcome the severe spectra overlapping among the analytes and also with the background of real samples. The vegetable tissues were fortified at concentration levels taking into account the maximum residue level, acquiring good recoveries for all analytes, with values between 80 and 115%. The developed method enabled determining the analytes in complex samples (lettuce, pear, orange and mushroom) with a simple extraction step with ethyl acetate, in an appropriate analysis time and without sophisticated instruments.
1. Introduction A pesticide is defined as any substance or mixture of substances intended for preventing, destroying, repelling, or mitigating any pests (insects, mites, nematodes, weeds, rats, etc.). Based on their targeted pests, they can be categorized into insecticides, herbicides, fungicides and various other substances [1]. They provide a great benefit in ensuring plentiful crops, reducing the cost of food for domestic use and export. However, pesticide residues in fruits and vegetables are among the primary sources of pesticide exposure through diet, producing several diseases (cancer, chronic obstructive pulmonary disease, birth defects, infertility) and more damages to human health [2]. Thus, monitoring these compounds is extremely important to ensure that only permitted levels of pesticide are consumed. The maximum residue limits (MRLs) for pesticides in each particular food have been regulated by different organizations, such as the Codex Alimentarius Commission [3], the European Union [4], and the United Nations Food and Agriculture Organization [5], among others. Determinations of pesticide residues mostly involve two main steps: extraction of analytes from the bulk of the matrix and clean–up of the analytes from any co–extractives presence in the matrix. Many options have been proposed for the pre–treatment and extraction of pesticide residues in food, such as solid–liquid extraction (SLE) or liquid–liquid extraction (LLE), QuEChERS (quick, easy, cheap, effective, rugged and
⁎
safe, for its acronym in English), extraction with supercritical fluids or pressurized liquids, as well as solid phase microextraction. In the case of LLE or SLE, the process usually involves homogenizing the sample with pure or mixture organic solvents, depending on the matrix/analyte combination. A review of the existing literature indicates that among the solvents most commonly used in the analysis of pesticide residues are ethyl acetate, acetonitrile, hexane and acetone [6,7]. Pesticide detection has traditionally been carried out by employing conventional chromatographic techniques, including high–performance liquid chromatography (HPLC) [8], gas chromatography (GC) [9] and mass spectrometry (MS) [10,11]. Although these techniques offer powerful trace analysis with excellent sensitivity and high reproducibility, they present many drawbacks, such as sophisticated equipment, time consuming, tedious sample preparation and purification steps. Recently, another alternative analytical techniques have also been published, such as immunochemical assays [12,13], capillary electrophoresis [14], and the use of biosensors [15]. Considerable endeavours have been devoted to investigating alternative strategies for analyzing pesticides in a facile, speedy, sensitive, selective, accurate, and user–friendly manner [7]. Fluorescence spectroscopy is a versatile analytical tool mainly because of its high sensitivity, and its selectivity when considering the spectral shapes produced by means of the analysis of single chemical component solutions. In addition, the spectrofluorimetric equipment is relatively easier to run and its cost is so much
Corresponding author. E-mail address: [email protected] (G.A. Ibañez).
https://doi.org/10.1016/j.microc.2019.104042 Received 4 April 2019; Received in revised form 25 June 2019; Accepted 25 June 2019 Available online 27 June 2019 0026-265X/ © 2019 Elsevier B.V. All rights reserved.
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cheaper than other more sophisticated instruments. Although the number of pesticides exhibiting an intrinsic fluorescence is limited, there are various kinds of derivatization techniques such as photochemical, chemical and electrochemical methods, through which the analyte with no or weak native fluorescence can converse into strongly fluorescent species [16]. Thus, fluorescence methods open another opportunity for trace analysis of pesticides. However, one of the main difficulties in the use of spectroscopic techniques in the field of quantitative analysis of multicomponent systems, such as pesticides multiresidues in complex samples, is the spectral overlap between compounds as well as the serious interferences of background signal. In this context, chemometric offers efficient alternatives to achieve the required selectivity to perform the analysis, avoiding experimental manipulations of the sample. Second–order calibration is a well–known method, which allows accurate quantification of analytes in samples with unexpected components that are potential interferences, achieving the “second–order advantage” [17]. Additionally, higher calibration methods enable better performance since they introduce more analytical information, attaining an increase in the predictive ability of the models, a high tolerance for heavily collinear data, and an improvement in sensitivity and selectivity [18]. In recent years, a series of new third–order calibration methods have been developed to resolve third–order data of complex samples [17,19]. For pesticides determination in different samples, spectroscopic signal in function of time (elution or kinetic time) combined with chemometric tools have been used, e.g. chromatographic UV–Vis/ fluorescence emission spectra second–order data [20–22], excitation–emission matrices (EEMs) modulated by hydrolysis time third–order data [23]. The main goal of this work is to develop a method for the simultaneous determination of four pesticides, thiabendazole (TBZ), fuberidazole (FBZ), carbaryl (CBL) and naphthyl acetic acid (NAA) in different vegetables and fruits (see Table 1), employing fluorescence EEMs as a function of pH third–order data. As in previous works [24,25], the spectroscopic data were collected using a fast–scan spectrofluorometer and the pH gradient was generated by a flow injection system. Although, the data arrays were analyzed with different third–order algorithms, successful results were obtained using unfolded partial minimum squares (U–PLS) combined with residual trilinealization (RTL) [17,26]. This algorithm was able to overcome the challenges that may be caused by the pH gradient generated through flow methodology, and by the strong superposition of both emission and excitation spectra exhibit within the compounds and the
matrix signal. After a simple extraction procedure, the proposed method was successfully applied to the quantification of the studied pesticides in lettuce, pear, orange and mushroom. 2. Experimental 2.1. Chemicals and reagents All experiments were performed with analytical grade chemicals. The following solutions were employed: acetic acid (HAc) 1.0 × 10−2 mol L−1, prepared from commercial HAc (Merck, Darmstadt, Germany); sodium hydroxide (NaOH) 1.0 × 10−3 mol L−1 obtained by dilution of Sörensen solution prepared from commercial NaOH (Analar, Poole, England). Ultrapure water provided by a MilliQ purification system was used. Methanol and ethyl acetate (Merck, Darmstadt, Germany) were HPLC grade. Carbaryl, thiabendazole, fuberidazole and naphthyl acetic acid were purchased from Sigma (Seelze, Germany). Stock solutions (all 500 mg L−1) were prepared by dissolving the exact amount of the corresponding compound in methanol. These solutions were stored at 4 °C and were stable for at least a month. Methanolic working solutions of 20.0 mg L−1 were daily prepared. 2.2. Instrumental Fluorescence measurements were performed using a fast scanning Cary Eclipse spectrofluorometer of Agilent (Santa Clara, California, United States), equipped with two Czerny–Turner monochromators and a xenon flash lamp, and connected to a microprocessor in a PC by a serial interface IEE 488 (GBIP). The flow injection system consisted on a Gilson Minipuls Evolution peristaltic pump (Gilson, Middleton, WI, USA) for the propulsion of the carrier solution and a dual proportional Upchurch injection valve (Upchurch scientific, Oak Harbor, WA, USA) for manual injection. The flow was driven into a quartz Hellma flow cell model 176.752–QS, 25 μL inner volume, 1.5 mm optical path length (Hellma, Müllheim, Germany). Temperature of the cell holder was controlled by means of a Lauda Alpha A12 thermostatic bath (Frankfurt, Germany). While the sample was flowing through the measurement cell, EEMs were collected according the following parameters: excitation wavelength range, 210–310 nm each 4 nm; emission wavelength range, 320–410 nm each 4 nm. Detecting voltage was set at 750 V and the slit widths were fixed both at 5 nm for the excitation and emission
Table 1 Pesticides under study. NAME
SPECIFIC ACTION
DISSOCIATION CONSTANTS
MRLs
Thiabendazole TBZa
Fungicide
pKa1 ~ 0.5 pKa2 = 4.8 pKa3 = 11.3
Lettuce
Pear
Orange
Mushroom
50
4,000
7,000
10,000
Fuberidaloze FBZa
Fungicide
pKa1 = 5.0 pKa2 = 11.7
Carbaryl CBLb
Insecticide
pKa = 12.02
50
10
10
50
10,000
12,000
10,000
10
Naphthyl acetic acid NAAa
Growth regulator
60
150
100
60
a b
STRUCTURE
pKa = 4.24
MRLs values (μg kg-1) were taken from European Commission–pesticides database [4]. MRLs values (μg kg-1) were taken from FAO [5]. 2
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2.4. Calibration and validation samples A 21–sample calibration set was prepared by duplicate as follows: seventeen samples had concentrations provided by a small central composite design and the remaining four solutions contained only one of the studied analytes at the high level concentrations (see Table S1, Supplementary material). The extreme concentrations for the design were as follows: 0–80 μg L−1 for FBZ, 0–400 μg L−1 for TBZ, 0–500 μg L−1 for NAA and 0–800 μg L−1 for CBL (five concentration levels for each pesticide). The ranges were selected taking into account both lineal range and the fluorescent signal of each studied compound. Validation samples (11 samples by duplicate) were prepared containing the analytes at random concentrations within the respective calibration ranges. Calibration and validation solutions were obtained starting from appropriate volumes of methanolic working solutions (20.0 mg L−1) of the four analytes, simultaneously placed in two 20.00 mL volumetric flask. The methanolic aliquots were evaporated to dryness under a stream of nitrogen, and finally completed to the mark with HAc 1.0 × 10−2 mol L−1 to be employed as carrier, or with NaOH 1.0 × 10−3 mol L−1 to be injected into the flow system. 2.5. Sample processing of vegetal tissues Samples of romaine lettuce (Lactuca sativa), orange (Citrus sinensis), pear (Pyrus communis), and mushroom (Agaricus bisporus) were purchased from local commercial markets. Tissue samples were chopped and crushed for 5 min (pear and orange were crushed with peel). Representative 15.000 g portion was accurately weighed and placed in a 15 mL centrifuge tube. Samples were spiked with the assayed analytes by adding the appropriate amount of methanolic stock solutions, in order to obtain concentrations near or below the corresponding MRL in each vegetable. Samples were allowed to stand for 20 min in the dark at room temperature before extraction with 7.00 mL of ethyl acetate. After ultrasonic sonication for 20 min, the mixture was centrifuged at 15,000 rpm for 10 min. Two aliquots of the organic phase (both 3.00 mL) were transferred into two 10.00 mL flasks and evaporated to dryness under a stream of nitrogen. The evaporated residues were resuspended with HAc 1.0 × 10−2 mol L−1 to be employed as carrier, or with NaOH 1.0 × 10−3 mol L−1 to be injected into the flow system. All solutions were sonicated for 15 min for complete redissolution and filtered with 0.2 μm pore–size nylon membrane.
Fig. 1. Fluorescence spectra of pesticides (A) excitation and (B) emission for TBZ (cyan), NAA (red), FBZ (dark blue) and CBL (dark green); at different pH values: pH = 2 in solid line, pH = 7 in dotted line and pH = 11 in dashed line for all the analytes. Inset Emission spectra of CBL (dark green) at different pH values and its metabolite naphthol (pink).
monochromators. A scanning speed of 19,200 nm/min was employed, registering a complete EEM in about 20 s, and 30 successive matrices for each sample in a total time of 10 min. The third–order array of 26 × 24 × 30 data points was saved in ASCII format for ensuing manipulation with the multivariate programs. For tissue process, an ultrasonic bath Cole Palmer 8820 (Niles IL, USA) and a high–speed microcentrifuge 2012 (Dragon–Lab, Beijing, China) were employed.
2.6. Software
2.3. Flow injection methodology
3.1. U–PLS/RTL
With the aim of generating the double pH gradient inside the flow stream, the alkaline sample was injected into the acid sample used as carrier. The peristaltic pump was used for the propulsion of the acid sample solution (HAc 1.0 × 10−2 mol L−1) fixing the flow rate at 0.15 mL min−1. After the alkaline sample (NaOH 1.0 × 10−3 mol L−1) was manually injected (1.00 mL), the flow was sent to the spectrofluorimeter flow cell, passing first through a Teflon tube mixer (10 cm length, 1.5 mm i.d.) and then, through a Teflon reactor (total length 4 m, 0.5 mm i.d.) (see Fig. S1, Supplementary material). EEMs collection under the instrumental conditions detailed in Section 2.2 was done 7 min after the sample injection.
Calibration with the U–PLS algorithm implies building a classical PLS model after unfolding the calibration data into vectors, without including data for the unknown sample [29]. The Ical third–order calibration data arrays (size J × K × L, where J, K and L are the numbers of data points in each measuring mode) are thus transformed into uni–dimensional arrays (J × K × L × 1 vectors), and a PLS model is built using this data together with the vector of calibration concentrations y (size Ical × 1). This provides a set of loadings P and weight loadings W (both of size JKL × A, where A is the number of latent factors), as well as regression coefficient v (size A × 1). The parameter A can be select by techniques such as leave–one–out cross–validation [30].
The routines employed to perform the calculations described in this work are written in MATLAB 7.0. All algorithms were implemented using the graphical interface of the MVC3 (Multivariate Calibration for third–order) toolbox [27], which is freely available on the Internet [28]. 3. Theory
3
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Fig. 2. Contour plots of (A) EEMs–pH third–order data for a solution containing the four pesticides under study. Each contour plot corresponds to a point in the pH dimension, representing the evolution along the pH gradient; (B) Contour plots of EEMs for tissue samples after pre–treatment, collected in the aforementioned spectral regions. Each contour plot corresponds to the first pH sensor; tissues are free of pesticides (see text).
If no unsuspected interferences occur in the test sample, v can be employed to estimate the analyte concentration. On the other hand, if unexpected constituents occur in the test sample, the residuals of the U–PLS prediction step will be abnormally large in comparison with the typical instrumental noise level and the sample scores are unsuitable for analyte prediction. This situation can be handled by a procedure called residual trilinearization (RTL), based on a Tucker3 decomposition that models the interferent effects in a flexible manner using a latent structure [26]. The number of interferents Ni can be assessed by comparing the final residuals su with the instrumental noise level. Typically, a plot of su computed for trial number of components will show decreasing values, starting at the residual of prediction before RTL (sp) when the number of components is equal to A, until it stabilizes at a value compatible with the experimental noise, allowing to locate the correct number of components. For a single unexpected component, this analysis is straightforward, and provides the corresponding interferents profiles in the three modes. For additional unexpected constituents, however, the retrieved profiles no longer resemble true spectral or pH profiles. Moreover, in this latter case, several different Tucker3 models could in principle be constructed, because the number of loadings may be different in each mode.
concentrations in the flow stream and achieving higher sensitivity. The methodology allows the generation of a double pH gradient: pH increases from the front of the flow reaching its maximum value at the center of the alkaline bolus, and then progressively decreases to reach its baseline value at the tail. The system has the complexity of evolution over time, so a high scanning rate and a very slow flow during the data collection were employed with the purpose of minimizing changes in the concentrations of the constituents as recording a complete EEM (about 20 s), while the pH evolves in the flow stream. The generated pH gradient in the system described in Section 2.2, was initially evaluated in a spectrophotometric flow cell using acid–base indicators with different pKa values (between 4 and 12), as previously discussed in a recent work [31]. According to the spectral changes obtained, we could infer that the proposed methodology generates a gradual and wide pH gradient, which would encompass a range of about pH 4 to 11. Pesticides under study are characterized by native fluorescence, and both UV–Vis and fluorescent spectral characteristics are pH dependent. Fluorescence of studied compounds was evaluated at different pH values, as shown in Fig. 1. As can be seen, FBZ and TBZ exhibit a high spectral overlap in emission mode (Fig. 1B), being the excitation mode the most selective (Fig. 1A). For NAA and CBL the emission spectra in acid medium are very similar, showing a slightly difference in their excitation spectra. In general, no spectral maxima shifts are observed when going from acid to alkaline pH values, just variations in fluorescence intensities. In the conversion of protoned into deprotoned forms, fluorescence decreases for FBZ and TBZ, whereas it increases for NAA. In the case of CBL, a noticeably decrease in the fluorescence emission intensity at 370 nm is observed, as well as the appearance of a maximum at 470 nm when the pH change from acid to alkaline values. Since its pKa = 12 (see Table 1) is not reached in the generated pH gradient (about 4 to 11), the spectral changes could not be justified based on the presence of acid–base equilibrium species. Hence, it is necessary to bear in mind the hydrolysis mechanism that this insecticide goes through in an alkaline
4. Results and discussion 4.1. General considerations Different experimental flow injection conditions were tested (i.e., composition and concentration of the solutions used in the injection, reactor lengths, injection loops and flow rate) in order to obtain a gradual pH variation, leading to suitable pH profiles. Each of the studied samples was diluted with HAc 1.0 × 10−2 mol L−1 and used as the carrier stream. The composition of the injected sample was identical to that of the carrier, except that the dilution was carried out with NaOH 1.0 × 10−3 mol L−1. This mode of generating the pH gradient has already been employed [24,25], preventing changes in total analyte 4
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4.2. Calibration and validation samples In a first stage, EEMs–pH were recorded for calibration samples to build a four–way calibration model. Subsequently, recoveries and figures of merit were calculated using validation samples at several concentration levels in order to evaluate the predictive ability of the proposed methodology. In view of the results obtained in previous works [24,25] in which the pH gradient was generated by a slow and well synchronized flow injection method, validation samples were in principle evaluated with PARAFAC [35,36]. However, in this work this algorithm leads to poor predictions, maybe associated with loss of quadrilinearity in the data array [31]. In the system under study, the extensive spectral overlapping exhibited by the analytes both in excitation as in emission mode may preclude the decomposition of the three–way data in physically reasonable profiles, explaining the failure of trilinear models such as PARAFAC. This data can be conveniently analyzed by more flexible algorithms, such as U–PLS that can take into account quadrilinearity deviations, as suggested by Escandar and Olivieri in their review describing the road map for multiway calibration models [37]. Therefore, the data arrange was analyzed using unfolded PLS. The number of calibration PLS latent variables was determined using leave–one–out cross validation (CV) method described by Haaland and Thomas [30]. In general, the number of CV estimated latent variable should be similar to the known number of sources of variation in the calibration data, or probably larger if there are additional phenomena to be modeled. This study yielded an optimum value of four latent variables for FBZ, TBZ and NAA, consistent with a system containing four emitting constituents. However, for CBL an extra latent variable is required to explain the variance of the data. In this case, PLS probably takes into account additional factors of the signal of analytes that produce spectral variation, especially because for CBL there is a hydrolysis mechanism associated with pH, which may not be fully reproducible from sample to sample. Predicted vs. nominal concentration plot for validation samples was constructed (Fig. 3A), and a good correlation was observed for all pesticides. The elliptical joint confidence region (EJCR) statistical test was performed to prove the accuracy of the predictions. This test consists in verifying if the ideal point (slope = 1, intercept = 0) is included in the elliptical region of mutual confidence of the slope and intercept in the predicted vs. nominal concentration plot. Fig. 3B shows that the theoretically expected point lies inside the elliptical regions for all the analytes, indicating the accuracy of the proposed methodology.
Fig. 3. (A) Plots for predicted concentrations of TBZ (cyan), NAA (red), FBZ (dark blue) and CBL (dark green) as a function of the nominal values in the validation test samples; (B) the corresponding elliptical joint regions at 95% confidence level. The black point marks the theoretical (intercept = 0, slope = 1) point.
medium [32,33]. This fact can explain both the fluorescence decrease and the band observed at 470 nm consistent with naphthol, the main metabolite of CBL (Fig. 1B). In general, within pesticides, insecticides are more severely affected than herbicides and fungicides by hydrolysis mechanisms, having pH and temperature a direct impact on the kinetic reaction [34]. The stability in alkaline medium of all the pesticides under study was tested by following of fluorescence emission of the four analytes as a function of time at 20 °C. It was found that only CBL undergoes hydrolysis, immediately generating naphthol under working conditions, whereas FBZ, TBZ and NAA were stable over time in alkaline medium. Fig. 2A represents the third–order data for a solution containing the four pesticides under study, showing the evolution of EEMs with the spectral variations generated by the double pH gradient. The spectral changes observed are associated with the injection of the alkaline bolus into the carrier acid flow (both containing samples in identical composition), going from acid pH (sensor 1) to alkaline pH (sensors 15 to 20), and returning to the acid pH of the carrier sample (sensor 25). The spectral ranges were restricted in order to include enough information to perform the analysis and to avoid both Rayleigh and Raman scattering, as well as second harmonic from the diffraction grating.
4.3. Real samples The usefulness of the proposed method was tested by quantifying the studied pesticides in vegetal tissues (pear, orange, mushroom, lettuce). Even though the evaluated vegetables belong to regions of intensive agriculture, where agrochemicals are profusely used, samples did not contain pesticides at levels higher that the detection limits. Therefore, spiked samples were prepared with the analytes at different concentration levels and a recovery study was carried out in order to assert the accuracy of the developed methodology [38]. In order to minimize sample manipulation and the consumption of organic solvents, a simple extraction with pure ethyl acetate was proposed (no clean–up step was used). The same methodology was used for the different matrices. Fig. 2B shows the contour plots of EEMs for tissues samples, representing only the first pH sensor since no significant spectral changes are observed along the pH mode. As can be seen, vegetal tissue samples under study, even after pre–treatment, present an important spectral overlapping with the analytes, becoming essential the second–order advantage to separate pesticides signal from those of the signal background. In order to remove the matrix contribution, the complementary technique known as residual trilinearization (RTL) was used together with U–PLS to accurately quantify the 5
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Table 2 Recovery study of pesticides in tissue samples using U–PLS/RTL. FBZa
LETTUCE
ORANGE
PEAR
MUSHROOM
texperimental tcritical a b
TBZa
NAAa
CBLa
Added
Found
R%b
Added
Found
R%b
Added
Found
R%b
Added
Found
R%b
28.6 66.6 19.0 19.0 38.1 42.8 33.3 38.1 na 18.6 na 9.3 na na 27.9 37.2 na – 13.3 20.0 26.7 26.7 66.7 46.7 53.3 40.0 13.3 30.7 53.3 26.7 40.0 20.0 50.7 46.7 86.7 60.0 66.7 26.7
28.6 63.8 17.1 19.0 40.0 40.0 35.2 38.1 nd 19.5 nd 10.2 nd nd 30.7 35.0 nd – 13.3 18.7 22.7 26.7 61.3 48.0 50.7 40.0 13.3 29.3 54.7 25.3 40.0 18.7 48.0 48.0 88.0 56.0 68.0 29.3 1.89 2.04
100 96 90 100 105 93 106 100 – 104 – 110 – – 110 94 – – 100 93 85 100 92 102 95 100 100 96 102 95 100 93 95 103 101 93 102 110
57.1 85.7 na na 47.6 47.6 76.2 95.2 95.2 279 238 93 257 66.6 147 65 190 133 307 200 267 267 400 467 293 na 293 240 133 200 293 133 267 333 467 333 400 400
61.9 88.5 nd nd 41.9 46.6 73.3 78.1 104.7 251 238 84 238 68.5 142 75 190 147 267 200 240 227 333 467 293 nd 306 213 133 173 280 112 253 333 467 333 400 347 1.43 2.03
108 103 – – 88 98 96 82 110 90 100 90 93 102 96 115 100 110 87 100 90 85 83 100 100 – 104 89 100 87 96 84 95 100 100 100 100 87
95.2 47.6 66.6 na na na 142 95.2 47.6 372 66.6 232 142 47.6 279 325 133 400 333 533 467 640 667 533 66.7 na 160 na 400 na na 267 133 333 627 na na 200
114 47.6 63.8 nd nd nd 142 85.7 42.8 362 66.6 242 152 42.8 270 325 133 400 333 493 467 640 667 507 53.3 nd 160 nd 400 nd nd 267 146 293 640 nd nd 226 1.5 2.05
120 100 96 – – – 100 90 90 98 100 104 106 90 97 100 100 100 100 92 100 100 100 95 80 – 100 – 100 – – 100 110 88 102 – – 113
na na 238 171 124 124 476 314 190 232 428 93 285 238 186 279 238 133 400 200 800 267 400 333 533 267 80.0 na 267 na na 600 800 na 467 na na 133
nd nd 247 171 124 124 485 304 228 232 400 93 314 257 177 260 257 127 400 226 840 267 360 333 493 280 72.0 nd 267 nd nd 600 826 nd 413 nd nd 120 0.36 2.04
– – 104 100 100 100 102 97 120 100 93 100 110 108 95 94 108 95 100 113 105 100 90 100 92 105 90 – 100 – – 100 103 – 88 – – 90
Concentrations are given in μg kg−1; na: not added, nd: values below the corresponding LOD. Recovery (R%) is calculated as R% = C found/C added * 100 only when the analytes were added.
analytes. The estimation of the number of potentially interfering components or RTL components was done by considering the progression of the residual fit as the number of RTL components is increased (see Section 3). The residual fits after U–PLS/RTL analysis (7.0–8.5 units) stabilized at the order of the calibration residues (7.2–7.6 units), and considerably smaller than the residues before RTL (15.0–33.0 units), requiring only an extra latent variable to “clean” the EEMs data from the native signal of tissue samples. This indicates that U–PLS/RTL considers in all cases the matrix signal as containing a single mathematical component. Moreover, reasonable match was found between the spectral profiles retrieved by the program after RTL convergence in each mode with those corresponding to the background of the different tissue matrices (see Fig. S2, Supplementary material). Prediction of the analytes content free of the interference is possible once the PLS scores for each of the tissue samples are corrected with the RTL procedure. Table 2 shows the recoveries obtained confirming the good results achieved using the proposed strategy, with values between 93 and 106% for FBZ, 87–115% for TBZ, 80–120% for NNA, 89–113% for CBL. Recovery was calculated only when the analyte was added. Nevertheless, the non-added analytes were also predicted, obtaining values below the limits of detection corresponding to each matrix (predicted concentrations close to zero). The t-test was used to evaluate
Table 3 Statistical results using U–PLS/RTL for pesticides in validation samples and spiked tissue samples.
Validation samples Calibration range (μg L-1) SEN (μg L-1) γ (μg -1 L) LOD (μg L-1) LOQ (μg L-1) RMSEP (μg L-1)a REP %b Spiked vegetables tissuesc SEN (μg L-1) γ (μg -1 L) LOD (μg L-1) LOQ (μg L-1) RMSEP (μg L-1)a REP %b
FBZ
TBZ
NAA
CBL
0-80 46.5 5.4 0.7-5 2-13 1.3 3.4
0-400 9.5 1.2 3-20 9-40 17 8.5
0-500 7.6 1.0 4-10 10-50 14 5.6
0˗800 3.9 0.55 7-30 30-80 28 7.1
23 2.6 2-6 5-15 3.2 8.1
3.1 0.4 5-30 15-50 20 9.1
3.2 0.4 10-20 30-80 17 6.3
3.6 0.4 8-40 40-90 30 8.1
a
RMSEP: root mean square error of prediction. REP %: relative error of prediction, expressed with respect to the average concentration of the calibration samples. c The values correspond to the concentrations obtained after processing the samples (see Experimental section). b
6
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whether the obtained recoveries do not differ significantly from 100%. The calculated t values for all analytes were less than the critical value of t, suggesting that the developed method provides recoveries not different from 100, for a significance level of 0.05.
Appendix A. Supplementary data
4.4. Figures of merit
References
Figures of merit are important components of the analytical validation process, in order to establish detection capabilities and to compare the relative performances with different methodologies. Table 3 summarizes the figures of merit for the validation and real samples processed by U–PLS/RTL. Sensitivity (SEN) was calculated using equations found in the literature for third–order data [39], based on the relation of input noise in an analytical system. Analytical sensitivity (γ) was calculated from the ratio between sensitivity and instrumental noise, expressed in units on μg−1 L. The instrumental noise considered for this case was 7.2 AFU. Finally, the limits of detection (LODs) and the limits of quantitation (LOQs) were calculated in units of μg L−1 for each analyte, following a rigorous approach recommended by the International Union of Pure and Applied Chemistry (IUPAC) [40]. The values for tissue samples are in the final conditions of measurements, and they show a good precision and an appropriate sensitivity. In general, the complexity of real samples can justify the unfavourable changes in the statistical results with respect to the validation samples. Despite this, the results were very good, validating the effectiveness of RTL to separate the contribution of the components not modeled in the calibration step, restoring selectivity and rendering good predictions. Taking into account the tissue samples procedure, the resulting calculated LODs expressed in μg kg−1 are: 3–7; 6–34; 11–22; 9–45 for FBZ, TBZ, NAA and CBL, respectively. Likewise, the LOQs are calculated in the tissue samples expressed in μg kg−1: 6–16; 17–56; 34–90; 45–98 for FBZ, TBZ, NAA and CBL, respectively. According to the previous results, the proposed methodology allowed the determination of the studied compounds in fruits and vegetables with concentrations near the maximum permissible residue limits (see Table 1), for each of the analyzed matrices. It is noteworthy that sensitivity can be improved, if required, by employing a protocol that includes a higher sample amount and a small extraction volume. Moreover, it would be possible to extend the application of this methodology to other vegetable matrices.
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5. Conclusion The simultaneous determination of four pesticides in fruits and vegetables was achieved employing a four–way calibration model based on EEMs as pH function. U–PLS/RTL algorithm was decisive to achieve enough selectivity, resolving the high degree of spectral overlapping and rendering good results, even in the presence of an interfering signal from the background in real samples. Therefore, EEMs modulated by pH gradient in combination with chemometric tools, offer a new perspective for the quantification of pesticides for food control in a reasonable time, using non–sophisticated instrument, a simple sample pre–treatment, and reducing the use of environmentally dangerous organic solvents. Declaration of Competing Interest Authors declare no conflict of interest. Acknowledgments The authors gratefully acknowledge the Universidad Nacional de Rosario, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) and Agencia Nacional de Promoción Científica y Tecnológica (ANPCyT, Project PICT 2016-1122) for financially supporting this work. 7
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