Method for distinguishing and detecting pesticide based on copper-platinum bimetallic nano-enzyme

Abstract

The invention is suitable for the technical field of pesticide residue detection, and provides a method for distinguishing and detecting pesticide based on copper-platinum bimetallic nano-enzyme. According to the method, a colorimetric sensing array with three sensing channels is constructed by synthesizing a copper-platinum bimetallic nano-enzyme (CuPtNPs) with oxidase-like activity, laccase-like activity and superoxide dismutase-like activity. A fingerprint spectrum is obtained based on specific effects of different pesticides and enzyme-like activities, and qualitative distinguishing and quantitative detection of various pesticides are realized by combining a machine algorithm. According to the method, nine pesticide residues can be measured at a time, six of the nine pesticide residues can be quantitatively analyzed, and the problem that a traditional method only can be used for single detection is solved. A strategy of constructing three channels based on a single nano-enzyme receptor CuPtNPs has the remarkable advantages of simple synthesis process, convenient operation flow and low detection cost, is good in applicability in actual agricultural product samples such as Chinese cabbages and apples, and provides an efficient solution for high-throughput detection of pesticide residues.

Description

Technical Field

[0001] The present invention belongs to the technical field of pesticide residue detection, and in particular relates to a method for distinguishing and detecting pesticides based on copper-platinum bimetallic nanoenzymes. Background Art

[0002] Pesticides, as crucial chemical inputs in agricultural production, play an indispensable role in integrated crop pest management systems. Their primary functions include effectively controlling pests, inhibiting weed growth, and defending against fungal and other harmful organisms, thereby ensuring the normal growth and development of crops and achieving stable and increased grain production. However, pesticide residues pose a dual threat to human health and the ecological environment. Therefore, establishing an efficient, accurate, and sensitive pesticide residue detection technology system has become an urgent task. Timely and accurate detection of pesticide residue levels in agricultural products and the environment can provide data support for scientific pesticide use in agricultural production, effectively reduce the potential risks of pesticide residues to human health and the ecological environment, and have important practical significance for ensuring food safety, maintaining ecological balance, and promoting sustainable agricultural development.

[0003] Although traditional pesticide detection methods (such as gas chromatography-mass spectrometry, high-performance liquid chromatography, and enzyme-linked immunosorbent assay) have high accuracy and sensitivity, they have many shortcomings. Among them, the problem of single detection type is particularly prominent. These methods can usually only detect a certain type or a certain class of specific pesticides. When it is necessary to detect multiple different types of pesticides, it is often necessary to use a variety of different instruments and detection methods, which makes the operation cumbersome and greatly increases the detection cost and time. In order to solve the problem of single detection type, array sensor technology came into being. This technology constructs multiple sensing channels, uses the channels to work together, and simultaneously collects the differences between multiple response signals to achieve simultaneous identification of multiple pesticide residues. It belongs to the category of high-throughput detection. In the design process of array sensors, it is necessary to integrate multiple sensing channels that are responsive to pesticide residues. With the synergistic effect of these sensing channels, the array sensor can produce specific response differences to different pesticide residues. When multiple sensing channels in the array sensor interact with pesticides, a series of complex response signals are generated. After integration and processing, a "fingerprint spectrum" with unique characteristics is formed. Combined with machine algorithm analysis and comparison, quantitative and qualitative analysis of various pesticides can be performed. At present, based on the type of sensor channel output signal, sensor arrays for detecting pesticide residues are mainly divided into categories such as acoustic wave array sensors, electrochemical array sensors, and optical sensors. However, existing sensor arrays for pesticide detection generally have a multi-receptor problem, that is, it is necessary to synthesize multiple sensor monomers and construct multiple sensor channels, which leads to complex operation procedures and high costs. For this reason, the present invention proposes a method for distinguishing and detecting pesticides based on copper-platinum bimetallic nanoenzymes. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for distinguishing and detecting pesticides based on copper-platinum bimetallic nanoenzymes, aiming to solve the problems raised in the above background technology.

[0005] The purpose of the present invention is achieved through the following technical solutions:

[0006] A method for distinguishing and detecting pesticides based on copper-platinum bimetallic nanozymes comprises the following steps:

[0007] Preparation of copper-platinum bimetallic nanozymes, namely CuPtNPs, with oxidase-like, laccase-like, and superoxide dismutase-like activities;

[0008] Based on the oxidase-like activity, laccase-like activity, and superoxide dismutase-like activity of CuPtNPs, a three-channel sensing array was constructed;

[0009] The pesticide to be tested is interacted with a three-channel sensor array, and a pesticide fingerprint is formed by detecting the signal differences of each channel;

[0010] Machine algorithms are used to analyze pesticide fingerprints to achieve qualitative differentiation and quantitative detection of pesticides.

[0011] Furthermore, the synthesis method of the CuPtNPs includes:

[0012] At room temperature, anhydrous copper sulfate, chloroplatinic acid hexahydrate, and ascorbic acid are added to a polyvinyl pyrrolidone solution, mixed thoroughly, and allowed to stand. The supernatant is removed by centrifugation, the precipitate is washed and centrifuged, and finally the precipitate is collected, freeze-dried, and weighed to obtain CuPtNPs.

[0013] Furthermore, the specific composition of the three-channel sensor array is as follows:

[0014] Channel 1: CuPtNPs-based oxidase-like activity, containing acetate buffer, CuPtNPs, 3,3',5,5'-tetramethylbenzidine, and the pesticide to be tested;

[0015] Channel 2: Laccase-like activity based on CuPtNPs, containing morpholineethanesulfonic acid buffer, CuPtNPs, 2,4-dichlorophenol, 4-aminoantipyrine, and the pesticide to be tested;

[0016] Channel 3: Superoxide dismutase-like activity based on CuPtNPs, containing N-(2-hydroxyethyl)piperazine-N'-2-ethanesulfonic acid buffer, methionine, disodium EDTA, nitro blue tetrazolium chloride, riboflavin, CuPtNPs, and the pesticide to be tested.

[0017] Furthermore, the pesticides detectable by the method include but are not limited to methyl paraoxon, dimethoate, carbaryl, etoxazole, methomyl, isocarb, phosmet, pyridocarb, and methomyl.

[0018] Furthermore, the three-channel sensor array has a minimum qualitative differentiation concentration of 1 μg / mL for pesticides.

[0019] Furthermore, the machine algorithm includes hierarchical cluster analysis and linear discriminant analysis.

[0020] A three-channel sensor array for pesticide detection is constructed based on copper-platinum bimetallic nanozymes and includes three detection channels for oxidase-like activity, laccase-like activity, and superoxide dismutase-like activity, which are used to synchronously collect signal differences between different pesticides and nanozymes.

[0021] A copper-platinum bimetallic nanozyme is used in the preparation of a pesticide residue detection reagent. The copper-platinum bimetallic nanozyme has oxidase-like activity, laccase-like activity, and superoxide dismutase-like activity, and is used to construct a multi-channel sensing array to distinguish and detect pesticides.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] The present invention synthesizes a copper-platinum bimetallic nanozyme (CuPtNPs) with oxidase-like activity, laccase-like activity, and superoxide dismutase-like activity, and utilizes its three enzyme-like activities to construct a colorimetric sensor array with three sensing channels. Based on the specific interaction between different pesticides and enzyme-like activities, a fingerprint spectrum is obtained, and combined with machine algorithms such as linear discriminant analysis and hierarchical cluster analysis, qualitative differentiation and quantitative detection of multiple pesticides are achieved. This method can measure 9 pesticide residues at one time and perform quantitative analysis on 6 of them, breaking through the limitation that traditional detection methods can only target a single type of pesticide. The innovative strategy of constructing three channels based on a single nanozyme receptor CuPtNPs avoids the complexity of multi-receptor synthesis, and has the significant advantages of simple synthesis process, convenient operation process, and low detection cost. It has shown good applicability in actual agricultural product samples such as cabbage, apple, broccoli, etc., providing an efficient solution for high-throughput detection of pesticide residues. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 Schematic diagram of the synthesis process of CuPtNPs.

[0025] Figure 2 Schematic diagram of constructing a sensing array for CuPtNPs for detecting multiple pesticide residues.

[0026] Figure 3 Schematic diagram of the sensor array constructed for CuPtNPs combined with machine algorithms to achieve qualitative and quantitative analysis of multiple pesticides.

[0027] Figure 4 Characterization of the physical and chemical properties of CuPtNPs; among them, (a) is transmission electron microscopy, (b) is particle size distribution, (c) is EDS elemental analysis, and (d) is infrared spectroscopy.

[0028] Figure 5 These are the enzyme-like activities of CuPtNPs; (a) is the oxidase-like activity; (b) is the laccase-like activity; and (c) is the superoxide dismutase-like activity.

[0029] Figure 6 Absorption spectrum and actual colorimetry of the CuPtNPs-based sensor array in the presence of pesticides; (a) is the actual color state after the pesticide acts on the sensor array, (b) is the UV-visible absorption spectrum of the oxidase-like activity of CuPtNPs, (c) is the UV-visible absorption spectrum of the laccase-like activity of CuPtNPs, and (d) is the UV-visible absorption spectrum of the superoxide dismutase-like activity of CuPtNPs.

[0030] Figure 7 A sensing array was constructed for CuPtNPs to differentiate nine pesticides at concentrations of 1-100 μg / mL.

[0031] Figure 8 The classification ability of the CuPtNPs sensor array for mixed pesticides is constructed; among them, (a) is the linear discriminant analysis diagram of Pm:Iso; (b) is the linear relationship (Pm:Iso); (c) is the linear discriminant analysis diagram of random multivariate mixed pesticides.

[0032] Figure 9 A sensing array was constructed for CuPtNPs for the qualitative detection of six pesticides.

[0033] Figure 10 The detection performance of the sensing array constructed for CuPtNPs on actual samples; among them, (a) is cabbage; (b) is apple; (c) is broccoli. DETAILED DESCRIPTION

[0034] In order to have a clearer understanding of the technical features, objectives and beneficial effects of the present invention, the technical solution of the present invention is now described in detail below, but it should not be understood as limiting the scope of implementation of the present invention.

[0035] The specific implementation of the present invention is described in detail below with reference to specific embodiments.

[0036] Example 1: Preparation and characterization of copper-platinum bimetallic nanozymes (CuPtNPs);

[0037] 1.1 Preparation method;

[0038] In this example, copper-platinum bimetallic nanozymes (CuPtNPs) with multi-enzyme activity were synthesized using copper-platinum metal as precursor, ascorbic acid as reducing agent, and polyvinylpyrrolidone as stabilizer. The specific steps are as follows ( Figure 1 ):

[0039] At room temperature, anhydrous copper sulfate (4 mM, 0.1 mL), chloroplatinic acid hexahydrate (10 mM, 0.1 mL), and ascorbic acid (200 mM, 0.1 mL) were added to a polyvinyl pyrrolidone (1 mg / mL, 1 mL) solution, and the mixed solution was immediately mixed thoroughly with a vortex shaker and allowed to stand for 1 hour; the mixed solution was then transferred to a high-speed centrifuge and centrifuged at 15,000 r / min for 15 minutes. The supernatant was removed, and the resulting precipitate was washed with a mixture of ethanol and water (volume ratio 1:1) and centrifuged (15,000 r / min, 15 minutes), and the washing and centrifugation were repeated three times; finally, the precipitate was collected, freeze-dried, and weighed to obtain CuPtNPs.

[0040] 1.2 Structure and performance characterization;

[0041] like Figure 4 As shown in (a), CuPtNPs with a concentration of 60 μg / mL are dispersed on a carbon support film. The structure of CuPtNPs is observed using a JEM-2100F transmission electron microscope, and it can be seen that the CuPtNPs have an elliptical structure. Figure 4 (b) shows that the size of CuPtNPs is about 60nm. EDS elemental analysis results show that ( Figure 4 In (c), copper and platinum elements are evenly distributed on the surface of the nanoparticles. Infrared spectrum ( Figure 4 (d) 1085.5cm -1 The absorption peak is attributed to the stretching vibration of the carbon-oxygen-copper bond (CO-Cu), indicating that the copper ions in anhydrous copper sulfate are loaded onto CuPtNPs. These physical and chemical properties verify the successful synthesis of CuPtNPs.

[0042] Example 2: Construction and performance verification of a multi-pesticide detection method based on a three-channel sensor array;

[0043] 2.1 Sensing channel construction;

[0044] Based on the oxidase (OXD)-like activity, laccase (LAC)-like activity, and superoxide dismutase (SOD)-like activity of CuPtNPs, a sensing array with three sensing channels was constructed ( Figure 2), the detection principle is: there is a specific interaction between different pesticides and sensor channels, resulting in the specific signal differences of the three enzyme activities in the pesticide residue detection analysis. This signal difference is the pesticide fingerprint. Using this fingerprint, combined with a variety of machine algorithms such as hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA) for data analysis, qualitative and quantitative detection of pesticides can be achieved ( Figure 3 ). The specific channel composition is as follows:

[0045] Channel 1 (OXD-like): consists of 70 μL acetate buffer (pH = 4.5), 10 μL CuPtNPs (60 μg / mL), 10 μL 3,3',5,5'-tetramethylbenzidine (10 mM), and 10 μL of the pesticide to be tested.

[0046] Channel 2 (LAC-like): consists of 60 μL of morpholineethanesulfonic acid buffer (60 mM, pH = 6.5), 10 μL of CuPtNPs (240 μg / mL), 10 μL of 2,4-dichlorophenol (2.5 mg / mL), 10 μL of 4-aminoantipyrine (2.5 mg / mL), and 10 μL of the pesticide to be tested.

[0047] Channel 3 (SOD-like): consists of 35 μL N-(2-hydroxyethyl)piperazine-N'-2-ethanesulfonic acid buffer (65 mM, pH = 7), 10 μL methionine (130 mM), 10 μL disodium ethylenediaminetetraacetic acid (100 μM), 20 μL nitro blue tetrazolium chloride (750 μM), 5 μL riboflavin (20 μM), 10 μL CuPtNPs (0.6 μg / mL), and 10 μL of the pesticide to be tested.

[0048] from Figure 5 It can be seen that CuPtNPs particles have three types of enzyme activities; among them, Figure 5 (a) shows that CuPtNPs particles have oxidase-like activity and can oxidize 3,3',5,5'-tetramethylbenzidine (TMB) in the presence of oxygen, with an ultraviolet absorption peak at 652nm in the ultraviolet absorption spectrum. Figure 5 (b) shows that CuPtNPs particles have laccase-like activity. When CuPtNPs are added in the presence of only 2,4-dichlorophenol (2,4-DP) and 4-aminoantipyrine (4-AP), there is a clear absorption peak at 510 nm in the UV absorption spectrum. Figure 5 (c) shows that when CuPtNPs exist in the reaction system, the absorption peak at 560nm of the UV absorption spectrum is significantly reduced, proving that CuPtNPs have superoxide dismutase-like activity.

[0049] 2.2 Detection performance verification;

[0050] (1) Qualitative differentiation feasibility verification;

[0051] Based on the oxidase (OXD)-like activity, laccase (LAC)-like activity, and superoxide dismutase (SOD)-like activity of CuPtNPs, colorimetric sensing array channels 1, 2, and 3 were constructed respectively, and a sensing array with three sensing channels was formed. The feasibility of qualitatively distinguishing a variety of pesticides was verified through visualized color response and ultraviolet absorption spectrum analysis.

[0052] A total of 9 pesticides, including methyl paraoxon (Pm), dimethoate (Dim), carbaryl (Car), etoxazole (Eto), methomyl (Mtmc), isocarbophos (Iso), phosmet (Pho), pyridocarb (Pir), and methomyl (Met), were exposed to the three-channel sensor array at 40 μg / mL, and then photographed and observed under natural light ( Figure 6 In (a), it can be seen that different pesticides show significant color differences after interacting with each channel, indicating that different pesticides have different effects on the enzyme-like activity of CuPtNPs particles. Since different pesticides have different effects on enzyme-like activity, resulting in different colorimetric responses, the absorbance of these three channels can be measured by ultraviolet absorption spectroscopy. The nine pesticides were incubated with the three sensing channels for a period of time and then reacted. After a period of reaction, the absorbance was measured by ultraviolet absorption spectroscopy. The results are shown in Figure 1. Figure 6 As shown in (b)-(d), 9 pesticides at 20 μg / mL each produced absorption peaks at wavelengths of 652 nm (channel 1), 510 nm (channel 2), and 560 nm (channel 3), and the effects of different pesticides on the absorption peaks at 652 nm, 510 nm, and 560 nm were significantly different. The same pesticide had different effects on the absorption peaks at these three different wavelengths, showing a complex specific interaction relationship between pesticide types and absorption peaks, confirming the feasibility of constructing a three-channel sensing array using the three enzymatic activities of CuPtNPs to detect pesticides.

[0053] (2) Qualitative analysis;

[0054] A three-channel sensor array was used to qualitatively analyze nine pesticides, including 1-100 μg / mL methyl paraoxon (Pm), dimethoate (Dim), carbaryl (Car), etoxazole (Eto), methomyl (Mtmc), isocarbophos (Iso), phosmet (Pho), pyridocarb (Pir), and methomyl (Met). The data were analyzed using a machine algorithm for linear discriminant analysis. The results showed that ( Figure 7), all pesticide samples formed nine independent, non-overlapping regions within the Factor 1-Factor 2 two-dimensional spatial coordinate plane, with clearly discernible class boundaries. This result demonstrates that the sensor array constructed based on the three enzymatic activities of CuPtNPs can generate highly specific response signals, forming characteristic fingerprints for different pesticides, effectively overcoming the limitation of traditional pesticide detection methods that can only detect a single type of analyte. The sensor array demonstrated excellent sensitivity and specificity, achieving a minimum qualitative discrimination concentration of 1 μg / mL for all nine pesticides.

[0055] (3) identification of mixed systems;

[0056] In view of the current status of common pesticide combinations in agricultural production, the application potential of a three-channel sensor array in complex mixture systems was evaluated. Methyl paraoxon (Pm) and isocarbophos (Iso), both structurally similar, were selected as research objects. Binary pesticide mixtures of varying proportions were prepared. Nine mixtures, including four binary pesticide mixtures (Car:Met = 8:2, Mtmc:Pho = 7:3, Met:Iso = 5:5, Eto:Dim = 5:5), two ternary pesticide mixtures (Car:Dim:Pm = 4:2:4, Car:Mtmc:Eto = 6:2:2), and three quaternary pesticide mixtures (Car:Dim:Eto:Pir = 3:3:2:2, Car:Met:Pho:Iso = 1:1:4:4, Car:Dim:Mtmc:Pm = 4:4:1:1) with varying molar ratios, were randomly selected, with a total concentration of 20 μg / mL. The recognition ability of the sensor array for mixtures with similar structures was evaluated using machine algorithm linear discriminant analysis. The results showed that ( Figure 8 In (a)-(c)), binary, ternary, and quaternary pesticide mixtures of all proportions form independent regions. These clear regions with independent boundaries indicate that the sensor array can distinguish binary, ternary, and quaternary pesticide mixtures.

[0057] (4) Quantitative analysis;

[0058] On the basis of verifying the qualitative identification ability of the three-channel colorimetric sensor array, its quantitative analysis performance was further systematically evaluated. Six pesticides, including methyl paraoxon (Pm), dimethoate (Dim), carbaryl (Car), etoxazole (Eto), methomyl (Mtmc), and methomyl (Met), were selected to construct a 1-100 μg / mL concentration gradient detection system (6 concentration points), and the pesticide concentration-response relationship model was established. The results showed that ( Figure 9The linear detection ranges for the six pesticides were Car (5-80 μg / mL), Dim and Pm (1-100 μg / mL), Met and Mtmc (5-100 μg / mL), and Eto (1-40 μg / mL). These results demonstrate that the sensor array has good linear correlation for the quantitative detection of different pesticides, covering the common pesticide residue concentration range.

[0059] (5) Actual sample verification;

[0060] In order to verify the applicability of the three-channel sensor array in real scenes, the field spraying situation was simulated, and three fruits and vegetables that are prone to pesticide residues, namely cabbage, apple, and broccoli, were selected as samples. A total of nine pesticides, including methyl paraoxon (Pm), dimethoate (Dim), carbaryl (Car), etoxazole (Eto), methomyl (Mtmc), isocarbophos (Iso), phosmet (Pho), pyridocarb (Pir), and methomyl (Met), were selected for actual sample analysis. Solutions containing different types of pesticides were extracted and filtered through a 0.22μm filter membrane as test samples for detection by the sensor array of the present invention. Machine algorithm linear discriminant analysis was used in the detection of actual samples, and the results showed that ( Figure 10 ), the nine pesticide samples were divided into nine separate, isolated regions within the discrimination space, demonstrating that the sensor array can effectively distinguish different pesticide residues in actual agricultural products. This result confirms that the CuPtNPs sensor array maintains high specificity and accuracy in complex matrix environments, capable of broad-spectrum pesticide analysis, and provides reliable technical support for pesticide residue detection in practical applications.

[0061] The above are only preferred embodiments of the present invention. It should be pointed out that for those skilled in the art, several variations and improvements can be made without departing from the concept of the present invention. These should also be regarded as the scope of protection of the present invention. These will not affect the effect of the implementation of the present invention and the practicality of the patent.

Claims

1. A method for distinguishing and detecting pesticides based on copper-platinum bimetallic nanozymes, characterized in that: The following steps are involved: Preparation of copper-platinum bimetallic nanozymes, namely CuPtNPs, with oxidase-like, laccase-like, and superoxide dismutase-like activities; Based on the oxidase-like activity, laccase-like activity, and superoxide dismutase-like activity of CuPtNPs, a three-channel sensing array was constructed; The pesticide to be tested is interacted with a three-channel sensor array, and a pesticide fingerprint is formed by detecting the signal differences of each channel; Machine algorithms are used to analyze pesticide fingerprints to achieve qualitative differentiation and quantitative detection of pesticides.

2. The method according to claim 1, characterized in that The synthesis method of the CuPtNPs comprises: At room temperature, anhydrous copper sulfate, chloroplatinic acid hexahydrate, and ascorbic acid are added to a polyvinyl pyrrolidone solution, mixed thoroughly, and allowed to stand. The supernatant is removed by centrifugation, the precipitate is washed and centrifuged, and finally the precipitate is collected, freeze-dried, and weighed to obtain CuPtNPs.

3. The method according to claim 1, characterized in that The specific composition of the three-channel sensor array is as follows: Channel 1: CuPtNPs-based oxidase-like activity, containing acetate buffer, CuPtNPs, 3,3',5,5'-tetramethylbenzidine, and the pesticide to be tested; Channel 2: Laccase-like activity based on CuPtNPs, containing morpholineethanesulfonic acid buffer, CuPtNPs, 2,4-dichlorophenol, 4-aminoantipyrine, and the pesticide to be tested; Channel 3: Superoxide dismutase-like activity based on CuPtNPs, containing N-(2-hydroxyethyl)piperazine-N'-2-ethanesulfonic acid buffer, methionine, disodium EDTA, nitro blue tetrazolium chloride, riboflavin, CuPtNPs, and the pesticide to be tested.

4. The method according to claim 1, wherein The pesticides detectable by the method include, but are not limited to, methyl paraoxon, dimethoate, carbaryl, etoxazole, methomyl, isocarb, phosmet, pyridocarb, and methomyl.

5. The method according to claim 1, wherein The lowest qualitative differentiation concentration of the three-channel sensor array for pesticides is 1 μg / mL.

6. The method according to claim 1, characterized in that The machine algorithms include hierarchical cluster analysis and linear discriminant analysis.

7. A three-channel sensor array for pesticide detection, characterized in that: The sensor array is constructed based on copper-platinum bimetallic nanozymes and includes three detection channels for oxidase-like activity, laccase-like activity, and superoxide dismutase-like activity, which are used to synchronously collect signal differences between different pesticides and nanozymes.

8. Application of a copper-platinum bimetallic nanozyme in the preparation of a pesticide residue detection reagent, characterized in that: The copper-platinum bimetallic nanozyme has oxidase-like activity, laccase-like activity, and superoxide dismutase-like activity, and is used to construct a multi-channel sensing array to distinguish and detect pesticides.

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