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Colorimetric Sensor Array for Detection and Identification of Organophosphorus and Carbamate Pesticides Sihua Qian, and Hengwei Lin Anal. Chem., Just Accepted Manuscript • Publication Date (Web): 27 Apr 2015 Downloaded from http://pubs.acs.org on April 27, 2015
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Analytical Chemistry
Colorimetric Sensor Array for Detection and Identification of Organophosphorus and Carbamate Pesticides Sihua Qian and Hengwei Lin*
Ningbo Institute of Materials Technology and Engineering (NIMTE), Chinese Academy of Sciences, Ningbo 315201, P. R. China
*E-mail: [email protected]; Tel: +86 574 86685130; Fax: +86 574 86685163
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ABSTRACT Due to relatively low persistence and high effectiveness for insect and pest eradication, organophosphates (OPs) and carbamates are the two major classes of pesticides that broadly used in agriculture. Hence, the sensitive and selective detection of OPs and carbamates is highly significant. In this current study, a colorimetric sensor array composing five inexpensive and commercially available thiocholine and H2O2 sensitive indicators for the simultaneous detection and identification of OPs and carbamates is developed. The sensing mechanism of this array is based on the irreversible inhibition capability of OPs and carbamates to the activity of acetylcholinesterase (AChE), preventing production of thiocholine and H2O2 from S-acetylthiocholine and acetylcholine and thus resulting in decreased or no color reactions to thiocholine and H2O2 sensitive indicators. Through recognition patterns and standard statistical methods (i.e. hierarchical clustering analysis and principal component analysis), the as-developed array demonstrates not only discrimination of OPs and carbamates from other kinds of pesticides, but more interestingly, identification of them exactly from each other. Moreover, this array is experimentally confirmed to be high selectivity and sensitivity, good anti-interference capability, and potential applications in real samples for OPs and carbamates.
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INTRODUCTION A myriad of pesticides, including diverse classes of organophosphates (OPs), carbamates, triazines, chloroacetanilides and pyrethroids, are used to protect plants from insects and pests infestation in agriculture across the world. The widespread and long-term usage of pesticides, however, caused serious contamination of air, water, soil and agricultural products, eventually being harmfulness to the ecosystems including humans.1-5 Due to the relatively low persistence under natural conditions and high effectiveness for insect and pest eradication, OPs and carbamates are the two major classes of pesticides that have been the most routinely used. Nevertheless, they also exhibit acute toxicity on human health through their residues in agricultural products and contamination of water.6,7 The high toxicity of OPs and carbamates is ascribed to their capability to irreversibly inhibit the activity of acetylcholinesterase (AChE) in the central and peripheral nervous system, resulting in accumulation of the neurotransmitter acetylcholine (ACh) in the body and thus inflicting serious harm to the human nervous system, respiratory tract, and cardiovascular system, which can lead to organ failure and eventual death. Therefore, the selective detection and quantitative determination of OPs and carbamates are highly desirable regarding to the concerns of the public safety and environmental protection. Many techniques, such as high-performance liquid chromatography (HPLC),8,9 gas chromatography-mass
spectrometry
(GC-MS),10
electrochemical
analysis,11,12
immunochips13,14 and enzyme-linked immunosorbent assays (ELISAs)15,16 have been developed for effective detection of pesticides in food and water in the past decades. Although these methods show high selectivity and adequate sensitivity and allow discrimination of pesticides, they are costly and/or rely upon sophisticated instruments and skilled manpower, making these approaches impractical for regular environments and food safety monitoring. To overcome these drawbacks, some novel methods, e.g. nanoparticle (NP) and AChE inhibition based colorimetric approaches, have recently emerged.17-23 However, the NP-based assays frequently encounter challenges of uncontrolled aggregation of NPs in real samples, thus resulting in a poor specificity. In 3 ACS Paragon Plus Environment
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addition, by taking advantages of the inhibition nature of OPs and carbamates to the activity of AChE, they can be recognized indirectly through detection of thiocholine18,19 or H2O2 (Ref. 20,21). Thanks to highly specific enzyme reactions, these AChE inhibition based methods usually show excellent selectivity and sensitivity to OPs and carbamates, but studies of discrimination between these pesticides have not yet reported. Considering practical requirements, it is of great importance to develop a simple, sensitive and cost-efficient method that can be not only for detection, but also for identification of pesticides. Fortunately, the recently established sensor-array technologies and pattern-recognition methods make such a task possible. Array sensing techniques have emerged as a potentially powerful approach toward the detection and recognition of chemically diverse analytes.24-27 Based on cross-responsive sensing elements rather than specific receptors, sensor arrays produce composite responses unique to an analyte in a fashion similar to the mammalian olfactory system.28-31 Although some array-based approaches have been developed for the detection and identification of poisonous organophosphorus chemicals, such as pesticides and herbicides,32,33 nerve agents34-37 and phosphorus-containing toxic gases,38-40 all of these arrays crucially rely on either costly or tediously synthesized molecular probes, and thus significantly limit their practical applicability. Hence, the further studies and developments of simple array based methods for simultaneous detection and discrimination of OPs and carbamates are still desirable. Based on the facts of inhibition of AChE activity by OPs and carbamates, herein, five inexpensive and commercially available H2O2 or thiocholine sensitive probes are taken to fabricate a colorimetric sensor array. This array is confirmed not only to excellently discriminate OPs and carbamates from other kinds of pesticides, but identify them exactly from each other. To the best of knowledge, this is the first example to achieve simultaneous detection and identification of a variety of OPs and carbamates through a simple array sensing technique. Finally, the potential applications of this array for the determination of OPs and carbamates in real samples, such as apple juice and green tea drinks, are observed as well. 4 ACS Paragon Plus Environment
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EXPERIMENTAL SECTION Reagents and materials All
chemicals
from
commercial
sources
are
of
analytical
grade.
Acetylcholinesterase (AChE, from Electrophorus electricus) and choline oxidase (ChOx, from Alcaligenes sp.) were obtained from Sigma-Aldrich. Acetylcholine iodide (ACh) and S-acetylthiocholine chloride (S-ACh) were purchased from Aladdin and Alfa Aesar, respectively. Thiocholine or H2O2 sensitive indicators: 5,5'-dithio bis-(2-nitrobenzoic acid) (DTNB), sodium 2,6-dichloroindophenolate hydrate (DCIP), N,N'-diphenyl1,4-diphenyldiamine (DPDA) and 3,3,5,5-tetramethylbenzidine (TMB) were obtained from Aladdin; 2,2'-azinobis-(3-ethylbenzthiazoline-6-sulphonate) (ABTS) was from Sigma-Aldrich.
Pesticides:
chlorpyrifos,
triazophos,
phoxim,
methamidophos,
dimethoate, carbaryl, methomyl, metolcarb, isoprocarb, pretilachlor and deltamethrin were purchased from Aladdin; fenobucarb, fenvalerate, DDT and α-BHC were provided by J&K Scientific; 2-methyl-4-chloro-phenoxyacetic acid (MCPA) sodium salt monohydrate was from Sigma-Aldrich. Glucose, sucrose and vitamin C were obtained from Sinopharm Chemical Reagent Co. Ltd, and lysine was provided by Aladdin. All reagents were used as received without further purification unless otherwise specified. Deionized water was used throughout this study. Stock solutions of ACh (10 mM), S-ACh (5.0 mM), AChE (10 unit/mL) and ChOx (10 unit/mL) were freshly prepared in PBS (10 mM, pH 7.4). The stock solutions of methamidophos, dimethoate and MCPA sodium salt monohydrate were directly dissolved in deionized water (0.1 g/L), and chlorpyrifos, triazophos, phoxim, carbaryl, methomyl, metolcarb, isoprocarb, pretilachlor, deltamethrin, fenobucarb, fenvalerate, DDT and α-BHC were initially dissolved in ethanol (0.1 g/L) and diluted at least 100-fold by PBS (10 mM, pH 7.4) before use. Note that all these stock solutions were freshly prepared before the experiments to minimize their potential effects of hydrolysis. Instrumentation For all sensing experiments, imaging of the arrays was performed with a flatbed 5 ACS Paragon Plus Environment
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scanner (Epson Perfection V300). The pH measurements were performed using a PHS-3C pH meter. 96-well plates (Corning 3632) were purchased from Genetimes Technology Incorporated.
Experimental procedure for the detection of pesticides Control solutions for the as-fabricated array: AChE (2×10-2 or 2×10-3 unit/mL) and S-ACh (1.0 or 0.4 mM) were incubated in PBS (10 mM, pH 7.4) at 37 ºC for 10 min (the same as below) and then added into DCIP (or DTNB, both in 10 µM) in PBS (10 mM, pH 7.4); AChE (2×10-3 unit/mL) and ACh (0.5 mM) were firstly incubated for 10 min and then ChOx (0.4 unit/mL) was added to incubate for another 10 min and finally added into DPDA (10 µM, and presence of 10 µM Fe2+ at pH 1.0) or TMB (40 µM, and presence of 40 µM Fe2+ at pH 3.0) in PBS (20 mM); 2×10-2 unit/mL of AChE replaced the just above procedure to prepare the enzyme reaction solution and added into ABTS (40 µM, and presence of 40 µM Fe2+ at pH 1.0) in PBS (20 mM). The composition of the developed array is summarized in Table S1. Work solutions were prepared in the same procedures as that of for control solutions, just by adding a desired amount of a certain pesticide during the preparation of the enzyme reaction solutions before adding into the indicator solutions. Then, 300 µL of the control and work solutions were loaded into a 96-well plate respectively, and the “before” (from the control solutions) and “after” (from the work solutions) images were acquired on an Epson Perfection V300 Photo flatbed scanner. All the analyses of pesticides samples were conducted in quadruplicate (RSDs are shown in Table S2). For each trial, a color change profile was obtained by subtracting the “before” image from the “after” image using the commercial Photoshop software. Difference maps were acquired by taking the difference of the red, green, and blue (RGB) values from the center of every indicator solution (in 96-well plates) from the “before” and “after” images. Subtraction of the two images yielded a difference vector of 3N dimensions, where N is total number of sensor units (for our one by five array, this difference vector is 15 dimensions). 6 ACS Paragon Plus Environment
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Digitization of the color differences can be as well as carried out using Adobe Photoshop. The chemometric analysis was performed on the color difference vectors using the Multi-Variate Statistical Package (MVSP v.3.1, Kovach Computing); in all cases, hierarchical cluster analysis (HCA)41,42 and principal component analysis (PCA)43 were performed on the database library (Table S3) using the minimum variance for classification.
Analysis of pesticide residues in real samples Methomyl detection in apple juice and green tea drinks was taken as an example to preliminarily test the capability of our developed array for real samples. The apple juice and green tea drinks were purchased at a local supermarket and subjected to decolorizing by activated charcoal (10% weight of the sample). The analysis procedure was the same as the above description for pesticides detection, just by adding appropriate amounts of real sample (i.e. 1% of apple juice and 5% of green tea drinks) into the work solutions.
RESULTS AND DISCUSSION Principle and fabrication of the array It’s well known that the organophosphorus and carbamate pesticides irreversibly inhibit the activity of AChE in central and peripheral nervous system of humans, which is actually responsible for their high toxicity. In a normal condition, ACh reacts with AChE to form choline that can be in turn catalytically oxidized by ChOx to produce H2O2. Additionally, S-ACh (an analogue of ACh) can be catalyzed by AChE to generate thiocholine. Based on these classical enzyme reactions and assumption of OPs and carbamates having different inhibition capability to AChE, the detection and identification of OPs and carbamates shall be possible through an array of H2O2 and thiocholine sensitive indicators. The “proof-of-principle” concept of the as-fabricated array can be simply described in Scheme 1. If no or with other pesticides, the activity of AChE is retained and AChE can convert S-ACh and ACh into thiocholine and choline, 7 ACS Paragon Plus Environment
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respectively, and choline is further catalyzed by ChOx to H2O2. Then thiocholine and H2O2 induce color changes of the array. However, if OPs and carbamates are present, the activity of AChE is (partially) inhibited and thus the generation of thiocholine and H2O2 is prevented, accompanied by decreasing or no color changes. To mostly simplify the array’s fabrication, herein, five inexpensive and commercially available indicators are employed, i.e. DPDA, TMB and ABTS for H2O2, and DTNB and DCIP for thiocholine (see Supporting Information (SI), Figure S1 for their chemical structures). It’s worthy to note that strongly acidic condition with the presence of Fe2+ was employed for H2O2 sensitive indicators to enhance detection sensitivity due to Fe2+ and H2O2 composing much stronger oxidative Fenton reagent. S-ACh OPs/carbamates
AChE (Partially) Inhibited
No/Less Thiocholine No/Less Choline H2 O2 ChOx
ACh
NO/Less Response
AChE Active AChE No/other pesticides
S-ACh Thiocholine ACh Choline
Strong Response
H2 O2 ChOx
The as-fabricated 1×5 array
AChE: acetylcholinesterase;
ChOx: choline oxidase; ACh: acetylcholine; S-ACh: S-acetylthiocholine
Scheme 1. Schematic illustration of the detection principle of the developed colorimetric sensor array for organophosphorus and carbamate pesticides.
Optimization of the detection conditions Since the basic principle of the fabricated array in this study is based on inhibition of the activity of AChE by OPs and carbamates, thus an appropriate concentration of AChE is critical to achieve good detection performances. As shown in SI Figure S2, color responses of every sensor unit [illustrated by the total Euclidean distances (EDs), i.e. square root of the sums of the squares of the ∆RGB values] are strongly dependent 8 ACS Paragon Plus Environment
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on the concentrations of AChE. According to these results, 2×10-2 unit/mL of AChE for DCIP and ABTS, 2×10-3 unit/mL of AChE for DTNB, DPDA and TMB, respectively, are subsequently selected (marked as red spots in SI Figure S2). The optimized detection conditions that applied to fabricate the array are summarized in SI Table S1. Array's responses to pesticides To examine the detection and identification capability of the as-fabricated array for pesticides, five OPs (i.e. chlorpyrifos, triazophos, phoxim, methamidophos and dimethoate), five carbamates (i.e. fenobucarb, carbaryl, metolcarb, isoprocarb and methomyl), two organochlorines (i.e. α-BHC and DDT), two pyrethroids (i.e. fenvalerate and deltamethrin) and two herbicides (i.e. MCPA and pretilachlor) are randomly taken as examples (see SI Figure S3 for their chemical structures). Firstly, extensive test of the array’s responses to the above pesticides at 10-5 g/L (for OPs and carbamates) or 10-4 g/L (for all the others) are carried out. As demonstrated by the difference maps of the pesticides shown in SI Figure S4, the colorimetric sensor array responses to all the OPs and carbamates with a unique pattern but all of the others are immune, indicating sufficiently in identifying all the investigated OPs and carbamates from other pesticides even at 10-fold higher concentrations. For quantitative comparison of the color changes of the array, we defined a 15-dimensional vector (i.e. five changes in RGB values of the 1×5 array) for each experiment. The high dispersion of the colorimetric sensor array data requires a classification algorithm that uses the full dimensionality of the data. Herein, hierarchical cluster analysis (HCA), which is a model-free method based on the grouping of the analyte vectors according to their spatial distances in their full vector space, is employed. Based on the clustering of the array response data in the 15 dimensional ∆RGB color space, dendrograms formed by HCA are depicted in Figure 1. Interestingly, OPs, carbamates and others (including other types of pesticides, herbicides and a control, the same as below) are classified principally into three main clustering groups, demonstrating clear discrimination of OPs, carbamates and other classes of pesticides. Moreover, in either OPs or carbamates clustering groups, all of them are further accurately identified against one another with 9 ACS Paragon Plus Environment
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no error or misclassifications in quadruplicate experiments. To provide further evidence for the array’s identification capability for OPs and carbamates, principal component analysis (PCA) was also performed. As shown in SI Figure S5, a two-dimensional plot was obtained with three “superclusters”. Similar to HCA, all the investigated pesticides separate principally as carbamates, OPs and others, and every carbamates and OPs further separate exactly from each other. Note that a better differentiation for carbamates than OPs are observed from PCA (SI Figure S5), and this probably implies that carbamates possess more different inhibition capabilities to AChE than that of OPs. The above HCA and PCA results clearly demonstrate that the as-fabricated array could not only be applied for the detection but identification of each OPs and carbamates from other kinds of pesticides.
Control Deltamethrin Fenvalerate
Others
DDT
//
α-BHC MCPA Pretilachlor
//
Carbamates
Isoprocarb Metolcarb Fenobucarb
//
Carbaryl Methomyl
//
Phoxim
//
Triazophos Chlorpyrifos Dimethoate
Organophosphates
Methamidophos
// 0
1000
2000
3000
// 5000
18000
Squared Euclidean
Figure 1. Hierarchical cluster analysis for OPs, carbamates, organochlorines, pyrethroids, herbicides and a control; no confusions or errors in classification for OPs and carbamates were observed in 68 experiments. All of the experiments were performed in quadruplicate (RSDs shown in SI Table S2). The concentrations of OPs and carbamates were 10-5 g/L; all other pesticides were 10-4 g/L.
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To further evaluate the capability of the array in the recognition and discrimination of OPs and carbamates, another set of lower concentrations (e.g. 10-7 g/L) were also investigated. Again, the array presents different colorimetric response maps for each of the OPs and carbamates, but all of others without noticeable responses (SI Figure S4). The HCA and PCA results are similar to that of at 10-5 g/L as well, i.e. all the investigated pesticides being firstly classified into three main groups as OPs, carbamates and others, and the former two classes being further discriminated from each of the other with no error or misclassifications (Figure 2 and SI Figure S6). Control Deltamethrin Fenvalerate
Others
DDT
//
α-BHC MCPA Pretilachlor
Carbamates
Isoprocarb Metolcarb Fenobucarb Carbaryl Methomyl
//
Chlorpyrifos Triazophos Phoxim Dimethoate Methamidophos
Organophosphates
// 0
400
800
1200
6000
Squared Euclidean
Figure 2. Hierarchical cluster analysis for OPs, carbamates, organochlorines, pyrethroids, herbicides and a control; no confusions or errors in classification for organophosphates and carbamates were observed in 68 experiments. All of the experiments were performed in quadruplicate (RSDs shown in SI Table S2). The concentrations of OPs and carbamates were 10-7 g/L; all other pesticides were at 10-4 g/L. The limits of detection (LODs) are subsequently calculated to examine the sensitivity of the array for OPs and carbamates by extrapolating from the observed array responses at the lower investigated concentration (i.e. 10-7 g/L). We have defined the LOD for our array response as the pesticide concentration needed to give three times the S/N vs. background for the sum of the three largest responses among the 15 color 11 ACS Paragon Plus Environment
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changes. The results of calculation reveal that the LODs for all the investigated OPs and carbamates are to be about 10-8 g/L (i.e. LODs for chlorpyrifos, triazophos, phoxim, methamidophos, dimethoate, methomyl, metolcarb, carbaryl, isoprocarb and fenobucarb are 4.6×10-8, 3.0×10-8, 3.8×10-8, 3.5×10-8, 3.3×10-8, 2.5×10-8, 2.1×10-8, 2.3×10-8, 2.4×10-8 and 2.3×10-8 g/L, respectively). Note that all these LODs meet the China national food safety standard for maximum residue limits for pesticides in food.44
Array’s discrimination capability to OPs and carbamates The response of a colorimetric sensor array depends primarily on equilibrium interactions between the indicators and the analytes. Consequently, different concentrations of the same analyte present different array responses. By combining the data sets at 10-7 and 10-5 g/L, we can distinctly differentiate the array responses to the same analytes at different concentrations. The HCA for the full set of database at 10-5 and 10-7 g/L (SI Table S3) indicates that the array could accurately identify all the investigated OPs and carbamates at 10-5 and 10-7 g/L against one another (SI Figure S7). All the above statistical analysis results (HCA and PCA) demonstrate that the as-fabricated colorimetric sensor array can be applied for effective identification and discrimination of OPs and carbamates at 10-5 and 10-7 g/L.
Array’s sensing properties to a single pesticide To examine potentials of the as-fabricated colorimetric sensor array for recognition of OPs and carbamates in a broad range of concentrations and even for quantitative analysis, a carbamate pesticide (i.e. methomyl) was taken as an example. As a result, methomyl induces different recognition patterns from 10-8 to 10-4 g/L (SI Figure S8). HCA further shows that methomyl at all these investigated concentrations and a control can be accurately identified against one another with no error or misclassifications out of 27 cases (Figure 3). Note that 10-4 g/L of methomyl is found to be misclassified with 5×10-4 g/L due to reaching saturation of the array’s response.
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5×10-4 g/L-3 5×10-4 g/L-2 10-4 g/L-3 10-4 g/L-2 5×10-4 g/L-1 10-4 g/L-1 5×10-5 g/L-2 5×10-5 g/L-3 5×10-5 g/L-1 10-5 g/L-3 10-5 g/L-2 10-5 g/L-1 5×10-6 g/L-2 5×10-6 g/L-3 5×10-6 g/L-1 10-6 g/L-3 10-6 g/L-2 10-6 g/L-1 5×10-7 g/L-3 5×10-7 g/L-2 5×10-7 g/L-1 10-7 g/L-3 10-7 g/L-2 10-7 g/L-1 10-8 g/L-3 10-8 g/L-2 10-8 g/L-1 Control-3 Control-2 Control-1
//
//
//
//
//
//
0
200
600
400
// 1200
7200
Squared Euclidean Distance
Figure 3. Hierarchical cluster analysis for methomyl at different concentrations and a control. All of the experiments were performed in triplicate.
We next examined the relationship between the color changes of the array (illustrated by EDs) and concentrations of methomyl. As depicted in Figure 4, the EDs of the array increase gradually with the concentrations of methomyl. These results indicate that the as-fabricated colorimetric sensor array shall be employed not only for recognition of pesticides in broad range of concentrations, but for semi-quantitative analysis [based on the corresponding fitting curve between the color changes of the array (total EDs) and the concentrations of a pesticide]. 60
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40
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20
10
0 0
1000
2000
3000
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5000
Methomyl concentration (10-7 g/L)
Figure 4. The total Euclidean distances (EDs) of the array plotted versus the concentrations of methomyl. All of the experiments were performed in triplicate; the error bars showed the standard deviation of triplicate experiments. 13 ACS Paragon Plus Environment
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Selectivity and anti-interference capability of the array Subsequently, selectivity and anti-interference capability of the developed array for OPs and carbamates are studied. As can be seen in SI Figure S4, none of other pesticides such as organochlorines (e.g. DDT and α-BHC), pyrethroids (e.g. deltamethrin and fenvalerate) and herbicides (e.g. pretilachlor and MCPA) induced any significant responses to the array even at relative high concentrations (10-4 g/L). The results came as no surprise due to the detection mechanism being based on the nature of OPs and carbamates to inhibit the activity of AChE, while other pesticides have no such a capability. In addition, some common interferants in food samples, such as glucide (e.g. glucose and sucrose), metal ions (e.g. K+ and Na+), organic acids (e.g. vitamin C), and amine acids (e.g. lysine), do not induce noticeable array’s responses as well (Figure S9). Furthermore, the sensor array shows closely identical responses to methomyl in the absence and in the presence of the above common interferants in food samples (Figure 5). These results demonstrate that the as-fabricated sensor array is of high specificity and strong anti-interference capability for OPs and carbamates. 50
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Euclidean Distance
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30
20
10
0 Blank
Na+
K+
Glucose Sucrose Lysine Vitamin C
Figure 5. Responses of the as-fabricated sensor array to a variety of possible interferants in food samples in the absence (gray bar) and presence (black bar) of 10-5 g/L methomyl. The concentrations of Na+, K+, glucose and sucrose were 10-3 g/L; lysine was at 10-4 g/L and vitamin C was at 10-5 g/L. The error bars shown are the standard deviation of triplicate experiments.
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Detection of pesticide residues in real samples Finally, the potentials of the as-developed sensor array for OPs and carbamates in real samples (e.g. apple juice and green tea drinks) are evaluated. Since our method is principally relying on color changes, a colored sample has to be decolorized, such as activated charcoal, before the analysis. Then, the same procedure as the above description for pesticides detection in buffer is applied, just by adding an appropriate amount of a decolorized sample into the work solutions. Note that our experiments displayed that maximal 1% apple juice and 5% green tea drinks could be included in the analysis owing to their complicated composition. As shown in Figure 6, both similar recognition patterns and responses (EDs) for methomyl (e.g. 10-6 g/L) in the absence and presence of decolorized apple juice and green tea drinks are observed, revealing applicability of the developed sensor array for OPs and carbamates in real samples. Additionally, a comparing experiment, i.e. the same concentration of a pesticide (10-6 g/L methomyl as an example again) being added into the juice and green tea samples before and after decolorization treatment, was performed to investigate potential effects of adsorption pesticides by activated charcoal. As seen in Figure S10, the array displays alike responses to all of these samples, demonstrating negligible adsorption of pesticides by activated charcoal under our decolorization condition (i.e. with 10% weight of the sample).
Figure 6. a) Color difference maps of the array for 10-6 g/L methomyl in the absence (I) and presence of 1% apple juice (II), and 5% green tea drinks (III). For purposes of visualization, the color range of these difference maps of methomyl was expanded from 3 to 8 bits per color (RGB range of 3-10 expanded to 0-255); b) Responses of the developed sensor array to 10-6 g/L methomyl in the absence (I) and presence of 1% apple juice (II), and 5% green tea drinks (III). The error bars showed the standard deviation of triplicate experiments. 15 ACS Paragon Plus Environment
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CONCLUSIONS In summary, a colorimetric sensor array that simply composing five inexpensive and commercially available thiocholine and H2O2 sensitive indicators for the simultaneous detection and identification of OPs and carbamates was developed. The sensing mechanism of this array is based on the irreversible inhibition capability of OPs and carbamates to the activity of AChE, preventing production of thiocholine and H2O2 from S-ACh and ACh and thus resulting in decreased or no color reactions to thiocholine and H2O2 sensitive indicators. Classification analysis (i.e. HCA and PCA) reveals that the as-developed sensor array has an extremely high dimensionality and, consequently, has the capacity to detect and discriminate a variety of OPs (e.g. chlorpyrifos, triazophos, phoxim, methamidophos and dimethoate) and carbamates (e.g. carbaryl, methomyl, metolcarb, isoprocarb and fenobucarb). Moreover, semiquantitative detections are also able to be achieved through combining HCA/PCA, recognition patterns and corresponding fitting curves. The unique features of this current method are the high selectivity and sensitivity, good anti-interference capability, simultaneous detection and identification each of OPs and carbamates. Above all, this array method shows potential applications for the detection of OPs and carbamates in real samples as well. ACKNOWLEDGMENTS The authors acknowledge the Natural Science Foundation of China (21277149), Zhejiang Provincial Natural Science Foundation of China (LR13B050001) and the Ningbo Science and Technology Bureau (2014B82010) for supporting this work.
SUPPORTING INFORMATION AVAILABLE Chemical structures of the commercially available indicators and pesticides; optimization of AChE concentrations; colorimetric difference maps; HCA; PCA; Figure S1 to S10; Tables S1-S3. This information is available free of charge via the Internet at http://pubs.acs.org.
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REFERENCES (1) Tankiewicz, M.; Fenik, J.; Biziuk, M. Trends Anal. Chem. 2010, 29, 1050–1063. (2) Eddleston, M.; Worek, F.; Eyer, P.; Thiermann, H.; von Meyer, L.; Jeganathan, K.; Sheriff, M. H. R.; Dawson, A. H.; Buckley, N. A. Q. J. Med. 2009, 102, 785–792. (3) Kamel, A.; Byrne, C.; Vigo, C.; Ferrario, J.; Stafford, C.; Verdin, G.; Siegelman, F.; Knizner, S.; Hetrick, J. Water Res. 2009, 43, 522–534. (4) Moffat, C. F.; Whittle, K. J. Environmental contaminants in food; Sheffield Academic Press: Sheffield, 1999. (5) http://www.epa.gov/pesticides/cumulative. (6) Kim, K.; Tsay, O. G.; Atwood, D. A.; Churchill, D. G. Chem. Rev. 2011, 111, 5345– 5403. (7) Aragay, G.; Pino, F.; Merkoçi, A. Chem. Rev. 2012, 112, 5317–5338. (8) Wang, J.; Chow, W.; Leung, D.; Chang, J. J. Agric. Food Chem. 2012, 60, 12088– 12104. (9) Kiso, Y.; Li, H.; Shigetoh, K.; Kitao, T.; Jinno, K. J. Chromatogr. A 1996, 733, 259– 265. (10) Gómez-Ramos, M. M.; Ferrer, C.; Malato, O.; Agüera, A.; Fernández-Alba, A. R. J. Chromatogr. A 2013, 1287, 24– 37. (11) Viswanathan, S.; Radecka, H.; Radecki, J. Biosens. Bioelectron. 2009, 24, 2772−2777. (12) Liu, G. D.; Lin, Y. H. Anal. Chem. 2005, 77, 5894−5901. (13) Gong, J. M.; Guan, Z. Q.; Song, D. D. Biosens. Bioelectron. 2013, 39, 320−323. (14) Llorent-Martinez, E. J.; Delgado-Blanca, I.; Ruiz-Medina, A.; Ortega-Barrales, P. Talanta 2013, 115, 462−467. (15) Qian, G. L.; Wang, L. M.; Wu, Y. R.; Zhang, Q.; Sun, Q.; Liu, Y.; Liu, F. Q. Food Chem. 2009, 117, 364−370. (16) Gabaldon, J. A.; Maquieira, A.; Puchades, R. Talanta 2007, 71, 1001–1010. (17) Fu, G. L.; Chen, W. W.; Yue, X. L.; Jiang, X. Y. Talanta 2013, 103, 110–115. (18) Sun, J. F.; Guo, L.; Bao, Y.; Xie, J. W. Biosens. Bioelectron. 2011, 28, 152−157. 17 ACS Paragon Plus Environment
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(19) Liu, D. B.; Chen, W. W.; Wei, J. H.; Li, X. B.; Wang, Z.; Jiang, X. Y.; Anal. Chem. 2012, 84, 4185−4191. (20) Liang, M. M.; Fan, K. L.; Pan, Y.; Jiang, H.; Wang, F.; Yang, D. L.; Lu, D.; Feng, J.; Zhao, J. J.; Yang, L.; Yan, X. Y. Anal. Chem. 2013, 85, 308−312. (21) Yi, Y. H.; Zhu, G. B.; Liu, C.; Huang, Y.; Zhang, Y. Y.; Li, H. T.; Zhao, J. N.; Yao, S. Z. Anal. Chem. 2013, 85, 11464−11470. (22) Zhang, K.; Mei, Q. S.; Guan, G. J.; Liu, B. H.; Wang, S. H.; Zhang, Z. P. Anal. Chem. 2010, 82, 9579–9586. (23) Zhang, K.; Yu, T.; Liu, F.; Sun, M. T.; Yu, H.; Liu, B. H.; Zhang, Z. P.; Jiang, H.; Wang, S. H. Anal. Chem. 2014, 86, 11727−11733. (24) Askim, J. R.; Mahmoudi, M.; Suslick, K. S. Chem. Soc. Rev. 2013, 42, 8649−8682. (25) Diehl, K. L.; Anslyn, E. V. Chem. Soc. Rev. 2013, 42, 8596−8611. (26) Wiskur, S. L.; Ait-Haddou, H.; Lavigne, J. J.; Anslyn, E. V. Acc. Chem. Res. 2001, 34, 963−972. (27) Anzenbacher Jr., P.; Lubal, P.; Buček, P.; Palacios M. A.; Kozelkov, M. E. Chem. Soc. Rev. 2010, 39, 3954−3979. (28) Zarzo, M. Biol. Rev. 2007, 82, 455−479. (29) Wang, J.; Luthey-Schulten, Z. A.; Suslick, K. S. Proc. Natl Acad. Sci. USA 2003, 100, 3035−3039. (30) You, C. C.; Miranda, O. R.; Gider, B.; Ghoash, P. S.; Kim, I. B.; Erdogan, B.; Krovi, S. A.; Bunz, U. H. F.; Rotello, V. M. Nat. Nanotechnol. 2007, 2, 318−323. (31) Folmer-Andersen, J. F.; Kitamura, M.; Anslyn, E. V. J. Am. Chem. Soc. 2006, 128, 5652−5653. (32) Minami, T.; Liu, Y.; Akdeniz, A.; Koutnik, P.; Esipenko, N. A.; Nishiyabu, R.; Kubo, Y.; Anzenbacher Jr., P. J. Am. Chem. Soc. 2014, 136, 11396−11401. (33) Liu, Y. L.; Bonizzoni, M. J. Am. Chem. Soc. 2014, 136, 14223–14229. (34) Esipenko, N. A.; Koutnik, P.; Minami, T.; Mosca, L.; Lynch, V. M.; Zyryanov, G. V.; Anzenbacher Jr., P. Chem. Sci. 2013, 4, 3617–3623. 18 ACS Paragon Plus Environment
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(35) de Greñu, B. D.; Moreno, D.; Torroba, T.; Berg, A.; Gunnars, J.; Nilsson, T.; Nyman, R.; Persson, M.; Pettersson, J.; Eklind, I.; Wästerby, P. J. Am. Chem. Soc. 2014, 136, 4125−4128. (36) Pascuala, L.; Camposa, I.; Batallera, R.; Olguín, C.; García-Breijo, E.; Martínez-Mañeza, R.; Soto, J. Sens. Actuators, B 2014, 192, 134−142. (37) Di Pietrantonio, F.; Benetti, M.; Cannatà, D.; Verona, E.; Palla-Papavlu, A.; Dinca, V.; Dinescu, M.; Mattle, T.; Lippert, T. Sens. Actuators, B 2012, 174, 158−167. (38) Feng, L.; Musto, C. J.; Kemling, J. W.; Lim, S. H.; Suslick, K. S. Chem. Commun. 2010, 46, 2037−2039. (39) Lin, H. W.; Jang, M.; Suslick, K. S. J. Am. Chem. Soc. 2011, 133, 16786−16789. (40) Kumar, V.; Anslyn, E. V. Chem. Sci. 2013, 4, 4292−4297. (41) Hasswell, S. Practical Guide to Chemometrics; Dekker: New York, 1992. (42) Johnson, R. A.; Wichern, D. W. Applied Multivariate Statistical Analysis, 6th ed.; Prentice Hall: Upper Saddle River, NJ, 2007. (43) Jolliffe, I. T. Principal Component Analysis, Springer-Verlag, New York, ed., 2002. (44) National food safety standard (China), maximum residue limits for pesticides in food (GB 2763-2012).
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For TOC only
2
Carbaryl
1
PC2 (5.62%)
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Methamidophos Chlorpyrifos
0
Methomyl
Baycarb
Metolcarb
Dimethoate Other pesticides, herbicides and Triazophos a control
-1
Phoxim Isoprocarb
-2 -5
Organophosphates
Carbamates -4
-3
-2 -1 0 PC1 (90.85%)
1
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Article
Colorimetric Sensor Array for Detection and Identification of Organophosphorus and Carbamate Pesticides Sihua Qian, and Hengwei Lin Anal. Chem., Just Accepted Manuscript • Publication Date (Web): 27 Apr 2015 Downloaded from http://pubs.acs.org on April 27, 2015
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Analytical Chemistry
Colorimetric Sensor Array for Detection and Identification of Organophosphorus and Carbamate Pesticides Sihua Qian and Hengwei Lin*
Ningbo Institute of Materials Technology and Engineering (NIMTE), Chinese Academy of Sciences, Ningbo 315201, P. R. China
*E-mail: [email protected]; Tel: +86 574 86685130; Fax: +86 574 86685163
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ABSTRACT Due to relatively low persistence and high effectiveness for insect and pest eradication, organophosphates (OPs) and carbamates are the two major classes of pesticides that broadly used in agriculture. Hence, the sensitive and selective detection of OPs and carbamates is highly significant. In this current study, a colorimetric sensor array composing five inexpensive and commercially available thiocholine and H2O2 sensitive indicators for the simultaneous detection and identification of OPs and carbamates is developed. The sensing mechanism of this array is based on the irreversible inhibition capability of OPs and carbamates to the activity of acetylcholinesterase (AChE), preventing production of thiocholine and H2O2 from S-acetylthiocholine and acetylcholine and thus resulting in decreased or no color reactions to thiocholine and H2O2 sensitive indicators. Through recognition patterns and standard statistical methods (i.e. hierarchical clustering analysis and principal component analysis), the as-developed array demonstrates not only discrimination of OPs and carbamates from other kinds of pesticides, but more interestingly, identification of them exactly from each other. Moreover, this array is experimentally confirmed to be high selectivity and sensitivity, good anti-interference capability, and potential applications in real samples for OPs and carbamates.
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INTRODUCTION A myriad of pesticides, including diverse classes of organophosphates (OPs), carbamates, triazines, chloroacetanilides and pyrethroids, are used to protect plants from insects and pests infestation in agriculture across the world. The widespread and long-term usage of pesticides, however, caused serious contamination of air, water, soil and agricultural products, eventually being harmfulness to the ecosystems including humans.1-5 Due to the relatively low persistence under natural conditions and high effectiveness for insect and pest eradication, OPs and carbamates are the two major classes of pesticides that have been the most routinely used. Nevertheless, they also exhibit acute toxicity on human health through their residues in agricultural products and contamination of water.6,7 The high toxicity of OPs and carbamates is ascribed to their capability to irreversibly inhibit the activity of acetylcholinesterase (AChE) in the central and peripheral nervous system, resulting in accumulation of the neurotransmitter acetylcholine (ACh) in the body and thus inflicting serious harm to the human nervous system, respiratory tract, and cardiovascular system, which can lead to organ failure and eventual death. Therefore, the selective detection and quantitative determination of OPs and carbamates are highly desirable regarding to the concerns of the public safety and environmental protection. Many techniques, such as high-performance liquid chromatography (HPLC),8,9 gas chromatography-mass
spectrometry
(GC-MS),10
electrochemical
analysis,11,12
immunochips13,14 and enzyme-linked immunosorbent assays (ELISAs)15,16 have been developed for effective detection of pesticides in food and water in the past decades. Although these methods show high selectivity and adequate sensitivity and allow discrimination of pesticides, they are costly and/or rely upon sophisticated instruments and skilled manpower, making these approaches impractical for regular environments and food safety monitoring. To overcome these drawbacks, some novel methods, e.g. nanoparticle (NP) and AChE inhibition based colorimetric approaches, have recently emerged.17-23 However, the NP-based assays frequently encounter challenges of uncontrolled aggregation of NPs in real samples, thus resulting in a poor specificity. In 3 ACS Paragon Plus Environment
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addition, by taking advantages of the inhibition nature of OPs and carbamates to the activity of AChE, they can be recognized indirectly through detection of thiocholine18,19 or H2O2 (Ref. 20,21). Thanks to highly specific enzyme reactions, these AChE inhibition based methods usually show excellent selectivity and sensitivity to OPs and carbamates, but studies of discrimination between these pesticides have not yet reported. Considering practical requirements, it is of great importance to develop a simple, sensitive and cost-efficient method that can be not only for detection, but also for identification of pesticides. Fortunately, the recently established sensor-array technologies and pattern-recognition methods make such a task possible. Array sensing techniques have emerged as a potentially powerful approach toward the detection and recognition of chemically diverse analytes.24-27 Based on cross-responsive sensing elements rather than specific receptors, sensor arrays produce composite responses unique to an analyte in a fashion similar to the mammalian olfactory system.28-31 Although some array-based approaches have been developed for the detection and identification of poisonous organophosphorus chemicals, such as pesticides and herbicides,32,33 nerve agents34-37 and phosphorus-containing toxic gases,38-40 all of these arrays crucially rely on either costly or tediously synthesized molecular probes, and thus significantly limit their practical applicability. Hence, the further studies and developments of simple array based methods for simultaneous detection and discrimination of OPs and carbamates are still desirable. Based on the facts of inhibition of AChE activity by OPs and carbamates, herein, five inexpensive and commercially available H2O2 or thiocholine sensitive probes are taken to fabricate a colorimetric sensor array. This array is confirmed not only to excellently discriminate OPs and carbamates from other kinds of pesticides, but identify them exactly from each other. To the best of knowledge, this is the first example to achieve simultaneous detection and identification of a variety of OPs and carbamates through a simple array sensing technique. Finally, the potential applications of this array for the determination of OPs and carbamates in real samples, such as apple juice and green tea drinks, are observed as well. 4 ACS Paragon Plus Environment
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EXPERIMENTAL SECTION Reagents and materials All
chemicals
from
commercial
sources
are
of
analytical
grade.
Acetylcholinesterase (AChE, from Electrophorus electricus) and choline oxidase (ChOx, from Alcaligenes sp.) were obtained from Sigma-Aldrich. Acetylcholine iodide (ACh) and S-acetylthiocholine chloride (S-ACh) were purchased from Aladdin and Alfa Aesar, respectively. Thiocholine or H2O2 sensitive indicators: 5,5'-dithio bis-(2-nitrobenzoic acid) (DTNB), sodium 2,6-dichloroindophenolate hydrate (DCIP), N,N'-diphenyl1,4-diphenyldiamine (DPDA) and 3,3,5,5-tetramethylbenzidine (TMB) were obtained from Aladdin; 2,2'-azinobis-(3-ethylbenzthiazoline-6-sulphonate) (ABTS) was from Sigma-Aldrich.
Pesticides:
chlorpyrifos,
triazophos,
phoxim,
methamidophos,
dimethoate, carbaryl, methomyl, metolcarb, isoprocarb, pretilachlor and deltamethrin were purchased from Aladdin; fenobucarb, fenvalerate, DDT and α-BHC were provided by J&K Scientific; 2-methyl-4-chloro-phenoxyacetic acid (MCPA) sodium salt monohydrate was from Sigma-Aldrich. Glucose, sucrose and vitamin C were obtained from Sinopharm Chemical Reagent Co. Ltd, and lysine was provided by Aladdin. All reagents were used as received without further purification unless otherwise specified. Deionized water was used throughout this study. Stock solutions of ACh (10 mM), S-ACh (5.0 mM), AChE (10 unit/mL) and ChOx (10 unit/mL) were freshly prepared in PBS (10 mM, pH 7.4). The stock solutions of methamidophos, dimethoate and MCPA sodium salt monohydrate were directly dissolved in deionized water (0.1 g/L), and chlorpyrifos, triazophos, phoxim, carbaryl, methomyl, metolcarb, isoprocarb, pretilachlor, deltamethrin, fenobucarb, fenvalerate, DDT and α-BHC were initially dissolved in ethanol (0.1 g/L) and diluted at least 100-fold by PBS (10 mM, pH 7.4) before use. Note that all these stock solutions were freshly prepared before the experiments to minimize their potential effects of hydrolysis. Instrumentation For all sensing experiments, imaging of the arrays was performed with a flatbed 5 ACS Paragon Plus Environment
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scanner (Epson Perfection V300). The pH measurements were performed using a PHS-3C pH meter. 96-well plates (Corning 3632) were purchased from Genetimes Technology Incorporated.
Experimental procedure for the detection of pesticides Control solutions for the as-fabricated array: AChE (2×10-2 or 2×10-3 unit/mL) and S-ACh (1.0 or 0.4 mM) were incubated in PBS (10 mM, pH 7.4) at 37 ºC for 10 min (the same as below) and then added into DCIP (or DTNB, both in 10 µM) in PBS (10 mM, pH 7.4); AChE (2×10-3 unit/mL) and ACh (0.5 mM) were firstly incubated for 10 min and then ChOx (0.4 unit/mL) was added to incubate for another 10 min and finally added into DPDA (10 µM, and presence of 10 µM Fe2+ at pH 1.0) or TMB (40 µM, and presence of 40 µM Fe2+ at pH 3.0) in PBS (20 mM); 2×10-2 unit/mL of AChE replaced the just above procedure to prepare the enzyme reaction solution and added into ABTS (40 µM, and presence of 40 µM Fe2+ at pH 1.0) in PBS (20 mM). The composition of the developed array is summarized in Table S1. Work solutions were prepared in the same procedures as that of for control solutions, just by adding a desired amount of a certain pesticide during the preparation of the enzyme reaction solutions before adding into the indicator solutions. Then, 300 µL of the control and work solutions were loaded into a 96-well plate respectively, and the “before” (from the control solutions) and “after” (from the work solutions) images were acquired on an Epson Perfection V300 Photo flatbed scanner. All the analyses of pesticides samples were conducted in quadruplicate (RSDs are shown in Table S2). For each trial, a color change profile was obtained by subtracting the “before” image from the “after” image using the commercial Photoshop software. Difference maps were acquired by taking the difference of the red, green, and blue (RGB) values from the center of every indicator solution (in 96-well plates) from the “before” and “after” images. Subtraction of the two images yielded a difference vector of 3N dimensions, where N is total number of sensor units (for our one by five array, this difference vector is 15 dimensions). 6 ACS Paragon Plus Environment
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Digitization of the color differences can be as well as carried out using Adobe Photoshop. The chemometric analysis was performed on the color difference vectors using the Multi-Variate Statistical Package (MVSP v.3.1, Kovach Computing); in all cases, hierarchical cluster analysis (HCA)41,42 and principal component analysis (PCA)43 were performed on the database library (Table S3) using the minimum variance for classification.
Analysis of pesticide residues in real samples Methomyl detection in apple juice and green tea drinks was taken as an example to preliminarily test the capability of our developed array for real samples. The apple juice and green tea drinks were purchased at a local supermarket and subjected to decolorizing by activated charcoal (10% weight of the sample). The analysis procedure was the same as the above description for pesticides detection, just by adding appropriate amounts of real sample (i.e. 1% of apple juice and 5% of green tea drinks) into the work solutions.
RESULTS AND DISCUSSION Principle and fabrication of the array It’s well known that the organophosphorus and carbamate pesticides irreversibly inhibit the activity of AChE in central and peripheral nervous system of humans, which is actually responsible for their high toxicity. In a normal condition, ACh reacts with AChE to form choline that can be in turn catalytically oxidized by ChOx to produce H2O2. Additionally, S-ACh (an analogue of ACh) can be catalyzed by AChE to generate thiocholine. Based on these classical enzyme reactions and assumption of OPs and carbamates having different inhibition capability to AChE, the detection and identification of OPs and carbamates shall be possible through an array of H2O2 and thiocholine sensitive indicators. The “proof-of-principle” concept of the as-fabricated array can be simply described in Scheme 1. If no or with other pesticides, the activity of AChE is retained and AChE can convert S-ACh and ACh into thiocholine and choline, 7 ACS Paragon Plus Environment
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respectively, and choline is further catalyzed by ChOx to H2O2. Then thiocholine and H2O2 induce color changes of the array. However, if OPs and carbamates are present, the activity of AChE is (partially) inhibited and thus the generation of thiocholine and H2O2 is prevented, accompanied by decreasing or no color changes. To mostly simplify the array’s fabrication, herein, five inexpensive and commercially available indicators are employed, i.e. DPDA, TMB and ABTS for H2O2, and DTNB and DCIP for thiocholine (see Supporting Information (SI), Figure S1 for their chemical structures). It’s worthy to note that strongly acidic condition with the presence of Fe2+ was employed for H2O2 sensitive indicators to enhance detection sensitivity due to Fe2+ and H2O2 composing much stronger oxidative Fenton reagent. S-ACh OPs/carbamates
AChE (Partially) Inhibited
No/Less Thiocholine No/Less Choline H2 O2 ChOx
ACh
NO/Less Response
AChE Active AChE No/other pesticides
S-ACh Thiocholine ACh Choline
Strong Response
H2 O2 ChOx
The as-fabricated 1×5 array
AChE: acetylcholinesterase;
ChOx: choline oxidase; ACh: acetylcholine; S-ACh: S-acetylthiocholine
Scheme 1. Schematic illustration of the detection principle of the developed colorimetric sensor array for organophosphorus and carbamate pesticides.
Optimization of the detection conditions Since the basic principle of the fabricated array in this study is based on inhibition of the activity of AChE by OPs and carbamates, thus an appropriate concentration of AChE is critical to achieve good detection performances. As shown in SI Figure S2, color responses of every sensor unit [illustrated by the total Euclidean distances (EDs), i.e. square root of the sums of the squares of the ∆RGB values] are strongly dependent 8 ACS Paragon Plus Environment
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on the concentrations of AChE. According to these results, 2×10-2 unit/mL of AChE for DCIP and ABTS, 2×10-3 unit/mL of AChE for DTNB, DPDA and TMB, respectively, are subsequently selected (marked as red spots in SI Figure S2). The optimized detection conditions that applied to fabricate the array are summarized in SI Table S1. Array's responses to pesticides To examine the detection and identification capability of the as-fabricated array for pesticides, five OPs (i.e. chlorpyrifos, triazophos, phoxim, methamidophos and dimethoate), five carbamates (i.e. fenobucarb, carbaryl, metolcarb, isoprocarb and methomyl), two organochlorines (i.e. α-BHC and DDT), two pyrethroids (i.e. fenvalerate and deltamethrin) and two herbicides (i.e. MCPA and pretilachlor) are randomly taken as examples (see SI Figure S3 for their chemical structures). Firstly, extensive test of the array’s responses to the above pesticides at 10-5 g/L (for OPs and carbamates) or 10-4 g/L (for all the others) are carried out. As demonstrated by the difference maps of the pesticides shown in SI Figure S4, the colorimetric sensor array responses to all the OPs and carbamates with a unique pattern but all of the others are immune, indicating sufficiently in identifying all the investigated OPs and carbamates from other pesticides even at 10-fold higher concentrations. For quantitative comparison of the color changes of the array, we defined a 15-dimensional vector (i.e. five changes in RGB values of the 1×5 array) for each experiment. The high dispersion of the colorimetric sensor array data requires a classification algorithm that uses the full dimensionality of the data. Herein, hierarchical cluster analysis (HCA), which is a model-free method based on the grouping of the analyte vectors according to their spatial distances in their full vector space, is employed. Based on the clustering of the array response data in the 15 dimensional ∆RGB color space, dendrograms formed by HCA are depicted in Figure 1. Interestingly, OPs, carbamates and others (including other types of pesticides, herbicides and a control, the same as below) are classified principally into three main clustering groups, demonstrating clear discrimination of OPs, carbamates and other classes of pesticides. Moreover, in either OPs or carbamates clustering groups, all of them are further accurately identified against one another with 9 ACS Paragon Plus Environment
Analytical Chemistry
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no error or misclassifications in quadruplicate experiments. To provide further evidence for the array’s identification capability for OPs and carbamates, principal component analysis (PCA) was also performed. As shown in SI Figure S5, a two-dimensional plot was obtained with three “superclusters”. Similar to HCA, all the investigated pesticides separate principally as carbamates, OPs and others, and every carbamates and OPs further separate exactly from each other. Note that a better differentiation for carbamates than OPs are observed from PCA (SI Figure S5), and this probably implies that carbamates possess more different inhibition capabilities to AChE than that of OPs. The above HCA and PCA results clearly demonstrate that the as-fabricated array could not only be applied for the detection but identification of each OPs and carbamates from other kinds of pesticides.
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Figure 1. Hierarchical cluster analysis for OPs, carbamates, organochlorines, pyrethroids, herbicides and a control; no confusions or errors in classification for OPs and carbamates were observed in 68 experiments. All of the experiments were performed in quadruplicate (RSDs shown in SI Table S2). The concentrations of OPs and carbamates were 10-5 g/L; all other pesticides were 10-4 g/L.
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To further evaluate the capability of the array in the recognition and discrimination of OPs and carbamates, another set of lower concentrations (e.g. 10-7 g/L) were also investigated. Again, the array presents different colorimetric response maps for each of the OPs and carbamates, but all of others without noticeable responses (SI Figure S4). The HCA and PCA results are similar to that of at 10-5 g/L as well, i.e. all the investigated pesticides being firstly classified into three main groups as OPs, carbamates and others, and the former two classes being further discriminated from each of the other with no error or misclassifications (Figure 2 and SI Figure S6). Control Deltamethrin Fenvalerate
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Figure 2. Hierarchical cluster analysis for OPs, carbamates, organochlorines, pyrethroids, herbicides and a control; no confusions or errors in classification for organophosphates and carbamates were observed in 68 experiments. All of the experiments were performed in quadruplicate (RSDs shown in SI Table S2). The concentrations of OPs and carbamates were 10-7 g/L; all other pesticides were at 10-4 g/L. The limits of detection (LODs) are subsequently calculated to examine the sensitivity of the array for OPs and carbamates by extrapolating from the observed array responses at the lower investigated concentration (i.e. 10-7 g/L). We have defined the LOD for our array response as the pesticide concentration needed to give three times the S/N vs. background for the sum of the three largest responses among the 15 color 11 ACS Paragon Plus Environment
Analytical Chemistry
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changes. The results of calculation reveal that the LODs for all the investigated OPs and carbamates are to be about 10-8 g/L (i.e. LODs for chlorpyrifos, triazophos, phoxim, methamidophos, dimethoate, methomyl, metolcarb, carbaryl, isoprocarb and fenobucarb are 4.6×10-8, 3.0×10-8, 3.8×10-8, 3.5×10-8, 3.3×10-8, 2.5×10-8, 2.1×10-8, 2.3×10-8, 2.4×10-8 and 2.3×10-8 g/L, respectively). Note that all these LODs meet the China national food safety standard for maximum residue limits for pesticides in food.44
Array’s discrimination capability to OPs and carbamates The response of a colorimetric sensor array depends primarily on equilibrium interactions between the indicators and the analytes. Consequently, different concentrations of the same analyte present different array responses. By combining the data sets at 10-7 and 10-5 g/L, we can distinctly differentiate the array responses to the same analytes at different concentrations. The HCA for the full set of database at 10-5 and 10-7 g/L (SI Table S3) indicates that the array could accurately identify all the investigated OPs and carbamates at 10-5 and 10-7 g/L against one another (SI Figure S7). All the above statistical analysis results (HCA and PCA) demonstrate that the as-fabricated colorimetric sensor array can be applied for effective identification and discrimination of OPs and carbamates at 10-5 and 10-7 g/L.
Array’s sensing properties to a single pesticide To examine potentials of the as-fabricated colorimetric sensor array for recognition of OPs and carbamates in a broad range of concentrations and even for quantitative analysis, a carbamate pesticide (i.e. methomyl) was taken as an example. As a result, methomyl induces different recognition patterns from 10-8 to 10-4 g/L (SI Figure S8). HCA further shows that methomyl at all these investigated concentrations and a control can be accurately identified against one another with no error or misclassifications out of 27 cases (Figure 3). Note that 10-4 g/L of methomyl is found to be misclassified with 5×10-4 g/L due to reaching saturation of the array’s response.
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5×10-4 g/L-3 5×10-4 g/L-2 10-4 g/L-3 10-4 g/L-2 5×10-4 g/L-1 10-4 g/L-1 5×10-5 g/L-2 5×10-5 g/L-3 5×10-5 g/L-1 10-5 g/L-3 10-5 g/L-2 10-5 g/L-1 5×10-6 g/L-2 5×10-6 g/L-3 5×10-6 g/L-1 10-6 g/L-3 10-6 g/L-2 10-6 g/L-1 5×10-7 g/L-3 5×10-7 g/L-2 5×10-7 g/L-1 10-7 g/L-3 10-7 g/L-2 10-7 g/L-1 10-8 g/L-3 10-8 g/L-2 10-8 g/L-1 Control-3 Control-2 Control-1
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Figure 3. Hierarchical cluster analysis for methomyl at different concentrations and a control. All of the experiments were performed in triplicate.
We next examined the relationship between the color changes of the array (illustrated by EDs) and concentrations of methomyl. As depicted in Figure 4, the EDs of the array increase gradually with the concentrations of methomyl. These results indicate that the as-fabricated colorimetric sensor array shall be employed not only for recognition of pesticides in broad range of concentrations, but for semi-quantitative analysis [based on the corresponding fitting curve between the color changes of the array (total EDs) and the concentrations of a pesticide]. 60
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Figure 4. The total Euclidean distances (EDs) of the array plotted versus the concentrations of methomyl. All of the experiments were performed in triplicate; the error bars showed the standard deviation of triplicate experiments. 13 ACS Paragon Plus Environment
Analytical Chemistry
Selectivity and anti-interference capability of the array Subsequently, selectivity and anti-interference capability of the developed array for OPs and carbamates are studied. As can be seen in SI Figure S4, none of other pesticides such as organochlorines (e.g. DDT and α-BHC), pyrethroids (e.g. deltamethrin and fenvalerate) and herbicides (e.g. pretilachlor and MCPA) induced any significant responses to the array even at relative high concentrations (10-4 g/L). The results came as no surprise due to the detection mechanism being based on the nature of OPs and carbamates to inhibit the activity of AChE, while other pesticides have no such a capability. In addition, some common interferants in food samples, such as glucide (e.g. glucose and sucrose), metal ions (e.g. K+ and Na+), organic acids (e.g. vitamin C), and amine acids (e.g. lysine), do not induce noticeable array’s responses as well (Figure S9). Furthermore, the sensor array shows closely identical responses to methomyl in the absence and in the presence of the above common interferants in food samples (Figure 5). These results demonstrate that the as-fabricated sensor array is of high specificity and strong anti-interference capability for OPs and carbamates. 50
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Figure 5. Responses of the as-fabricated sensor array to a variety of possible interferants in food samples in the absence (gray bar) and presence (black bar) of 10-5 g/L methomyl. The concentrations of Na+, K+, glucose and sucrose were 10-3 g/L; lysine was at 10-4 g/L and vitamin C was at 10-5 g/L. The error bars shown are the standard deviation of triplicate experiments.
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Detection of pesticide residues in real samples Finally, the potentials of the as-developed sensor array for OPs and carbamates in real samples (e.g. apple juice and green tea drinks) are evaluated. Since our method is principally relying on color changes, a colored sample has to be decolorized, such as activated charcoal, before the analysis. Then, the same procedure as the above description for pesticides detection in buffer is applied, just by adding an appropriate amount of a decolorized sample into the work solutions. Note that our experiments displayed that maximal 1% apple juice and 5% green tea drinks could be included in the analysis owing to their complicated composition. As shown in Figure 6, both similar recognition patterns and responses (EDs) for methomyl (e.g. 10-6 g/L) in the absence and presence of decolorized apple juice and green tea drinks are observed, revealing applicability of the developed sensor array for OPs and carbamates in real samples. Additionally, a comparing experiment, i.e. the same concentration of a pesticide (10-6 g/L methomyl as an example again) being added into the juice and green tea samples before and after decolorization treatment, was performed to investigate potential effects of adsorption pesticides by activated charcoal. As seen in Figure S10, the array displays alike responses to all of these samples, demonstrating negligible adsorption of pesticides by activated charcoal under our decolorization condition (i.e. with 10% weight of the sample).
Figure 6. a) Color difference maps of the array for 10-6 g/L methomyl in the absence (I) and presence of 1% apple juice (II), and 5% green tea drinks (III). For purposes of visualization, the color range of these difference maps of methomyl was expanded from 3 to 8 bits per color (RGB range of 3-10 expanded to 0-255); b) Responses of the developed sensor array to 10-6 g/L methomyl in the absence (I) and presence of 1% apple juice (II), and 5% green tea drinks (III). The error bars showed the standard deviation of triplicate experiments. 15 ACS Paragon Plus Environment
Analytical Chemistry
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CONCLUSIONS In summary, a colorimetric sensor array that simply composing five inexpensive and commercially available thiocholine and H2O2 sensitive indicators for the simultaneous detection and identification of OPs and carbamates was developed. The sensing mechanism of this array is based on the irreversible inhibition capability of OPs and carbamates to the activity of AChE, preventing production of thiocholine and H2O2 from S-ACh and ACh and thus resulting in decreased or no color reactions to thiocholine and H2O2 sensitive indicators. Classification analysis (i.e. HCA and PCA) reveals that the as-developed sensor array has an extremely high dimensionality and, consequently, has the capacity to detect and discriminate a variety of OPs (e.g. chlorpyrifos, triazophos, phoxim, methamidophos and dimethoate) and carbamates (e.g. carbaryl, methomyl, metolcarb, isoprocarb and fenobucarb). Moreover, semiquantitative detections are also able to be achieved through combining HCA/PCA, recognition patterns and corresponding fitting curves. The unique features of this current method are the high selectivity and sensitivity, good anti-interference capability, simultaneous detection and identification each of OPs and carbamates. Above all, this array method shows potential applications for the detection of OPs and carbamates in real samples as well. ACKNOWLEDGMENTS The authors acknowledge the Natural Science Foundation of China (21277149), Zhejiang Provincial Natural Science Foundation of China (LR13B050001) and the Ningbo Science and Technology Bureau (2014B82010) for supporting this work.
SUPPORTING INFORMATION AVAILABLE Chemical structures of the commercially available indicators and pesticides; optimization of AChE concentrations; colorimetric difference maps; HCA; PCA; Figure S1 to S10; Tables S1-S3. This information is available free of charge via the Internet at http://pubs.acs.org.
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REFERENCES (1) Tankiewicz, M.; Fenik, J.; Biziuk, M. Trends Anal. Chem. 2010, 29, 1050–1063. (2) Eddleston, M.; Worek, F.; Eyer, P.; Thiermann, H.; von Meyer, L.; Jeganathan, K.; Sheriff, M. H. R.; Dawson, A. H.; Buckley, N. A. Q. J. Med. 2009, 102, 785–792. (3) Kamel, A.; Byrne, C.; Vigo, C.; Ferrario, J.; Stafford, C.; Verdin, G.; Siegelman, F.; Knizner, S.; Hetrick, J. Water Res. 2009, 43, 522–534. (4) Moffat, C. F.; Whittle, K. J. Environmental contaminants in food; Sheffield Academic Press: Sheffield, 1999. (5) http://www.epa.gov/pesticides/cumulative. (6) Kim, K.; Tsay, O. G.; Atwood, D. A.; Churchill, D. G. Chem. Rev. 2011, 111, 5345– 5403. (7) Aragay, G.; Pino, F.; Merkoçi, A. Chem. Rev. 2012, 112, 5317–5338. (8) Wang, J.; Chow, W.; Leung, D.; Chang, J. J. Agric. Food Chem. 2012, 60, 12088– 12104. (9) Kiso, Y.; Li, H.; Shigetoh, K.; Kitao, T.; Jinno, K. J. Chromatogr. A 1996, 733, 259– 265. (10) Gómez-Ramos, M. M.; Ferrer, C.; Malato, O.; Agüera, A.; Fernández-Alba, A. R. J. Chromatogr. A 2013, 1287, 24– 37. (11) Viswanathan, S.; Radecka, H.; Radecki, J. Biosens. Bioelectron. 2009, 24, 2772−2777. (12) Liu, G. D.; Lin, Y. H. Anal. Chem. 2005, 77, 5894−5901. (13) Gong, J. M.; Guan, Z. Q.; Song, D. D. Biosens. Bioelectron. 2013, 39, 320−323. (14) Llorent-Martinez, E. J.; Delgado-Blanca, I.; Ruiz-Medina, A.; Ortega-Barrales, P. Talanta 2013, 115, 462−467. (15) Qian, G. L.; Wang, L. M.; Wu, Y. R.; Zhang, Q.; Sun, Q.; Liu, Y.; Liu, F. Q. Food Chem. 2009, 117, 364−370. (16) Gabaldon, J. A.; Maquieira, A.; Puchades, R. Talanta 2007, 71, 1001–1010. (17) Fu, G. L.; Chen, W. W.; Yue, X. L.; Jiang, X. Y. Talanta 2013, 103, 110–115. (18) Sun, J. F.; Guo, L.; Bao, Y.; Xie, J. W. Biosens. Bioelectron. 2011, 28, 152−157. 17 ACS Paragon Plus Environment
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