Green approach for simultaneous determination of multi-pesticide residue in environmental water samples using excitation-emission matrix fluorescence and multivariate calibration

Journal Pre-proof Green approach for simultaneous determination of multi-pesticide residue in environmental water samples using excitation-emission matrix fluorescence and multivariate calibration

Yuan-Yuan Yuan, Shu-Tao Wang, Shi-Yu Liu, Qi Cheng, ZhiFang Wang, De-Ming Kong PII:

S1386-1425(19)31191-6

DOI:

https://doi.org/10.1016/j.saa.2019.117801

Reference:

SAA 117801

To appear in:

Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy

Received date:

5 September 2019

Revised date:

14 November 2019

Accepted date:

14 November 2019

Please cite this article as: Y.-Y. Yuan, S.-T. Wang, S.-Y. Liu, et al., Green approach for simultaneous determination of multi-pesticide residue in environmental water samples using excitation-emission matrix fluorescence and multivariate calibration, Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy(2019), https://doi.org/10.1016/j.saa.2019.117801

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© 2019 Published by Elsevier.

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Green approach for simultaneous determination of multi-pesticide residue in environmental water samples using excitation-emission matrix fluorescence and multivariate calibration Yuan-Yuan Yuan, Shu-Tao Wang*, Shi-Yu Liu,Qi Cheng, Zhi-Fang Wang, De-Ming Kong Measurement Technology and Instrument Key Lab of Hebei Province, Yanshan University, Qinhuangdao 066004, China * Correspondence: [email protected]

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Abstract: Pesticides are among the most widespread organic contaminants in aquatic environments. In this work, a new green fluorescence application was proposed for the simultaneous determination of four widely employed pesticides in environmental water samples. To overcome the highly overlapped spectra within the analytes, and with the tissue matrix interferences in complex solutions, we have used the multivariate calibration methods such as parallel factor analysis (PARAFAC) and unfolded partial least squares coupled to residual bilinearization (U-PLS/RBL). These four pesticides can be identified simultaneously, and the correlation coefficients between resolved and actual spectra are all above 0.95. The second-order advantage allowed the determination of four pesticides at the ng mL -1 level, even in the presence of humic acid (HA). The best results were obtained with the limits of detection of 1.72-18.69 for Carbendazim (CBZ), 0.30-5.19 for carbaryl (CAR), 0.35-6.32 for chlorothalonil (CHL), and 4.92-29.96 for tsumacide (TSU) (ng mL -1), which can fully meet the quantitative detection and analysis requirements of trace pesticides in water samples. The real water sample of Bohai Seawater was used to check the performance of this approach in practical applications, which have achieved good prediction results of U-PLS/RBL. This study demonstrated the proposed method is rapid, accurate, sensitive, low detection limit, and environmentally friendly to determinate multi-pesticide residues in environmental water samples.

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Keywords: Multi-pesticide residue; Environmental water samples; Excitation-emission matrix fluorescence; Multivariate calibration

Introduction

With the increased population density in the world, high yields of crops in existing agricultural land are required [1]. The use of pesticides can ensure the high yield of food grains. According to statistics, four million tons of pesticides are used to control pests annually worldwide, but less than 1% of the total amount of applied pesticides can reach to the target pests [2]. Most of the pesticides will remain in the environment and on crops, and then enter the nearby water bodies along with precipitation, surface runoff, and soil leaching, which will worsen the water quality [3, 4], cause adverse effects on aquatic organisms [5, 6], and even pose a threat to human health [7]. The extensive use of pesticides in agriculture has had an undisputed impact on water quality and has become a quite serious environmental pollution problem. At present, there have been many methods to determine pesticide residues in water samples, such as chromatography, which include liquid chromatography (LC) [8], gas chromatography (GC) [9], high-performance liquid chromatography (HPLC) [10], gas

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chromatography-mass spectrometry (GC-MS) [11] and liquid chromatography-mass spectrometry (LC-MS) [12]. Also, there are some other detection methods such as biosensor technology [13, 14] and spectrophotometry [15, 16]. Petrie et al. reviewed and summarized the rapid progress of determination of chiral pesticides in the environment using stereoselective LC-MS/MS methodologies [17]. Zhang et al. developed a targeted screening using GC-MS/MS for evaluating above 450 pesticides in precipitation, the predicted concentrations of these pesticides were all below 500 ng L-1, and these findings could provide a better understanding of the pesticide contamination in precipitation [18]. Chromatography has been widely used in pesticide residues detection due to its high separation efficiency, high sensitivity, and excellent selectivity. However, chromatographic qualitative ability is poor and needs to be combined with other analytical techniques, and sample preparation often requires large amounts of solvent, which generates a large amount of waste. Therefore, green analytical chemistry, which is environmentally friendly, without separations and cleaning steps, to reduce the use of chemical organic solvents, and to prevent waste, are very welcome [19]. Considering most pesticides are organic aromatic compounds and have molecular structures that can generate fluorescence, fluorescence spectroscopy has a greater appeal in pesticide residue analysis [20]. More recently, three-dimensional excitation-emission matrix (EEMs) fluorescence has been considered to be an effective means for studying pesticide residues in the environment due to its high sensitivity, simplicity, low cost, and without pre-separation of samples. However, for mixtures with severe spectral overlaps and interferents, analysis of the interest cannot be achieved just by fluorescence spectroscopy, which dramatically reduces the selectivity of fluorescence spectroscopy. In order to obtain maximum spectral information and improve the selectivity of fluorescence spectroscopy, EEMs fluorescence combined with multivariate calibration methods [21] has become a research hotspot. It is using "mathematical separation" rather than "physical-chemical separation" to realize the analysis of interest components in complex solutions, which include uncalibrated background or interference. Owing to the unique second-order advantages, these multivariate calibration methods can quantitative analysis of analytes even with highly overlapped spectra and unknown interference [22]. In analytical chemistry, chemometrics is the use of mathematical and statistical methods to obtain relevant information about the substance in an optimal manner. The chemometric methods have been successfully applied in the fields of chemistry and food science [23]. Recently, the combined techniques of EEMs fluorescence with the multivariate calibration, for instance, PARAFAC [24] and alternating trilinear decomposition algorithm (ATLD) [25] of trilinear decomposition (TLD) models, unfolded partial least-squares (U-PLS) and multi-dimensional partial least-squares (N-PLS) of residual bilinearization (RBL) models [26, 27], etc. have been successfully applied to determine organic pollutants in water samples. Ferretto et al. proposed PARAFAC algorithm combined with EEM fluorescence to discriminate and quantify nine polycyclic aromatic hydrocarbons (PAHs) and three pesticides in the aquatic environment at the μg L−1 level [28]. Maggio et al. showed a research paper using multi-way calibration methods to determine carbaryl and 1-naphthol in water and with the concentration ranges 0–363 μg L−1 and 0–512 μg L−1, respectively [29]. Fuentes et al. presented the

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second-order multivariate calibration combined with photochemically induced fluorescence to determine imidacloprid in water samples, average recovery was 101±10% and the LOD was about 1 ng mL-1 [30]. To the best of our knowledge, there is no paper report on simultaneous determination of Carbendazim (CBZ), Carbaryl (CAR), Chlorothalonil (CHL), and Tsumacide (TSU) (shown in Fig. 1) in environmental water samples. In particular, the limits for the CAR and CHL pesticides are specified in the National Environmental Quality Standards for Surface Water (GB3838-2002) of China. Hence, we developed a quantitative analysis of multi-pesticide residues in environmental water samples through EEM fluorescence spectra, and data were analyzed using several multivariate calibration algorithms.

Fig. 1. Structure of the studied pesticides

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2. Theory: Figures of merit

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Figures of merits are used to evaluate the prediction results and provide guidance in choosing a more appropriate method in our research. In addition, searching for new methods to optimize parameters in figures of merit is the imperative power to impact current analytical chemistry. Olivieri et al. reviewed different approached for computing the analytical figures of merits from univariate to multi-way calibration [31]. The root mean square error of prediction (RMSEP) and relative error of prediction (REP) are computed by 2 1 N cact  c pred  ,   n 1 N 1 100  RMSEP REP= , cave

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RMSEP 

(1) (2)

where N is the number of predicted samples, cact and c pred are the actual and predicted concentration, respectively, cave is the average concentration of an analyte in predicted samples. The International Union of Pure and Applied Chemistry (IUPAC) has set the definitions of sensitivity (SEN) in numerous calibration scenarios [32]. In this work, it is defined as the instrument response change divided by the corresponding stimulus change, which are the concentrations of analytes. And can be calculated by the following expression:





SENn  sn /   Aex T (I  Aun Aun  ) Aex   Bex T  I  Bun Bun   Bex 



1

 ,  nn

(3)

where sn is a signal indicative of a target analyte within a unit concentration; A and B are the normalized excitation and emission matrix of component of interest by multivariate calibration methods; The subscript “ex” and “un” represent the expected and unexpected components, that is, the component of interest and the component of interference;  is the Hadamard product; I is the unit matrix.

Journal Pre-proof According to a novel IUPAC estimator, the limit of detection (LOD) is expressed in the form of interval detection (LODmin–LODmax) correspond to PLS calibration [33]. It can be regarded that analyte is not detected in a given sample if its predicted value is below LODmin, or that it is present if its predicted concentration is above LODmax. LODmin and LODmax are estimated by: 1/ 2

LODmin  3.3  SEN2 x 2  h0min SEN2 x 2  h0min var  ycal 2 

,

(4)

LODmax  3.3  SEN2 x 2  h0 max SEN2 x 2  h0max var  ycal 2  ,

(5)

1/ 2

where h0 min and h0 max are the minimum and maximum value for sample leverage suggested by Allegrini et al [33].  x represents the standard deviation of predicted concentrations,  ycal represents the standard deviation calibration concentrations.

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The limit of quantitation (LOQmin–LOQmax) is easily set at a concentration value which is 10 times the associated prediction uncertainty, can be defined as follows: 1/ 2

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LOQmin  10  SEN2 x 2  h0 min SEN2 x 2  h0min var  ycal 2  , 1/ 2

(7)

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LOQmax  10  SEN2 x 2  h0 max SEN2 x 2  h0max var  ycal 2  ,

(6)

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3. Experiment 3.1. Instrument and software

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EEMs fluorescence spectra measurements were obtained with an Edinburgh FS920 steady-state fluorescence spectrometer, which possessed a Xe900 450W Xenon arc lamp, Single-photon counting, Czerny-turner monochromator, and F900 advanced software. The EEMs fluorescence data were recorded in the excitation wavelength range of 250 380 nm every 5 nm, and the emission wavelength range of 270 - 500 nm every 2 nm. The slit widths of excitation and emission monochromators were set to 10 nm, and the scan rate was set as 1500 nm min-1. The sample chamber temperature was maintained at 20℃ using a TC125 temperature control (Quantum Northwest, Inc. Spokane, WA). The spectra data were saved as ASCII format using F900 system software for subsequent manipulation. All data were handled using MATLAB R2014a, the multivariate calibration methods using graphical interface MVC2 (Multivariate Calibration for second-order) [34], freely available on the Internet [35]. 3.2. Chemicals and reagents

CBZ, CAR, CHL, TSU, HA and methanol were of high analytical standard and obtained from Aladdin Biological Technology Co., Ltd., (Shanghai, China, https://www.aladdin-e.com). Using FA1004 precision electronic scale (actual scale value: 0.1mg) manufactured from Tianma Scale Instrument Co., Ltd., (Tianjin, China) to weigh 0.01g CBZ, CAR, CHL, and TSU, respectively. Then dissolved individually in methanol with 10 mL brown volumetric flasks, and shocked with Hy-5a cyclotron oscillator (Changzhou, China) for 5 min to be completely dissolved. Then we obtained primary stock solutions with a concentration of 1 mg mL-1. Take 0.1mL from the primary standard solution individually by micro-adjustable pipette, add to 100 mL brown volumetric flasks with ultra-pure water. Then secondary standards solutions of CBZ, CAR, CHL, and TSU at 1 µg mL-1 were obtained individually. The HA was dissolved in ultrapure water, and a

Journal Pre-proof 10 µg mL-1 HA solution was obtained. All prepared standard stock solutions were stored in sealed brown volumetric flasks at 4 oC in the dark. 3.3. Calibration, validation and test samples

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A calibration set of 8 pesticide mixtures samples (C1-C8) were prepared from the secondary standards solutions. The concentration designs of pesticide mixtures in calibration samples (C1-C8) are shown in Table 1. The final concentrations of each analyte in these mixtures ranged from 0-90 ng mL-1 (CBZ), 0-20 ng mL-1 (CAR), 0-54 ng mL-1 (CHL), 0-180 ng mL-1 (TSU) in ultra-pure water. Validation samples (V1-V5) were made with the concentrations ratio different than those used in calibration set, and following a random concentration design within the calibration sample ranges. To evaluate the predictive effect of the proposed approach in presence of the interfering pollutants HA, which is widely found in natural waters and has fluorescence signals overlapped with those pesticides, 5 test samples (T1-T5) were prepared to contain these four pesticides and HA interference. The details of concentration designs of pesticide mixtures in validation and test samples are listed in Table 1.

Concentrations (ng mL-1) CBZ

CAR

TSU

0 0 30 0 42 6 54 18

0 0 0 100 180 140 60 20

-

12 6 10 16 8

32 30 24 12 36

160 140 120 80 60

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14 4 8 12 16

18 9 12 24 36

70 80 120 180 100

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50 0 0 0 10 30 70 90

HA(µg mL-1)

CHL

Calibration samples C1 C2 C3 C4 C5 C6 C7 C8

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Table 1. The concentration designs of pesticide mixtures in calibration samples (C1-C8), validation samples (V1-V5) and test samples (T1-T5)

Validation samples V1 V2 V3 V4 V5

20 30 50 60 80

Test samples T1 T2 T3 T4 T5

50 90 70 15 20

3.4. Sample processing of real water sample

CBZ, CAR, CHL, and TSU were simultaneously analyzed in a real water sample of Bohai Seawater (Qinhuangdao, China). To filter out some impurities, Bohai Seawater was filtered twice with an M2637 microporous membrane of 0.45 μm diameter. To avoid the fluorescence inner filter effect, Bohai Seawater was diluted 1:10 with ultra-pure water. These four pesticide concentrations were designed from the corresponding calibration ranges using the diluted Bohai Seawater.

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Scattering often interferes the fluorescence spectra of the analytes being measured. Also, it is the main limiting factor for improving the sensitivity of fluorescence analysis. The Rayleigh and Raman scatterings are the main scattered light in fluorescence spectrum measurement, which seriously interferes the determination of these pesticides [36]. The three-dimensional fluorescence spectroscopy of sample V3 obtained by the instrument is shown in Fig. 2a. It can be seen that the spectral data directly obtained by the instrument has severe Raman and Rayleigh scattering. These scattering effects do not contain any chemical information, and can interfere with the mathematical model of the interest compounds. Hence, it is better to eliminate or reduce scattering as much as possible before applying the algorithms. First, Raman scatter was eliminated by subtracting a blank solvent from the raw data. The result is shown in Fig. 2b. It can be seen that Raman scattering is subtracted, but there is still severe Rayleigh scattering, which will affect the qualitative and quantitative determination of analytes. Even if we have set emission wavelength lag excitation wavelength 20 nm, the first-order Rayleigh scattering seriously affects the spectrum of CBZ and makes it incompletely displayed. In this work, Rayleigh scattering was handled by the new Delaunay triangulation method [37], which applying three-dimensional interpolation of the remaining data to remove the scatter peaks and replace the excised values. The width parameters of first-order and second-order Rayleigh scattering were set to [15,15] and [18,18]. The spectrum after removal of the scattering is shown in Fig. 2c. Comparing Fig. 2a and Fig. 2c, it was found that the method can better eliminate the scattering, and the true spectra of these pesticides were displayed.

Fig. 2. (a) Original spectrum and (b) Spectrum after Raman scatter removal (c) Spectrum after Rayleigh scatters removal of sample V5.

4. Results and Discussion

4.1. Fluorescence signatures of the individual pesticides

In order to determine the analyte of interest in a complex system, the emission and excitation loads estimated by the algorithm model should be contrasted with the actual emission and excitation spectra. Therefore, it is a prerequisite to analyze the actual spectrum of the analyte that needs to be identified. Fig. 3a-d show EEMs landscapes corresponding to pure samples of CBZ, CAR, CHL, and TSU at 50, 10, 30, 80 ng mL-1, respectively. Using the data preprocessing mentioned in section 3.5, the broad feature-free bands of Rayleigh and Raman scattering were eliminated. As can be seen from Fig. 3, the CBZ shows a fluorescence peak in the wavelength range ex / em = 265-315 nm / 280-350 nm (Fig. 3a); The CAR exhibits two fluorescence peaks next to each other in the wavelength range

ex / em = 260-320 nm /

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ex / em =

300-350 nm / 325-400 nm and a lower intensity fluorescence peak with the wavelength range ex / em = 255-290 nm / 325-400 nm are shown in CHL (Fig. 3b). The TSU exhibits a fluorescence peak in the wavelength range

ex / em = 260-280 nm / 270-330 nm. As

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mentioned above, the EEMs of these pesticides show a significant overlap in the wavelength range measured, especially the range from 260-300 nm for excitation and 270-350 nm for emission. Therefore, to achieve the determination of these four pesticide residues in a complex system simultaneously, multivariate calibration algorithms are required.

Fig. 3. EEM landscapes for each pure analyte: (a) 50 ng mL-1 of CBZ, (b) 10 ng mL-1 CAR, (c) 30 ng mL-1 of CHL, and (d) 80 ng mL-1 of TSU.

4.2. Quantitative analysis

The fluorescence spectra for calibration, validation, and test samples were obtained upon FS920 Fluorescence Spectrometer. As described in 4.1, there is highly overlapped among these four pesticides spectra, which prevents their direct measurement by fluorescence in a hybrid system. The selectivity situation becomes more severe if another fluorescent pollutant (HA) (shown in Fig. 4.b1-b2) is also present. Especially the presence of HA causes the multi-pesticide residues fluorescence spectrum to be masked. Therefore, to overcome this conundrum, avoid separation steps, second-order calibrations such as PARAFAC and U-PLS/RBL, which can achieve the second-order advantage, were used. This advantage indicates that quantitative analysis can be performed in the presence of spectral overlap or interference components in samples, avoiding any interference cancellation requirements.

Journal Pre-proof 4.2.1. Calibration and validation samples

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A set of EEMs X (27 × 116 ×13), which containing calibration and validation samples, can be arranged as a three-way tensor. The first dimension of this tensor is the number of excitation wavelengths, the second one refers to the number of emission wavelengths, and the third one is the number of samples. The PARAFAC method (which employs SVD vectors) has been applied to this tensor. Due to both the resolved excitation and emission spectra must be positive, non-negative constraints on PARAFAC. Usually, the number of components should be estimated before multivariate calibration. The core consistency diagnostic (CORCONDIA) was used to select the number of spectral components in this three-way tensor. As a rule of thumb, a core consistency beyond 90% can be inferred as "very trilinear", while a core consistency around 50% would imply a defective model that deviates from the trilinearity [38]. As for the validation set, a four-factor PARAFAC model was chosen (the core consistency ranged from 73% to 100%). The evaluated number of components was 4, which can also be justified by the presence of four pesticides in validation samples. In Fig. 4a1-a2, through the PARAFAC decomposition of this tensor, the resolved excitation and emission spectra (solid lines) are substantially coincident with actual ones of pure pesticides (dotted lines). Even four pesticides have severe fluorescence spectral overlap, the correlation coefficients between resolved excitation, emission spectra and actual ones are 0.989 and 0.953 for CBZ, 0.995 and 0.996 for CAR, 0.984 and 0.996 for CHL, and 0.992 and 0.997 for TSU. Using the same data analysis process as the PARAFAC method, validation samples were studied by U-PLS algorithm, which is detailed depicted in the literature [39]. In the U-PLS method, the original three-way tensor is expanded into vectors before PLS application, as has been demonstrated by Wold et al. [40]. When using U-PLS, an efficient process for establishing the number of latent variables is cross-validation, according to the Haaland and Thomas criterion [41]. The optimal number of latent variables was

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* calculated using the formula F ( A)  PRESS ( A  A ) , where PRESS represents the

PRESS  1 ( yno min al  y predicted ) I

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squared error of the prediction, and expressed as ; A * A corresponds to a trial number of factors; represents the minimum value of PRESS . Select optimum number of latent variables by judging whether the probability p is less than 75% and F  1 . Unlike the PARAFAC algorithm, there is no physical interpretation of these latent variables. Furthermore, when unexpected interference is present in test samples, the RBL procedure should be involved to achieve the second-order advantage. Moreover, for an in-depth understanding of the prediction accuracy of the proposed algorithms, the prediction results of four pesticides are represented with the elliptical join confidence region (EJCR) (at 95% confidence level), which calculates the joint confidence interval for the intercept and slope of nominal versus found concentrations. Generally, the prediction results for this system can be judged by whether the ideal point (0,1) is within the ellipse and the size of the ellipse. EJCR plots have been presented in Fig. 5, resulted from PARAFAC and U-PLS/RBL, respectively. As can be seen, all figures include the ideal point (0,1), which shows a good prediction of PARAFAC and U-PLS methods. Moreover, for CHL (Fig. 5c), there were no significant differences between PARAFAC and U-PLS; and for CBZ, CAR, TSU (Fig. 5a, 5b, 5d), the ellipse size obtained by U-PLS showed the smallest size. This indicates that U-PLS predicts concentration better than PARAFAC. The evaluation parameters, such as RMSEP, REP, SEN, LOD and LOQ, for determination of CBZ, CAR, CHL and TSU in validation samples were calculated, shown in Table 3. The RMSEP, which is a parameter to express the root mean

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square error of predicted concentrations and actual ones, can reach 3.40, 0.83, 1.11 and 11.02 ng mL-1, respectively. The REPs can reach 7.08, 7.85, 4.13 and 9.67%, respectively. The LODs, which is a crucial parameter to evaluate the performances of this method, are 1.39-18.45, 0.25-5.15, 0.29-6.27 and 4.01-27.24 ng mL-1, which can fully meet the quantitative detection and analysis requirements of trace pesticides in surface water in China.

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Fig. 4. Normalized actual and resolved spectra by PARAFAC: (a1) emission mode, (a2) excitation mode of validation samples and (b1) emission mode, (b2) excitation mode of test samples.

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Fig. 5. EJCR (at the 95% confidence level) for (a) CBZ, (b) CAR, (c) CHL and (d) TSU in validation samples by PARAFAC (blue) and U-PLS (orange). The ideal points are represented by intersections (intercept =0, slope =1).

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4.2.2. Test samples

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HA is widely found in natural waters and accounts for a large proportion. The potential interferent HA displays fluorescence, which overlapping the fluorescence spectra of target analytes (Fig. 4b1, 4b2). Therefore, to simulate genuine water, test samples containing these four pesticides and HA were analyzed. Fig. 4b1-b2 illustrates the resolved emission and excitation spectra decomposed by PARAFAC for test samples. As can be seen, the algorithm can achieve the determination of the components of interest even if there is unknown interference. The correlation coefficients between resolved excitation, emission spectra and actual ones are 0.980 and 0.957 for CBZ, 0.993 and 0.985 for CAR, 0.983 and 0.988 for CHL, and 0.992 and 0.983 for TSU. The test samples data were analyzed with the U-PLS algorithm. In order to attain second-order advantage, the RBL based on a Tucker3 decomposition, which can predict the concentration of interest in the unknown samples, was used. As shown in Fig. 6a, the predicted concentration for all pesticides coincide quite well with the nominal values, and the resulting EJCR also indicates the accurate and precise of these approaches (Fig. 6b). The significant differences in the size of ellipses indicated that U-PLS/RBL performs better than PARAFAC for test samples. The predicted results and analytical figures of merit are listed in Table 2. For CBZ, CAR, CHL and TSU, the RMSEP can reach 5.35, 1.23, 3.77 and 10.28 ng mL-1. The LOD of these pesticide residues can reach 1.72-18.69, 0.30-5.19, 0.35-6.32 and 4.92-29.96 ng mL-1, respectively. The results are encouraging, this green approach can achieve simultaneous determination of four pesticides simply and precisely in complex matrices, which spectra highly overlapped and with interference.

Journal Pre-proof Fig. 6. (a) Predicted concentrations for CBZ (orange), CAR (green), CHL (purple) and TSU (blue) by U-PLS/RBL in test samples, as a function of the nominal concentrations (the solid line). (b) EJCR (at 95% confidence level) for the slope and intercept of the predicted results by PARAFAC (solid line) and U-PLS/RBL (dotted line) in test samples. The ideal point is represented by intersection (intercept =0, slope =1). Table 2. Figures of merit for CBZ, CAR, CHL and TSU in validation, test and Bohai Seawater samples using U-PLS/RBL.

CAR

CHL

TSU

Validation samples RMSEP REP SEN LOD (min-max) LOQ (min-max)

3.40 7.08 548 1.39-18.45 4.1-55.37

0.83 7.85 2980 0.25-5.15 0.77-15.46

1.11 4.13 2640 0.29-6.27 0.87-18.81

11.02 9.67 189 4.01-27.24 12.02-81.73

Test samples RMSEP REP% SEN LOD (min-max) LOQ (min-max)

5.35 10.92 530 1.72-18.69 5.15-56.08

1.23 11.44 2900 0.30-5.19 0.90-15.48

Bohai Seawater (Qinhuangdao,China) RMSEP REP% SEN LOD (min-max) LOQ (min-max)

9.18 15.97 487 2.43-18.67 7.31-56.03

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1.87 19.64 2480 0.46-5.19 1.38-15.56

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CBZ

3.77 19.06 2500 0.35-6.32 1.06-18.98

10.28 9.34 178 4.92-29.96 14.78-89.90

4.59 15.96 1740 1.37-6.02 4.11-18.06

10.25 9.54 164 6.85-28.35 20.55-85.06

RMSEP, LOD and LOQ are given in ng mL-1. SEN is given in ng-1 mL. REP is given in %.

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4.3. Real water samples

Water samples collected from Bohai, northeast of Hebei province, China, were analyzed. Bohai Seawater was preprocessed, as described in section 3.4. Fig. 7 shows the EEM contour plots of Bohai Seawater after scattering removal. As can be seen in this figure, it has visible fluorescence peaks within the measured excitation-emission range. The excitation/emission wavelength range of H: 320-350/400-500 nm is humic-like fluorescence, which is the organic matter decomposed by microorganisms to form a colloidal substance. The excitation/emission wavelength range of P: 260-300/320-370 nm is protein-like fluorescence, which connected with phytoplankton and aquatic [42]. As observed, the Bohai Seawater has considerable fluorescence signals in the same area as these pesticides display. Despite this overlap, the U-PLS/RBL approach can overcome this drawback. The predicted concentrations and recovery (%) of the pesticide mixtures in Bohai Seawater using U-PLS/RBL are shown in Table. 3. We can see that satisfactory results were obtained with the average recovery ± standard deviation 102.4 ± 15.1 %, 107.9 ± 23.8 %, 96.7 ± 15.6 % and 102.6 ± 11.2 % for CBZ, CAR, CHL and TSU, respectively. On the other hand, as for CAR, only U-PLS/RBL achieved acceptable results and PARAFAC showed completely undesirable results according to prediction values. There may be more than one cause for these phenomena, such as severe spectral overlap between CAR and protein-like in Bohai Seawater, and probability of data deviating from

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a trilinear condition in the presence of seawater matrix. Figures of merit of pesticide residues in Bohai Seawater by U-PLS/RBL are shown in Table. 2. As for CBZ, CAR, CHL and TSU, the RMSEP can reach 9.18, 1.87, 4.59 and 10.25 ng mL-1, the LOD of these pesticide residues can reach 2.43-18.67, 0.46-5.19, 1.37-6.02 and 6.85-28.35 ng mL-1. For the lowest legislative limit of pesticide residues in surface waters, various countries have established relevant standards. The National Environmental Quality Standards for Surface Water (GB3838-2002) of China [43] stipulates that the standard limits of CAR and CHL are 0.05 and 0.01 μg mL-1, respectively. Although there are no clear regulations for CBZ and TSU, the detection limits of different pesticides based on the proposed approach are low, which can fully meet the quantitative detection and analysis requirements of trace pesticides in surface water in China. Even though the four pesticides studied in this paper are not included in priority pollutants listed in the European Union (EU) water framework directive, while the maximum allowable concentration of pesticide residues in the surface water is at μg L-1 level [44]. The proposed method can meet the detection requirements of partial pesticides in surface water specified by the EU.

Fig. 7. Contour plot of the Bohai Seawater after preprocessing

Sample

S1 S2 S3 S4 AR±SD%b

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Table 3. The predicted concentrations and recovery (%) of the pesticide mixtures in Bohai Seawater using U-PLS/RBL a

CBZ

CAR

CHL

TSU

Added

Predicted [R%]

Added

Predicted [R%]

Added

Predicted [R%]

Added

Predicted [R%]

30 50 80 70

33.7 [112.3] 58.6 [117.2] 67.5 [84.4] 67.0 [95.7] 102.4 ± 15.1

12 14 4 8

10.3 [85.8] 12.5 [89.3] 5.3 [132.5] 9.9 [123.8] 107.9 ± 23.8

15 24 28 48

18.0 [120.0] 21.8 [90.8] 24.9 [88.9] 41.7 [86.9] 96.7 ± 15.6

70 60 140 160

82.8 [118.3] 61.8 [103.0] 134.0 [95.7] 149.4 [93.4] 102.6 ± 11.2

a b

Concentrations are given in ng mL-1; Recovery(R%) is calculated as R= Predicted / Added *100; AR ± SD% represents the Average recovery ± standard deviation.

5. Conclusions

The focus of this paper is to report a novel application based on EEMs fluorescence combined with PARAFAC and U-PLS/RBL to determine four pesticide residues (CBZ, CAR, CHL, and TSU) in environmental water samples. In complex water systems, "mathematical separation" is used instead of "physical-chemical separation" to achieve prediction of the interest component. The new Delaunay triangulation method was used

Journal Pre-proof

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to eliminate Raman and Rayleigh scattering. Even if the spectral highly overlap and interference exists, the correlation between the resolved spectra and actual ones can reach above 0.95 in validation and test set samples. Among these two algorithms, the superlative results were obtained with U-PLS/RBL, which offered excellent results for the quantification of multi-pesticide residues in water samples. Furthermore, we tested these pesticide residues in Bohai seawater, even though a high fluorescence overlapped with the humic-like and protein-like interferences. The RMSEP, REP, SEN, LOD, and LOQ were evaluated to examine the prediction results of the proposed analytical method. Based on the obtained results, EEMs fluorescence in conjunction with multivariate calibration, has been proven to be sufficient for routine analysis of trace pesticide residues in environmental water samples at the ng mL-1 level. In conclusion, the proposed approach is accurate, sensitive, involves lower detection limit, low experimental time and environmentally friendly. Furthermore, it fully meets the quantitative detection and analysis requirements of trace pesticides in surface water in China.

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Funding: This research was funded by the National Natural Science Foundation of China (Nos. 61771419) and Natural Science Foundation of Hebei Province of China (Nos. F2017203220).

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Acknowledgments: We thank Yanshan University (Hebei, China) for providing fluorescence spectrometer instruments and Aladdin Biological Technology Co., Ltd., (Shanghai, China) for providing experimental material.

References

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Conflicts of Interest: The authors declare no conflict of interest.

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Author Contributions Section: Yuan-Yuan Yuan: Conceptualization, Methodology, Software, Writing - Original Draft Shu-Tao Wang: Validation, Data Curation, Resources Shi-Yu Liu: Data Curation, Writing Review & Editing Qi Cheng: Formal analysis, Writing - Review & Editing Zhi-Fang Wang:

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Investigation De-Ming Kong: Supervision

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√ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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may be considered as potential competing interests:

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☐The authors declare the following financial interests/personal relationships which

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Graphical abstract

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Highlights 1.

Green approach is proposed for determination of multi-pesticide residue in environmental water samples.

2.

EEM fluorescence and chemometrics (PARAFAC and U-PLS/RBL) were applied.

3.

Highly overlapped EEM fluorescence spectra were resolved via “mathematical separation”. U-PLS/RBL allows obtaining the best results.

5.

Figures of Merits of proposed method are estimated in real water samples.

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4.