A method for detecting multiple pesticide residues based on Cu@MOF-808-NH2 nanosensor array

Abstract

The application belongs to the field of food safety detection, and relates to a kind of multi-pesticide residue detection method based on Cu@MOF-808-NH2 nanoenzyme sensing array.The application first synthesizes Cu@MOF-808-NH2 nanoenzyme with phosphatase-like, laccase-like, peroxidase-like activity and fluorescence characteristics by hydrothermal method; then, the optimal reaction conditions of each enzymatic activity are systematically optimized, and a four-channel sensor array detection system is constructed; the application also comprehensively utilizes PCA method and HCA method to reduce the dimension of multi-dimensional response data, realizes cross-validation and qualitative identification of different types of pesticides through visual clustering and tree diagram analysis. Further, by analyzing the quantitative relationship between the response signal value of a specific channel and the pesticide concentration, a linear regression equation is constructed, and the precise quantification of the specific pesticide residue concentration in the sample under test is realized, which has the advantages of simple operation, good stability, strong anti-interference ability, wide applicability and the like.

Description

Technical Field

[0001] This invention belongs to the field of food safety testing. In particular, this invention relates to a method for detecting multiple pesticide residues based on Cu@MOF-808-NH2 nanoenzyme sensing array. Background Technology

[0002] Pesticide residues are a significant threat to food safety and environmental pollution. Currently, common pesticide detection methods on the market mainly include chromatography (such as GC-MS and LC-MS), immunoassay, and enzyme inhibition methods. While chromatography offers high sensitivity and accurate results, the equipment is expensive, the operation is complex, and the pretreatment is cumbersome, making it difficult to meet the needs of rapid on-site screening. Immunoassay has high specificity, but an antibody typically targets only one type or class of pesticides, resulting in high development costs and susceptibility to matrix interference.

[0003] Enzyme inhibition methods, particularly those based on acetylcholinesterase (AChE), are widely used for the rapid detection of organophosphorus (OPs) and carbamate (CPs) pesticides due to their simplicity and low cost. However, these methods have significant limitations: First, their recognition range is narrow, limited to OPs and CPs that inhibit AChE activity, and they are ineffective for many other pesticides such as organochlorines, sulfonylureas, and phenoxyazines. Second, these methods can only reflect the total toxic equivalent of pesticides through the degree of enzyme activity inhibition, and cannot qualitatively distinguish coexisting pesticides or quantitatively analyze their individual concentrations, easily leading to false positives or false negatives.

[0004] Sensor arrays offer a novel approach to simultaneous multi-component detection. They utilize a set of cross-reactive sensing units to respond to the analyte, generating multidimensional signals. These signals are then analyzed using pattern recognition algorithms to interpret the "fingerprint," thereby enabling analyte identification. In recent years, nanomaterials with enzyme-like catalytic activity (nanozymes) have become ideal sensing units for constructing sensor arrays due to their high stability, low cost, and ease of modification. However, existing research often focuses on utilizing the single activity of nanozymes (such as peroxidase-like activity) or requires combining multiple nanomaterials to construct the array, resulting in complex systems and difficulties in ensuring stability. Data analysis often relies on single algorithms, lacking the rigor of cross-validation between multiple algorithms. Therefore, developing a new method for constructing a multifunctional sensor array based on a single nanozyme, capable of simply, rapidly, and accurately distinguishing multiple different types of pesticides, effectively coping with complex matrix interference, and employing multiple chemometric methods for cross-validation, has significant practical implications and application value. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies, such as single detection and weak anti-interference ability. This invention provides a multi-channel sensing array based on a single material Cu@MOF-808-NH2 nanozyme for rapid differentiation and identification of various types of pesticides, thus solving the related defects in existing technologies.

[0006] Specifically, the present invention adopts the following technical solution:

[0007] In one aspect, this invention discloses a method for detecting multiple pesticide residues based on a Cu@MOF-808-NH2 nanozyme sensing array, comprising the following steps:

[0008] (1) Sensing signal acquisition: The sample to be tested is reacted with Cu@MOF-808-NH2 nanozyme under preset conditions, and its response signals in the four channels of phosphatase-like activity channel, laccase-like activity channel, peroxidase-like activity channel and fluorescence channel are detected respectively to obtain the multidimensional response data vector corresponding to the sample.

[0009] (2) Chemometric model establishment and analysis: Using a variety of known pesticide standards, repeat step (1) to obtain the training set data matrix. Use principal component analysis (PCA) and hierarchical cluster analysis (HCA) to perform dimensionality reduction and analysis on the training set data matrix to obtain PCA score map and HCA dendrogram that can intuitively distinguish different pesticide types. In addition, construct standard curves of response signal values ​​and pesticide standard concentrations under four channels for different samples.

[0010] (3) Analysis of the test sample: The multidimensional response data vector of the test sample obtained in step (1) is projected into the PCA model space established in step (2), and HCA analysis is performed based on its Euclidean distance with the training set data. The position of the test sample in the PCA score map and the clustering situation in the HCA tree map are compared with the clustering regions of known pesticides to achieve qualitative identification of pesticide types in the test sample. In addition, the response values ​​of the test sample in different channels are substituted into the constructed standard curve to obtain the concentration value of the test sample, so as to achieve quantitative analysis of pesticides in the test sample.

[0011] In one aspect, the present invention discloses a method for preparing Cu@MOF-808-NH2 nanozymes, the method comprising:

[0012] (1) Synthesis of MOF-808-NH2: A mixture of pyromellitic acid, 2-aminoterephthalic acid and zirconium oxychloride octahydrate was dispersed in a mixture of N,N-dimethylformamide and formic acid (V:V=1:1). The mixture was sonicated for a period of time, and the solution was transferred to a reaction vessel and placed in an oven for continuous heating. After the reaction was completed, the reactants were naturally cooled to room temperature. The resulting mixture was washed three times by centrifugation with N,N-dimethylformamide and acetone, and then dried in an oven to obtain MOF-808-NH2.

[0013] (2) Synthesis of Cu@MOF-808-NH2: Copper acetate monohydrate and MOF-808-NH2 prepared in step (1) were mixed and dispersed in N,N-dimethylformamide, acetic acid was added, and ultrasonic dispersion was performed. The dispersed solution was transferred to a reaction vessel and heated in an oven. After the reaction was completed, the reactants were naturally cooled to room temperature. The resulting mixture was washed three times by centrifugation with methanol and acetone, and finally dried in an oven to obtain Cu@MOF-808-NH2.

[0014] In one embodiment, the present invention discloses a method for preparing Cu@MOF-808-NH2 nanozymes, the method comprising:

[0015] (1) Synthesis of MOF-808-NH2: Weigh 0.1175 g of trimesic acid, 0.1014 g of 2-aminoterephthalic acid and 0.54 g of zirconium oxychloride octahydrate and disperse them in 30 mL of a mixture of N,N-dimethylformamide and formic acid (V:V=1:1). Sonicate for 15 min and then transfer the above solution to a reaction vessel. Place it in an oven at 120 ℃ and heat for 48 h. After the reaction is completed, cool the reactants to room temperature naturally. Wash the resulting mixture three times by centrifugation at 10000.0 rpm with N,N-dimethylformamide and acetone respectively. Finally, dry it in an oven at 60 ℃ for 6.0 h.

[0016] (2) Synthesis of Cu@MOF-808-NH2: Weigh 0.1039 g of copper acetate monohydrate and 0.05 g of MOF-808-NH2, disperse them in 15 mL of N,N-dimethylformamide, add 2.5 mL of acetic acid, sonicate for 15 min, then transfer the above solution to a reaction vessel, place it in an oven at 120 ℃, and heat for 12 h. After the reaction is completed, cool the reactants to room temperature naturally. The resulting mixture is washed three times by centrifugation with methanol and acetone at 10000.0 rpm, and finally dried in an oven at 60 ℃ for 6.0 h.

[0017] In one aspect, the present invention discloses the application of Cu@MOF-808-NH2 nanozyme in the detection of multiple pesticide residues, wherein the application involves using Cu@MOF-808-NH2 nanozyme to form a sensor array for the detection of multiple pesticide residues.

[0018] In this invention, the Cu@MOF-808-NH2 nanozyme synthesized by this invention has detectable characteristics in four channels: phosphatase-like active channel, laccase-like active channel, peroxidase-like active channel and fluorescence channel. Under preset conditions, it can be used to detect the sample to be tested. The four channels exhibit different signal characteristics or signal intensities for different sample to be tested or different concentrations of the same sample, so that they can be observed by the detection device.

[0019] In this invention, the phosphatase-like active channel uses disodium p-nitrophenyl phosphate (p-NPP) as a substrate, and the absorbance value is detected at a wavelength of 400 nm under reaction conditions of pH 9.5 and 45 °C.

[0020] In this invention, the laccase-like active channel uses 2,4-dichlorophenol (2,4-DP) and 4-aminoantipyrine (4-AP) as substrates, and the absorbance value is detected at a wavelength of 510 nm under reaction conditions of pH 7.5 and 55 ℃.

[0021] In this invention, the peroxidase-like active channel uses 3,3',5,5'-tetramethylbenzidine (TMB) and hydrogen peroxide (H2O2) as substrates, and the absorbance value is detected at a wavelength of 652 nm under reaction conditions of pH 4.0 and 40 °C.

[0022] In this invention, the fluorescence channel detects the fluorescence intensity value at an emission wavelength of 425 nm under an excitation wavelength of 325 nm.

[0023] In one embodiment, the aforementioned method for detecting multiple pesticide residues based on a Cu@MOF-808-NH2 nanozyme sensing array further includes a quantitative detection step, which comprises:

[0024] (a) For the pesticide species to be quantified, select one or more detection channels with the most significant response;

[0025] (b) Prepare a series of pesticide standard solutions of different concentrations and measure their response signal values ​​in the selected channels according to the method in step (1);

[0026] (c) Plot a standard curve with pesticide concentration on the x-axis and response signal value on the y-axis, and fit the linear regression equation y = aC + b, where y is the signal value, C is the pesticide concentration, a is the slope, and b is the intercept.

[0027] (d) The signal value y of the sample to be tested measured in the corresponding channel in step (1) n Substituting into the linear regression equation, the concentration C of the pesticide can be calculated. n .

[0028] In one embodiment, the pesticides that the present invention can detect include organophosphates, carbamates, organochlorines, sulfonylureas, and phenoxyazines; preferably, the organophosphate pesticide is glyphosate (Gly), the carbamate pesticide is pirimicarb (Pir) and methomyl (Mtmc), the organochlorine pesticide is chlorothalonil (Cht), the sulfonylurea pesticide is metsulfuron-methyl (Met), and the phenoxyazine pesticide is etoxazole (Eto).

[0029] In one aspect, the present invention discloses a kit for detecting multiple pesticide residues, the kit comprising Cu@MOF-808-NH2 nanozyme.

[0030] In one embodiment, the kit further includes: disodium nitrophenyl phosphate (p-NPP), 2,4-dichlorophenol (2,4-DP), 4-aminoantipyrine (4-AP), 3,3',5,5'-tetramethylbenzidine (TMB), hydrogen peroxide (H2O2), and buffer solution.

[0031] In one embodiment, the buffer in the kit is NEM buffer, Tris-HCl buffer, or NaAC buffer. Beneficial effects

[0032] 1. Multiple benefits from a single material, easy to construct: By utilizing the four catalytic activities and fluorescence properties of the single nanomaterial Cu@MOF-808-NH2, a four-channel sensor array was successfully constructed, avoiding the complexity, high cost and instability issues caused by using multiple natural enzymes or nanomaterials.

[0033] 2. Wide range of identification, overcoming limitations: This method can effectively distinguish between a variety of pesticides with different chemical structures, such as organophosphates, carbamates, organochlorines, sulfonylureas, and phenoxyzoline, greatly expanding the detection range.

[0034] 3. Dual algorithm verification for more reliable results: The system innovatively combines two chemometric methods: Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA). PCA provides intuitive visualizations of clustering diagrams, while HCA provides objective dendrograms of clustering relationships. These two methods complement and verify each other, significantly improving the accuracy and reliability of pesticide identification results.

[0035] 4. Integrated detection, highly practical: The most prominent advantage of this invention's method lies in integrating "identification" and "quantification" functions into one. A single test can complete the entire process from pesticide type identification to residue concentration calculation, effectively solving the pain points of traditional methods that are fragmented and unable to address both aspects simultaneously, providing strong technical support for rapid on-site detection. Attached Figure Description

[0036] Figure 1 This is a schematic diagram illustrating the synthesis and detection principle of Cu@MOF-808-NH2 nanozymes.

[0037] Figure 2 These are transmission electron microscope (TEM) images and elemental distribution diagrams of Cu@MOF-808-NH2 nanozymes.

[0038] Figure 3 These are the optimized curves of four enzymatic activities of Cu@MOF-808-NH2 nanozymes: (A) pH optimization curve for phosphatase-like activity; (B) pH optimization curve for laccase-like activity; (C) pH optimization curve for peroxidase-like activity; (D) Temperature optimization curve for phosphatase-like activity; (E) Temperature optimization curve for laccase-like activity; (F) Temperature optimization curve for peroxidase-like activity.

[0039] Figure 4 The following are standardized response pattern diagrams of different pesticides at 5 ppm under a four-channel sensor array: (A) Two-dimensional score plot of principal component analysis (PCA); (B) Heatmap; (C) Fingerprint.

[0040] Figure 5 The following are standardized response pattern diagrams of different pesticides at 25 ppm under a four-channel sensor array: (A) Two-dimensional score plot of principal component analysis (PCA); (B) Heatmap; (C) Fingerprint.

[0041] Figure 6 The following are standardized response pattern diagrams of different pesticides at 50 ppm under a four-channel sensor array: (A) Two-dimensional score plot of principal component analysis (PCA); (B) Heatmap; (C) Fingerprint.

[0042] Figure 7 The following are standardized response pattern diagrams of different types of pesticides at 100 ppm under a four-channel sensor array: (A) Two-dimensional score plot of principal component analysis (PCA); (B) Heatmap; (C) Fingerprint.

[0043] Figure 8 This is a hierarchical clustering analysis diagram of 5 ppm of different types of pesticides in a four-channel sensor array.

[0044] Figure 9 This is a hierarchical clustering analysis diagram of 25 ppm of different types of pesticides in a four-channel sensor array.

[0045] Figure 10 This is a hierarchical clustering analysis diagram of 50 ppm of different types of pesticides in a four-channel sensor array.

[0046] Figure 11 This is a hierarchical clustering analysis diagram of 100 ppm of different types of pesticides on a four-channel sensor array.

[0047] Figure 12 The graphs show the relationship between different concentrations of glyphosate and the sensor signal: (A) Two-dimensional score graph of principal component analysis (PCA); (B) Radar graph; (C) Heatmap.

[0048] Figure 13 The graphs show the relationship between different concentrations of pirimicarb and sensor signals: (A) Two-dimensional score graph of principal component analysis (PCA); (B) Radar graph; (C) Heatmap.

[0049] Figure 14 The graphs show the relationship between different concentrations of velocicarb and sensor signals: (A) Two-dimensional score graph of principal component analysis (PCA); (B) Radar graph; (C) Heat map.

[0050] Figure 15 The graphs show the relationship between different concentrations of chlorothalonil and sensor signals: (A) Two-dimensional score graph of principal component analysis (PCA); (B) Radar graph; (C) Heatmap.

[0051] Figure 16 The following are graphs showing the relationship between different concentrations of mesosulfuron and sensor signals: (A) Two-dimensional score graph of principal component analysis (PCA); (B) Radar graph; (C) Heatmap.

[0052] Figure 17 The graphs show the relationship between different concentrations of etoxazole and sensor signals: (A) Two-dimensional score graph of principal component analysis (PCA); (B) Radar graph; (C) Heatmap.

[0053] Figure 18 The linear relationships between glyphosate concentration and sensor signal are: (A) linear relationship between phosphatase channel and glyphosate concentration; (B) linear relationship between laccase channel and glyphosate concentration; (C) linear relationship between fluorescence channel and glyphosate concentration.

[0054] Figure 19 The linear relationships between pirimicarb concentration and sensor signal are: (A) linear relationship between laccase channel and pirimicarb concentration; (B) linear relationship between fluorescence channel and pirimicarb concentration.

[0055] Figure 20 The linear relationships between semacarb concentration and sensor signal are: (A) linear relationship between laccase channel and semacarb concentration; (B) linear relationship between fluorescence channel and semacarb concentration.

[0056] Figure 21 The linear relationships between chlorothalonil concentration and sensor signal are: (A) linear relationship between laccase channel and chlorothalonil concentration; (B) linear relationship between fluorescence channel and chlorothalonil concentration.

[0057] Figure 22 The linear relationship between mesosulfuron concentration and sensor signal is: (A) Linear relationship between fluorescence channel and mesosulfuron concentration.

[0058] Figure 23 The linear relationship between etoxazole concentration and sensor signal is shown in Figure (A): Linear relationship between fluorescence channel and etoxazole concentration.

[0059] Figure 24 The selective verification of sensor array detection of interfering substances in complex environments is shown in the following diagrams: (A) bar chart; (B) two-dimensional score plot of principal component analysis (PCA).

[0060] Figure 25 This is a two-dimensional score plot of the principal component analysis (PCA) of spiked samples in tea.

[0061] Figure 26 This is a dendrogram of hierarchical cluster analysis (HCA) of spiked samples in tea. Detailed Implementation

[0062] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Please refer to... Figure 1-26 It should be noted that the structures, ratios, sizes, etc., illustrated in the accompanying drawings are merely for illustrative purposes to aid those skilled in the art and are not intended to limit the scope of the invention. Therefore, they have no substantial technical significance, and any modifications to the structure, changes in ratios, or adjustments to size are not permitted. The following embodiments are provided to better understand the invention, but are not intended to limit it. Unless otherwise specified, the experimental methods used in the following embodiments are conventional methods. Unless otherwise specified, the experimental materials used in the following embodiments were purchased from conventional biochemical reagent stores.

[0063] Relevant reagents:

[0064] The raw materials used in this invention are: disodium p-nitrophenyl phosphate (p-NPP), 2,4-dichlorophenol (2,4-DP), 4-aminoantipyrine (4-AP), 3,3',5,5'-tetramethylbenzidine (TMB), hydrogen peroxide (H2O2), trimesic acid, 2-aminoterephthalic acid, zirconium oxychloride octahydrate (ZrOCl2·8H2O), N,N-dimethylformamide (DMF), formic acid, acetone, copper acetate monohydrate (Cu(CH3COO)2·H2O), acetic acid, and methanol, which were purchased from Shanghai Maclean Biochemical Technology Co., Ltd.; acetate-sodium acetate buffer, Tris-HCl buffer, and N-ethylmorpholine buffer (NEM) were purchased from Shanghai Yuanye Biotechnology Co., Ltd. All chemicals were purchased directly from suppliers without further purification; ultrapure water was used in the experiments.

[0065] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments, but the scope of protection of the present invention is not limited thereto.

[0066] Example 1: Synthesis and Characterization of Cu@MOF-808-NH2 Nanozymes

[0067] The Cu@MOF-808-NH2 nanozyme of this invention is prepared by the following method:

[0068] (1) Synthesis of MOF-808-NH2: Weigh 0.1175 g of trimesic acid, 0.1014 g of 2-aminoterephthalic acid and 0.54 g of zirconium oxychloride octahydrate and disperse them in a mixture of 30.0 mL of N,N-dimethylformamide and formic acid (V:V=1:1). Sonicate for 15.0 min. Then transfer the above solution to a reaction vessel and place it in an oven at 120.0 °C for 48.0 h. After the reaction is completed, the reactants are naturally cooled to room temperature. The resulting mixture is washed three times by centrifugation at 10000.0 rpm with N,N-dimethylformamide and acetone, respectively. Finally, it is dried in an oven at 60.0 °C for 6.0 h to obtain MOF-808-NH2.

[0069] (2) Synthesis of Cu@MOF-808-NH2: Weigh 0.1039 g of copper acetate monohydrate and 0.05 g of MOF-808-NH2, disperse them in 15.0 mL of N,N-dimethylformamide, add 2.5 mL of acetic acid, sonicate for 15.0 min, then transfer the above solution to a reaction vessel, place it in an oven at 120.0 °C, and heat for 12.0 h. After the reaction is completed, cool the reactants to room temperature naturally. The resulting mixture is washed three times by centrifugation with methanol and acetone at 10000.0 rpm, and finally dried in an oven at 60.0 °C for 6.0 h to obtain Cu@MOF-808-NH2.

[0070] The morphology of the prepared nanozymes was characterized using transmission electron microscopy (TEM), and the results are as follows: Figure 2 As shown, Cu nanoparticles are uniformly distributed on the MOF-808-NH2 support, with no obvious aggregation. The elemental mapping further confirms the uniform distribution of C, N, O, Zr, and Cu elements in the material, successfully demonstrating the synthesis of Cu@MOF-808-NH2 nanozymes.

[0071] Example 2: Cu@MOF-808-NH2 nanozyme multi-enzyme activity detection system and condition optimization

[0072] 2.1 Establishment of the testing system

[0073] Phosphatase-like activity: A mixture of Cu@MOF-808-NH2 and the analyte was added to NEM buffer, followed by the addition of p-NPP (100 mM) as a substrate. The mixture was reacted at 45 °C for 25 min, and the absorbance at 400 nm was monitored (A). 400 (Changes).

[0074] Peroxidase-like activity: A mixture of Cu@MOF-808-NH2 and the analyte was added to NaAc buffer, followed by the addition of TMB and H2O2 as substrates. The absorbance at 652 nm was monitored (AL). 652 )change.

[0075] Laccase-like activity: A mixture of Cu@MOF-808-NH2 and the analyte was added to Tris-HCl buffer, followed by the addition of 2,4-DP and 4-AP as substrates. The absorbance at 510 nm was monitored (AL). 510 )change.

[0076] 2.2 Optimization of reaction conditions for the detection system

[0077] To determine the optimal reaction conditions for each active channel of the Cu@MOF-808-NH2 nanozyme for sensing analysis, the conditions for its enzyme-like activity were systematically optimized to obtain the highest catalytic reaction rate and detection sensitivity.

[0078] (1) Optimization objective and experimental design:

[0079] Optimal catalytic activity depends on specific reaction environments (pH and temperature). Using a univariate method, this invention systematically investigated the effects of pH and temperature on the three catalytic activities of nanozymes to determine the optimal reaction conditions for each sensing channel, ensuring that subsequent sensor array detection obtains the strongest and most stable signal response.

[0080] (2) pH optimization:

[0081] Methods: The reaction temperature (45.0 ℃) and nanozyme concentration (0.3 mg / mL) were fixed, and the nanozyme was placed in buffer systems with different pH values ​​for reaction.

[0082] Phosphatase-like activity: 20 mL of Cu@MOF-808-NH2 (3 mg / mL) was added to 160 mL of 9.8 mM NEM buffer (pH 8.0-10.0) (8.0, 8.5, 9.0, 9.5, 10.0), followed by 20 mL of p-NPP (100 mM) as substrate. The reaction was carried out at 45°C for 25 min, and the absorbance at 400 nm was monitored (A). 400 (Changes).

[0083] Peroxidase-like activity: In 160 mL of 0.2 M NaAc buffer (pH 3.0–5.0), 20 mL of Cu@MOF-808-NH2 (3 mg / mL) was added, followed by 10 mL of TMB (20 mM) and 10 mL of H2O2 (20 mM) as substrates. The absorbance at 652 nm was monitored (AL). 652 )change.

[0084] Laccase-like activity: In 160 mL of 1.0 M Tris-HCl buffer (pH 7.0–9.0), 20 mL of Cu@MOF-808-NH2 (3 mg / mL) was added, followed by 10 mL of 2,4-DP (20 mM) and 10 mL of 4-AP (20 mM) as substrates. The absorbance at 510 nm was monitored (A). 510 )change.

[0085] Results and Analysis: See below. Figure 3As shown in Figure A: Phosphatase-like activity peaks at pH 9.5. Under these alkaline conditions, the deprotonation of p-NPP substrates is more favorable, leading to their binding to the catalytic sites on the nanozyme surface, thus exhibiting the highest hydrolysis efficiency. See also... Figure 3 As shown in Figure C: Peroxidase-like activity is highest at pH 4.0. An acidic environment favors the homolytic cleavage of H₂O₂, generating hydroxyl radicals (·OH), and simultaneously promotes the oxidation of TMB (electron donor), resulting in the most rapid colorimetric reaction. See also... Figure 3 As shown in Figure B, laccase-like activity is optimal at pH 7.5. This near-neutral condition is most favorable for the formation and stability of quinone products, resulting in the highest catalytic efficiency.

[0086] Conclusion: The optimal reaction pH values ​​for the phosphatase channel, peroxidase channel, and laccase channel were determined to be 9.5, 4.0, and 7.5, respectively.

[0087] (3) Temperature optimization:

[0088] Methods: Under their respective optimal pH conditions, the reaction systems were placed at different temperatures (30 °C, 35 °C, 40 °C, 45 °C, 50 °C, 55 °C, 60 °C) for reaction, and the maximum absorbance of each channel was monitored.

[0089] Results and Analysis: The results are as follows Figure 3 As shown in the DF, the activities of the three enzymes all exhibit typical temperature dependence: the reaction rate increases with increasing temperature; after exceeding the optimum temperature, the reaction rate decreases due to possible changes in the nanozyme structure or substrate denaturation.

[0090] Conclusion: Since different pesticides enhance or inhibit the activity of Cu@MOF-808-NH2 to varying degrees, excessively low absorbance makes it difficult to distinguish pesticide types, while excessively high absorbance exceeds the linear range of Lambert-Beer's law. Therefore, the phosphatase channel was selected for reaction at 45 °C, the laccase channel at 55 °C, and the peroxidase channel at 40 °C.

[0091] (4) The final determined optimal reaction conditions:

[0092] Through the above system optimization, the optimal response conditions for each channel of the sensor array in this invention were finally determined as shown in the table below:

[0093]

[0094] Channel 4 is a fluorescence channel that does not involve enzyme activity and does not require the addition of substrate. It directly monitors the fluorescence response of the sample to the nanozyme. The test was conducted in Tris-HCl (0.1M, pH 8.0) buffer, and the fluorescence intensity at 425nm was measured by excitation at 325nm.

[0095] This optimization process ensures that each activity can operate at its peak state in subsequent sensing tests, thereby maximizing the capture of the inhibitory or enhancing effects of different pesticides on the activity (i.e., signal changes). This greatly improves the sensitivity and discrimination ability of the sensor array, laying a solid foundation for the successful and efficient identification and quantification of multiple pesticides.

[0096] Example 3: Sensor Array Construction and Pesticide Response Database Establishment

[0097] 3.1. Preparation of pesticide standard solution: Accurately weigh the standards of glyphosate (Gly), pirimicarb (Pir), methomyl (Mtmc), chlorothalonil (Cht), mesotrione (Met), and etoxazole (Eto) to prepare a pesticide standard solution with a concentration of 1000 ppm, and store it in a refrigerator away from light.

[0098] 3.2. Sensor array response signal acquisition:

[0099] Take 0.2, 1, 2, 5, 10, 15, 20, 25, 30, 35, 40, and 45 mL of the above pesticide standard solutions (1000 ppm) respectively, mix them thoroughly with 20 mL of Cu@MOF-808-NH2 nanozyme suspension (0.3 mg / mL), and incubate at room temperature for 15 min to allow the pesticides to fully react with Cu@MOF-808-NH2 nanozymes.

[0100] The mixed solution was added to the reaction system corresponding to the four channels and incubated under optimal reaction conditions for a certain period of time to ensure that the reaction proceeded fully.

[0101] Channel 1 (phosphatase-like activity, OPH): Add 20 mL of p-NPP (100 mM) to 9.8 mM, pH 9.5 NEM buffer, react at 45 °C for 25 min, and monitor the absorbance at 400 nm (A). 400 (Changes).

[0102] Channel 2 (laccase-like activity, LAC): Add 10 mL of 2,4-DP (20 mM) and 10 mL of 4-AP (20 mM) to 1.0 M, pH 7.5 Tris-HCl buffer, react at 45 °C for 25 min, and monitor the absorbance at 510 nm (A). 510 )change.

[0103] Channel 3 (peroxidase-like activity, POD): In 0.2 M, pH 4.0 NaAc buffer, add 10 mL LTMB (20 mM) and 10 mL H2O2 (20 mM), react at 45 °C for 25 min, and monitor the absorbance at 652 nm (A). 652 )change.

[0104] Channel 4 (fluorescence properties, FL): The fluorescence intensity (F) was measured at 425 nm at an excitation wavelength of 325 nm after reacting in 0.1 M, pH 8.0 Tris-HCl buffer for 25 min at room temperature. 425 ).

[0105] Note: The volume of buffer solution added is calculated based on the volume of the mixed solution, and the final reaction system is kept at 200 mL. That is, 0, 0.2, 1, 2, 5, 10, 15, 20, 25, 30, 35, 40, and 45 mL of the above pesticide standard solutions (1000 ppm) are added to a 200 mL reaction system, and the corresponding final working concentrations are 0, 1, 5, 10, 25, 50, 75, 100, 125, 150, 175, 200, and 225 ppm, respectively.

[0106] Using an ELISA reader, the response signals of the reaction system were detected sequentially according to the channel order:

[0107] At least five parallel experiments were set up for each pesticide sample and blank control to eliminate random errors.

[0108] 3.3. Data Processing and Response Value Calculation:

[0109] All data points were normalized (Z-score normalization) to eliminate the influence of dimensions and improve comparability at different concentrations.

[0110] 3.4. Construction of Pesticide Response Database:

[0111] The obtained multidimensional response values ​​were organized by pesticide type to construct an initial training set data matrix. The rows of this matrix represent different pesticide samples (6 pesticides × 12 concentrations × 5 replicates), and the columns represent the response values ​​of the four sensor channels.

[0112] The database should include information such as pesticide name, concentration, raw signal values ​​of the four channels, normalized data, and corresponding experimental conditions, forming a structured "fingerprint" response database. This database serves as the basis for subsequent chemometric analysis.

[0113] Example 4: Identification of different types of pesticides at the same concentration using PCA and HCA analysis

[0114] The data matrix (6 pesticides × 5 replicates) obtained in Example 3 at the same concentration was imported into Origin data analysis software. Multivariate analysis was selected in the statistics menu, and principal component analysis (PCA) and hierarchical cluster analysis (HCA) were performed respectively.

[0115] PCA Analysis: Data was normalized before analysis to eliminate the influence of dimensions. A two-dimensional score plot of PC1 and PC2 was drawn (see...). Figure 4-7 ).from Figure 4-7 It can be clearly seen that the 30 data points of the six pesticides formed six independent and clear clusters. The five repeating points within each cluster were closely clustered, while the different clusters were completely separated.

[0116] HCA analysis: Euclidean distance was used as the measure of similarity between samples, and the average connectivity (between groups) method was used for hierarchical clustering. The analysis results are presented in the form of a dendritic diagram (see...). Figure 8-11 ).from Figure 8-11 As can be seen, on the distance scale, all five replicates of the same pesticide first cluster in pairs to form small branches. Then these branches aggregate with other replicates of the same pesticide. Finally, different types of pesticides form six clear and independent clusters on a larger distance scale.

[0117] The analytical conclusions of PCA and HCA are highly consistent, both indicating that the four-channel sensor array constructed in this invention, combined with chemometrics, can very effectively distinguish these six different categories of pesticides.

[0118] Example 5: Establishment and Application of Quantitative Analysis Model

[0119] Taking glyphosate (Gly) and pirimicarb (Pir) as examples, the quantitative detection process is explained in detail.

[0120] For glyphosate (Gly): According to the response heatmap ( Figure 12 ), glyphosate-dependent phosphatase (A 400 ), Laccase channel (A 510 ) and fluorescence channel (F 425 The signal response of these three channels is the most significant. Therefore, these three channels are selected to establish a quantitative model.

[0121] Glyphosate standard solutions with concentration gradients of 1, 5, 10, 25, 50, 75, 100, 125, 150, 175, 200, and 225 ppm were prepared, and the concentrations at each point were measured according to the method in Example 3. 400 A 510 and F 425 Signal value under the channel. With concentration C as the x-axis, signal value (A) 510 and F 425Using y as the ordinate, a linear fit was performed, and the result is as follows: Figure 12 As shown.

[0122] In A 400 The channel yields the linear regression equation: A 400 = -0.0082C + 1.063 (R² = 0.995), linear range is 1 - 50 ppm, and the limit of detection (LOD) calculated based on 3σ / k is 0.254 ppm.

[0123] In A 510 The channel yields the linear regression equation: A 510 = -0.0029C + 0.785 (R² = 0.997), linear range is 1 - 150 ppm, and the limit of detection (LOD) calculated based on 3σ / k is 0.261 ppm.

[0124] In F 425 The channel yields the linear regression equation: F 425 = -0.3025C + 111.384 (R² = 0.999), linear range is 1 - 150 ppm, limit of detection (LOD) is 0.278 ppm.

[0125] For Pir: According to the response heatmap ( Figure 13 ), pirimicarb against laccase channels (A 510 ) and fluorescence channel (F 425 The signal response of the two channels is the most significant. Therefore, these two channels are chosen to establish a quantitative model.

[0126] Prepare standard solutions of pirimicarb with concentration gradients of 1, 5, 10, 25, 50, 75, 100, 125, 150, 175, 200, and 225 ppm. Detect the concentration at each point in A according to the method in Example 3. 510 and F 425 Signal value under the channel. With concentration C as the x-axis, signal value (A) 510 and F 425 Using y as the ordinate, a linear fit was performed, and the result is as follows: Figure 13 As shown.

[0127] In A 510 The channel yields the linear regression equation: A 510 = -0.0019C + 0.916 (R² = 0.997), linear range is 1 - 225 ppm, and the limit of detection (LOD) calculated based on 3σ / k is 0.244 ppm.

[0128] In F 425The channel yields the linear regression equation: F 425 =5.5141C + 389.627 (R² = 0.992), linear range of 1 - 200 ppm, limit of detection (LOD) of 0.33 ppm.

[0129] Similar response heatmaps can be created for other pesticides. Figure 14-17 ), select the channel with the most significant response to establish a quantitative model.

[0130] The quantitative analysis results are summarized in the table below (for reference). Figure 18-23 ):

[0131]

[0132] Example 6: Verification of anti-interference capability based on signal strength

[0133] To demonstrate the specificity of the sensor array's response to the target pesticide, this experiment compares the signal intensities of the pesticide and potential interfering substances at the same concentration to verify the array's ability to accurately identify pesticides in complex matrices.

[0134] The following representative interfering substances were selected: amino acids (glutamic acid, alanine), sugars (xylose, fructose), and inorganic ions (Na+). + K + ), metal ions (Mg) 2+ Mn 2+ Ca 2+ Co 2+ ) and antibiotics (tetracycline). All interfering agents and target pesticides (glyphosate (Gly), pirimicarb (Pir), methomyl (Mtmc), chlorothalonil (Cht), mesotrione (Met), and etoxazole (Eto)) were prepared as 100 ppm solutions.

[0135] The response signals of all samples in the four-channel sensor array were detected according to the method in Example 3. The results are as follows: Figure 24 As shown in Figure A, under the four detection channels, the signal intensity generated by all target pesticides was significantly higher than that of each interfering substance. Principal component analysis (PCA) was further used to reduce the dimensionality of all data. (PCA score graph) Figure 24(B) Clearly, all data points from interfering agent groups are tightly clustered together, forming an independent "interfering agent cluster." Data points from all target pesticides are clearly distributed in different spatial regions far from the interfering agent cluster. Multiple repeated data points for the same pesticide exhibit good clustering, and the points from pesticide-interfering agent mixture samples are completely converged within the same cluster region as the points for the corresponding single pesticide. This fully demonstrates that the sensor array constructed in this study has a significantly stronger response capability to the target pesticide. This inherent signal strength advantage is the physical basis for the array's ability to effectively resist complex matrix interference and achieve accurate pesticide identification. Combined with chemometric analysis, it can be ensured that even in the presence of multiple coexisting substances, the characteristic "fingerprint" signal of the pesticide can still be clearly captured and identified in actual sample detection.

[0136] Example 7: Actual Sample Detection and Spike Recovery Experiment

[0137] To verify the applicability and reliability of this invention in real-world complex samples, five representative agricultural products—tea, blueberries, loquats, cucumbers, and cherries—were selected for spiked recovery experiments. In this study, tea, blueberries, loquats, cucumbers, and cherries were randomly purchased from a supermarket in Hefei, China. 5.0 g of sample was weighed into a 50 mL centrifuge tube, and 20 mL of acetonitrile-water solution (V:V = 4:1) was added. The mixture was vortexed for 5 min to extract the sample, followed by centrifugation at 8000 r / min for 10 min. The supernatant was filtered through a 0.22 mm organic filter membrane; the filtrate was used as the blank sample extract and stored at 4 ℃ for later analysis. Accurately measure 1.0 mL of each blank sample extract and add standard solutions of glyphosate (Gly), pirimicarb (Pir), methomyl (Mtmc), chlorothalonil (Cht), mesotrione (Met), and etoxazole (Eto) to prepare spiked samples at two concentration levels: low (25 ppm) and medium (100 ppm). Set up 3 replicates for each concentration.

[0138] The spiked sample was tested according to the method in Example 3 to obtain its four-channel response data vector. The data vectors were then substituted into the established PCA and HCA models for identification.

[0139] PCA results: as follows Figure 25 As shown (taking tea as an example), the projection points of the spiked samples of glyphosate, pirimicarb, methomyl, chlorothalonil, mesotrione and etoxazole all fall accurately within the clustering areas of their corresponding pesticide standards, with clear positions and no overlap.

[0140] HCA results: as follows Figure 26 As shown (taking tea as an example), all data points of the spiked samples are clustered with their corresponding pesticide standards in the smallest branch of the same large cluster, showing a high degree of similarity.

[0141] The results from the two chemometric analysis methods were consistent, enabling accurate identification of spiked pesticides and forming a strong mutual verification.

[0142] The pesticide residues in each spiked sample were calculated using the quantitative model established in Example 6, and the recoveries and relative standard deviations (RSDs) were also calculated. The results are shown in the table below. The average recoveries of pesticides (taking glyphosate as an example) in the five complex agricultural product matrices were all between 98.80% and 102.12%, and the RSDs were all less than 5%, which fully meet the requirements for the accuracy and precision of pesticide residue detection methods.

[0143]

[0144] Note: — indicates that the signal value of this channel exceeds the linear range at this concentration.

[0145] This embodiment demonstrates that the present invention has been successfully applied to complex matrices of various representative agricultural products, including tea, blueberries, loquats, cucumbers, and cherries. Combined with an optimized pretreatment method, matrix interference is effectively overcome, enabling accurate qualitative identification and quantitative analysis of multiple pesticides. This method effectively overcomes the limitations of traditional enzyme inhibition methods, such as identifying only a single pesticide species, being unable to distinguish structural analogs, and being susceptible to interference from complex matrices. It exhibits significant advantages such as ease of operation, good stability, strong anti-interference ability, and wide applicability, providing an efficient and reliable technical solution for the rapid screening and accurate differentiation of multiple pesticide residues in agricultural products.

[0146] The above description, in conjunction with specific embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, several simple deductions or substitutions can be made without departing from the concept of the present invention, and all such deductions or substitutions should be considered to fall within the scope of protection defined by the claims submitted herein.

Claims

1. A method for detecting multiple pesticide residues based on Cu@MOF-808-NH2 nanoenzyme sensing array, characterized in that, The method includes the following steps: (1) Sensing signal acquisition: The sample to be tested is reacted with Cu@MOF-808-NH2 nanozyme under preset conditions, and its response signals in the four channels of phosphatase-like activity channel, laccase-like activity channel, peroxidase-like activity channel and fluorescence channel are detected respectively to obtain the multidimensional response data vector corresponding to the sample. (2) Chemometric model establishment and analysis: Using a variety of known pesticide standards, repeat step (1) to obtain the training set data matrix. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) are used to perform dimensionality reduction and analysis on the training set data matrix to obtain PCA score map and HCA dendrogram that can intuitively distinguish different pesticide types. In addition, standard curves of response signal values ​​and pesticide standard concentrations under four channels for different samples are constructed. (3) Analysis of the test sample: The multidimensional response data vector of the test sample obtained in step (1) is projected into the PCA model space established in step (2), and HCA analysis is performed based on its Euclidean distance with the training set data. The position of the test sample in the PCA score map and the clustering situation in the HCA tree map are compared with the clustering regions of known pesticides to achieve qualitative identification of pesticide types in the test sample. In addition, the response values ​​of the test sample in different channels are substituted into the constructed standard curve to obtain the concentration value of the test sample, so as to achieve quantitative analysis of pesticides in the test sample. The preparation of the Cu@MOF-808-NH2 nanozyme includes: Synthesis of MOF-808-NH2: A mixture of pyromellitic acid, 2-aminoterephthalic acid and zirconium oxychloride octahydrate was dispersed in a mixture of N,N-dimethylformamide and formic acid with a volume ratio of V / V = 1:

1. The mixture was sonicated for a period of time, and the solution was transferred to a reaction vessel and placed in an oven for continuous heating. After the reaction was completed, the reactants were naturally cooled to room temperature. The resulting mixture was washed three times by centrifugation with N,N-dimethylformamide and acetone, and then dried in an oven to obtain MOF-808-NH2. Synthesis of Cu@MOF-808-NH2: Copper acetate monohydrate and MOF-808-NH2 prepared in step (1) were mixed and dispersed in N,N-dimethylformamide, acetic acid was added, and the mixture was ultrasonically dispersed. The dispersed solution was transferred to a reaction vessel and heated in an oven. After the reaction was completed, the reactants were naturally cooled to room temperature. The resulting mixture was washed three times by centrifugation with methanol and acetone, and finally dried in an oven to obtain Cu@MOF-808-NH2.

2. The detection method according to claim 1, characterized in that, The preparation of the Cu@MOF-808-NH2 nanozyme includes: (1) Synthesis of MOF-808-NH2: Weigh 0.1175 g of trimesic acid, 0.1014 g of 2-aminoterephthalic acid and 0.54 g of zirconium oxychloride octahydrate, and disperse them in 30 mL of a mixture of N,N-dimethylformamide and formic acid with a volume ratio of V:V=1:

1. Sonicate for 15 min, then transfer the above solution to a reaction vessel and place it in an oven at 120 ℃ for 48 h. After the reaction is completed, the reactants are naturally cooled to room temperature. The resulting mixture is washed three times by centrifugation at 10000.0 rpm with N,N-dimethylformamide and acetone, respectively. Finally, it is dried in an oven at 60 ℃ for 6.0 h to obtain MOF-808-NH2. (2) Synthesis of Cu@MOF-808-NH2: Weigh 0.1039 g of copper acetate monohydrate and 0.05 g of MOF-808-NH2, disperse them in 15 mL of N,N-dimethylformamide, add 2.5 mL of acetic acid, sonicate for 15 min, then transfer the above solution to a reaction vessel, place it in an oven at 120 ℃, and heat for 12 h. After the reaction is completed, cool the reactants to room temperature naturally. The resulting mixture is washed three times by centrifugation with methanol and acetone at 10000.0 rpm, and finally dried in an oven at 60 ℃ for 6.0 h to obtain Cu@MOF-808-NH2.

3. The detection method according to claim 1, characterized in that, In step (1), the phosphatase-like active channel uses disodium p-nitrophenyl phosphate (p-NPP) as a substrate, and the absorbance is detected at a wavelength of 400 nm under reaction conditions of pH 9.5 and 45℃; the laccase-like active channel uses 2,4-dichlorophenol (2,4-DP) and 4-aminoantipyrine (4-AP) as substrates, and the absorbance is detected at a wavelength of 510 nm under reaction conditions of pH 7.5 and 55℃; the peroxidase-like active channel uses 3,3',5,5'-tetramethylbenzidine (TMB) and hydrogen peroxide (H2O2) as substrates, and the absorbance is detected at a wavelength of 652 nm under reaction conditions of pH 4.0 and 40℃; the fluorescence channel is detected at a fluorescence intensity of 425 nm emission wavelength under an excitation wavelength of 325 nm.

4. The detection method according to claim 1, characterized in that, The quantitative analysis of pesticide samples in step (3) includes: (a) For the pesticide species to be quantified, select one or more detection channels with the most significant response; (b) Prepare a series of pesticide standard solutions of different concentrations and measure their response signal values ​​in the selected channels according to the method in step (1); (c) Plot a standard curve with pesticide concentration on the x-axis and response signal value on the y-axis, and fit the linear regression equation y = aC + b, where y is the signal value, C is the pesticide concentration, a is the slope, and b is the intercept. (d) Substitute the signal value yn measured in the corresponding channel of the sample to be tested in step (1) into the linear regression equation to calculate the concentration Cn of the pesticide.

5. The detection method according to claim 1, characterized in that, The pesticides include one or more of the following: organophosphates, carbamates, organochlorines, sulfonylureas, and phenoxyzoline pesticides.

6. The detection method according to claim 5, characterized in that, The pesticide is one or more of glyphosate, pirimicarb, methomyl, chlorothalonil, mesotrione, and etoxazole.

7. A reagent kit for detecting multiple pesticide residues, characterized in that, The kit includes the Cu@MOF-808-NH2 nanozyme as described in claim 1.

8. The reagent kit according to claim 7, characterized in that, The kit also includes disodium nitrophenyl phosphate (p-NPP), 2,4-dichlorophenol (2,4-DP), 4-aminoantipyrine (4-AP), 3,3',5,5'-tetramethylbenzidine (TMB), hydrogen peroxide (H2O2), and buffer solution.

9. The reagent kit according to claim 8, characterized in that, The buffer solutions in the kit are NEM buffer, Tris-HCl buffer, and NaAC buffer.

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