Method for detecting pesticide based on ferriporphyrin modified covalent organic framework nano-enzyme sensing array

Abstract

The invention relates to a method for detecting pesticide based on a ferriporphyrin modified covalent organic framework nano-enzyme sensing array. According to the invention, ferriporphyrin molecules are loaded on a porous and ordered COFs skeleton, so that excellent peroxidase simulated catalytic activity is realized, H2O2 can be catalyzed to be decomposed to generate reactive oxygen free radicals, and TMB, OPD and ABTS chromogenic reactions are catalyzed. Based on the performance, a three-channel colorimetric sensing array is constructed to detect and distinguish various pesticides such as malathion, profenofos, chlorpyrifos, acephate and dimethoate, and the detection limit is as low as 0.341 nM. After different pesticides are adsorbed to active sites, catalytic reaction can be remarkably inhibited, and specific colorimetric fingerprint signals are generated. Colorimetric data is further combined with a machine learning algorithm, high-accuracy classification and recognition of single pesticides and mixtures thereof are achieved by means of a multilayer sensor, a support vector machine, a random forest and other models, the classification accuracy of an MLP model reaches 90%, ROC curve AUC exceeds 0.97, and the reliability of the detection method is proved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of pesticide detection, in particular to a method for detecting pesticides based on iron porphyrin modified covalent organic framework nanoscale enzyme sensing array. BACKGROUND

[0002] Pesticides are widely used in modern agriculture to control pests and improve crop yields. However, excessive application and improper management of pesticides have led to their accumulation in agricultural products, soil and water bodies, posing a serious threat to human health and the ecological environment. In particular, organophosphorus pesticides can irreversibly inhibit acetylcholinesterase activity, causing dizziness, nausea and even organ damage. Currently, pesticide residue detection mainly relies on chromatography, electrochemical and immunoassay methods, but these methods generally have problems such as expensive instruments, complicated operation, limited sensitivity, and are difficult to meet the needs of rapid on-site detection of multiple pesticides.

[0003] In recent years, nanoscale enzymes have attracted attention in the field of pesticide detection due to their biomimetic catalytic activity, low cost and good stability. Covalent organic frameworks (COFs) are used to load active metals or enzyme mimics due to their highly ordered channels, rich functional groups and excellent chemical stability. However, the catalytic activity of COFs is limited and needs to be improved by metal center modification to enhance their enzyme-like activity. The present application is based on the peroxidase-like activity of COFs modified by iron porphyrin (Hemin), and designs a sensing array combined with machine learning algorithms to achieve high sensitivity and high throughput detection of multiple pesticides. SUMMARY

[0004] To solve the technical problems of poor sensitivity, insufficient selectivity and inability to simultaneously detect multiple pesticides in the prior art, the present application provides a method for detecting pesticides based on iron porphyrin modified covalent organic framework nanoscale enzyme sensing array. The present application utilizes the unique structure and catalytic properties of iron porphyrin modified covalent organic framework (COFs-Hemin) to prepare TAPB-DMTP-COFs with highly ordered channels and rich functional groups, and then in situ anchor iron porphyrin active sites, thereby significantly enhancing its peroxidase-like activity. This catalytic material can efficiently catalyze the decomposition of hydrogen peroxide to generate hydroxyl radicals (·OH) and superoxide anion radicals (O2· - ), which in turn oxidize colorimetric substrates TMB, OPD and ABTS, producing obvious optical absorption signals. Different pesticide molecules interact with the catalytic active centers of COFs-Hemin, hindering the oxidation reaction of the substrate through hydrogen bonding, van der Waals forces or coordination, causing different degrees of attenuation of the colorimetric signal. This characteristic is used to construct a multi-channel colorimetric response array to achieve simultaneous and differentiated detection of multiple pesticides.

[0005] The first object of the present application is to provide an application of an iron porphyrin modified covalent organic framework nanoscale enzyme in multi-channel detection of pesticides, wherein the iron porphyrin modified covalent organic framework nanoscale enzyme is prepared by the following method: 1,3,5-tri(4-aminophenyl)benzene and 2,5-dimethoxy terephthaldehyde are dissolved in a mixed solvent, acid is added and stirring reaction is carried out to obtain TAPB-DMTP-COFs; TAPB-DMTP-COFs is ultrasonically dispersed in water, and a hemin chloride solution is added dropwise, and stirring reaction is carried out to obtain the iron porphyrin modified covalent organic framework nanoscale enzyme (COFs-hemin complex).

[0006] In some embodiments of the present application, the mass ratio of 1,3,5-tri(4-aminophenyl)benzene and 2,5-dimethoxy terephthaldehyde is (40-50):(35-40); the mixed solvent comprises 1,4-dioxane and m-trimethylbenzene; and the volume ratio of 1,4-dioxane and m-trimethylbenzene is (1:2)-(2:1).

[0007] In some embodiments of the present application, the acid is a C1-C6 fatty acid; further, the C1-C6 fatty acid is selected from one or more of acetic acid, formic acid, propionic acid, lactic acid or citric acid, and the effect is the same as long as the pH is kept in the range of 3.5-5.5; the stirring reaction time is 60-80 hours, and the temperature is 20-35℃.

[0008] In some embodiments of the present application, the concentration of the hemin chloride solution is 5-15 mM.

[0009] In some embodiments of the present application, the mass ratio of TAPB-DMTP-COFs to hemin is (8-12):1.

[0010] In some embodiments of the present application, the pesticide comprises one or more of malathion, profenofos, chlorpyrifos, dimethoate and acephate.

[0011] In some embodiments of the present application, the concentration of the solution containing the pesticide in the process of detecting the pesticide is 10-100 nM.

[0012] In some embodiments of the present application, the amount of the iron porphyrin modified covalent organic framework nanoscale enzyme is 0.5-2 mg·mL -1 .

[0013] The second object of the present application is to provide a method for detecting pesticides by using an iron porphyrin modified covalent organic framework nanoscale enzyme sensing array, comprising the following steps: providing a sensing element of an iron porphyrin modified covalent organic framework nanoscale enzyme; A multi-channel pesticide colorimetric detection system is constructed by using the sensing element, and the absorbance values of different pesticides are detected according to a plurality of pesticide standard solutions with different concentration gradients, H2O2 and a color developing agent. The obtained absorbance values are input into a multi-layer perceptron (MLP), a random forest (RFC), a support vector machine (SVM) and a K nearest neighbor (KNN) model for training and identification to obtain a training data matrix of different pesticides.

[0014] The above-mentioned model is used for intelligent identification and classification of pesticide colorimetric signals. Specifically, the multi-channel absorbance data of different pesticides under three color developing substrates (TMB, OPD and ABTS) are used as input features, and the types of pesticides are used as output labels. The model is trained and identified by using a multi-layer perceptron (MLP), a random forest (RFC), a support vector machine (SVM) and a K nearest neighbor (KNN). The MLP is used for nonlinear feature learning, the RFC enhances the stability of classification, the SVM realizes high-dimensional accurate classification, and the KNN judges the category based on similarity. The model outputs the identification results and classification probabilities of different pesticides, and realizes high-accuracy intelligent discrimination of single and mixed pesticides.

[0015] The above technical solutions of the present application have the following advantages compared with the prior art: The COFs-Hemin nanoscale enzyme provided by the present application can generate hydroxyl radicals (·OH) and superoxide anions (O2·⁻) under hydrogen peroxide conditions, and realizes efficient catalytic oxidation of color developing substrates (TMB, OPD and ABTS), and has excellent peroxidase-like activity. The present application utilizes the specific binding of pesticides to the active site of the nanoscale enzyme to inhibit the catalytic performance, and realizes high-sensitivity detection of a plurality of pesticides and their mixtures. Combined with machine learning algorithms, the detection accuracy and throughput are significantly improved, and the problems of long detection period, high cost and insufficient sensitivity of traditional detection methods are overcome, thereby providing an efficient and convenient new method for pesticide residue detection in environmental water and food safety. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to make the content of the present application more easily understood, the present application will be further described in detail below according to specific embodiments of the present application and in conjunction with the drawings, wherein, Figure 1 The morphology and composition of the iron porphyrin modified covalent organic framework (COFs-Hemin) prepared in the present application are shown in the following figures, wherein A is a scanning electron microscope (SEM) image, B is a transmission electron microscope (TEM) image, C is an element distribution map, D is a Fourier transform infrared spectrum (FT-IR), E is an N 1s X-ray photoelectron spectrum (XPS), and F is an element content statistical analysis.

[0017] Figure 2The prepared COFs-Hemin multi-enzyme catalytic activity verification chart, wherein A is a mechanism schematic diagram of COFs-Hemin catalyzing H2O2 to produce reactive oxygen species, B is an ultraviolet-visible absorption spectrum of catalyzing TMB, C is an absorption spectrum of catalyzing OPD, D is an absorption spectrum of catalyzing ABTS, and E is an ESR (electron paramagnetic resonance) spin trapping verification chart.

[0018] Figure 3 The colorimetric response and discriminant analysis chart of the pesticide detection sensor platform constructed based on the COFs-Hemin, wherein A is a schematic diagram of the COFs-Hemin sensor array for pesticide detection, B is an ultraviolet absorption intensity column chart of three catalytic substrates for the detection of five pesticides at a concentration of 60 nM, C is a schematic diagram of the operation process of the colorimetric detection of COFs-Hemin, D is a typical score chart of the colorimetric response mode of different concentrations of malathion (Mal), E is a typical score chart of the colorimetric response mode of five pesticides at a concentration of 20 nM, and F is a typical score chart of the colorimetric response mode of different proportions of acephate (Ace) and malathion (Mal) mixtures.

[0019] Figure 4 The pesticide classification and functional group identification chart based on the COFs-Hemin sensor and the support vector machine (SVM) model, wherein A is a classification process schematic diagram, B is a five-pesticide classification confusion matrix, C is an acetyl group identification confusion matrix, D is a distinction confusion matrix of dimethoate and acephate, E is an aromaticity identification confusion matrix, and F is a distinction confusion matrix of profenofos and chlorpyrifos.

[0020] Figure 5 The pesticide detection platform performance verification chart based on the COFs-Hemin nanosensor array combined with a machine learning algorithm, wherein A is a detection process schematic diagram, B is a column chart of the recognition accuracy of multiple machine learning models, C is a classification of seven types of pesticides (including single and mixed samples), D is a ROC curve of a multilayer perceptron (MLP) model, and E is a confusion matrix of the MLP classification model. DETAILED DESCRIPTION

[0021] The present application will be further described below in conjunction with the drawings and specific embodiments, so that those skilled in the art can better understand the present application and implement it, but the embodiments are not limiting to the present application.

[0022] Example 1 The present application provides a preparation and structure characterization of COFs-Hemin, which is specifically as follows: I. A preparation method of iron porphyrin modified covalent organic framework (COFs-Hemin): 1. 44.8 mg of 1,3,5-tris(4-aminophenyl)benzene and 38.4 mg of 2,5-dimethoxytetraphenylbenzene were dissolved in a mixed solvent of 1.2 mL of 1,4-dioxane and 1.2 mL of mesitylene. 0.4 mL of 3 M acetic acid was added, and the mixture was stirred at 2000 rpm for 72 hours at room temperature. After the reaction was completed, a pale yellow precipitate was formed. The precipitate was filtered through a 0.45 μm microporous membrane, and impurities were removed by washing with methanol and deionized water alternately. The precipitate was then dried under vacuum at 80 °C for 12 hours to obtain TAPB-DMTP-COFs powder.

[0023] 2. Take 2.5 mg TAPB-DMTP-COFs powder and disperse it in 10 mL of deionized water. Sonicate the dispersion for 30 minutes, slowly add 100 μL of Hemin chloride solution dissolved in 10 mM DMSO, stir magnetically at room temperature for 12 h, filter and wash thoroughly with DMSO, and vacuum dry to obtain COFs-Hemin complex.

[0024] II. Structural Characterization: The obtained COFs-Hemin complex was structurally characterized, such as... Figure 1 As shown, A is a scanning electron microscope (SEM) image showing a microscopic spiky porous morphology; B is a transmission electron microscope (TEM) image observing an ordered porous framework; C is an elemental distribution map showing uniform distribution of C, N, O, and Fe; and D is a Fourier transform infrared (FT-IR) spectrum showing the N–H bending vibration peak starting at 1618 cm⁻¹. -1 Moved to 1645 cm -1 E represents the N 1s X-ray photoelectron spectroscopy (XPS) detected an imine nitrogen peak at 399.3 eV and a Fe–N coordination peak at 397.8 eV, and F represents the elemental content analysis, which showed an Fe content of 0.96%, together verifying that Hemin was successfully loaded and its structure is stable.

[0025] Performance test examples This test example provides a verification of the multi-enzyme catalytic activity and free radical generation ability of COFs-Hemin, as detailed below: The COFs-Hemin complex obtained in Example 1 was dispersed in deionized water to prepare a working solution with a concentration of 40 μg / mL. 10 μL of this solution was added to each well of a 96-well plate, followed by 178 μL of 100 mM acetate-sodium acetate buffer (pH=3.6), 10 μL of 50 mM H2O2, and 10 μL of substrate solution (TMB 0.8 mM, OPD 0.8 mM, and ABTS 1.2 mM). The plate was incubated at room temperature for 24 minutes, and the absorbance of TMB (652 nm), OPD (455 nm), and ABTS (415 nm) was measured using a microplate reader. The results are as follows: Figure 2A is the mechanism diagram of catalyzing H2O2 to generate active oxygen species, B, C, D are the oxidation product absorption peaks of TMB, OPD, ABTS, respectively. Further, 5, 5-dimethyl-1-pyrroline N-oxide (DMPO) was added to the reaction system, and free radicals were detected by electron paramagnetic resonance (ESR), E is the ESR spectrum showing the characteristic signals of DMPO-·OH and DMPO-O2·⁻, indicating that the catalytic decomposition of H2O2 generates double free radicals.

[0026] Application Example This application example is used to construct a colorimetric detection platform based on COFs-Hemin complex to identify various pesticides. Malathion, profenofos, chlorpyrifos, dimethoate and acephate were prepared into 10-100 nM concentration series solutions, respectively. In the 96-well plate, 10 μL of COFs-Hemin complex working solution, 20 μL of pesticide solution, 178 μL of buffer (acetic acid-sodium acetate buffer, pH 3.8), 10 μL of H2O2 and 10 μL of three substrates (TMB concentration of 0.8 mM, OPD concentration of 0.8 mM, ABTS concentration of 1.2 mM) were added to each well in turn. After incubation at room temperature for 16 minutes, the absorbance at 652 nm, 455 nm and 415 nm was measured, and (A0-A) / A0 was calculated. Each pesticide was repeated 6 times, and a colorimetric data matrix of three channels, five pesticides and six repetitions was obtained. The results were subjected to principal component analysis (PCA), as shown in Figure 3 A is the schematic diagram of the detection process, B is the absorption intensity of five pesticides detected by different substrates, C is the construction process of the colorimetric sensing array and the principal component analysis (PCA) method, D is the PCA score plot of malathion at different concentrations, E is the PCA score plot of five pesticides at a concentration of 20 nM, and F is the PCA score plot of malathion and acephate mixed sample. The results show that different pesticides and their mixed samples can be distinguished.

[0027] Example 2 This example provides a COFs-Hemin combined SVM model to realize pesticide functional group identification On the basis of the colorimetric data in the above application example, a machine learning model was constructed to improve the identification accuracy. The three-channel data matrix was grouped according to 80% training and 20% testing, and a support vector machine (SVM) model was constructed using Python scikit-learn tool to classify and train five pesticides and their functional groups. The results are shown in Figure 4 A is the schematic diagram of the classification process, B is the confusion matrix of the classification of five pesticides, C is the confusion matrix of acetyl group identification, D is the confusion matrix of the differentiation between dimethoate and acephate, E is the confusion matrix of aromaticity identification, and F is the confusion matrix of the differentiation between profenofos and chlorpyrifos. The overall accuracy is more than 92%, indicating that the model has high identification performance.

[0028] Example 3 This example provides COFs-Hemin combined with various machine learning algorithms for actual sample detection To verify the applicability of the platform in actual samples, the surface water samples were filtered with 0.45 μm, five-fold diluted, and then 60 nM pesticide standard solution (including single pesticides Dim, Cpf, Mal and their mixtures Dim+Cpf, Mal+Dim, Mal+Cpf, Mal+Dim+Cpf) was added. The colorimetric reaction was carried out according to the conditions in the application example. The obtained three-channel absorbance data were input into multilayer perceptron (MLP), random forest (RFC), support vector machine (SVM), K nearest neighbor (KNN) and other models for training and identification. The results are shown in Figure 5 A is a schematic diagram of the multi-model detection process, B is a comparison of the classification accuracy of each model, C is the classification result of seven types of samples, D is the ROC curve of the MLP model (all AUCs are greater than 0.97), E is the confusion matrix of the MLP model, showing that the recognition accuracy reaches 90%, and the platform has high universality and accuracy for complex samples.

[0029] Obviously, the above examples are only examples for the sake of clarity, and are not limited to the embodiments. For those skilled in the art, other different forms of changes or variations can be made on the basis of the above description. All embodiments do not need to be exhausted here. The obvious changes or variations derived therefrom are still within the protection scope of the present application.

Claims

1. An application of an iron porphyrin modified covalent organic framework nanoszyme in multi-channel detection of pesticides, characterized in that, The iron porphyrin-modified COF nanoszyme is prepared by the following method: 1,3,5-tris(4-aminophenyl)benzene and 2,5-dimethoxy terephthaldehyde are dissolved in a mixed solvent, an acid is added and stirred to react, and TAPB-DMTP-COFs is obtained; TAPB-DMTP-COFs is ultrasonically dispersed in water, and a hemin chloride solution is added dropwise, stirred to react, and the iron porphyrin-modified COF nanoszyme is obtained.

2. Use according to claim 1, characterized in that, The mass ratio of 1,3,5-tris(4-aminophenyl)benzene to 2,5-dimethoxy terephthaldehyde is (40-50):(35-40).

3. Use according to claim 1, characterized in that, The mixed solvent includes 1,4-dioxane and m-trimethylbenzene; the volume ratio of the 1,4-dioxane to the m-trimethylbenzene is (1:2)-(2:1).

4. Use according to claim 1, characterized in that, The acid is a C1-C6 aliphatic acid; the stirring reaction time is 60-80 hours, and the temperature is 20-35°C.

5. The use according to claim 1, characterized in that, The concentration of the hemin chloride solution is 5-15 mM.

6. Use according to claim 1, characterized in that, The mass ratio of the TAPB-DMTP-COFs to the hemin is (8-12):

1.

7. The use according to claim 1, characterized in that, The pesticide includes one or more of malathion, profenofos, chlorpyrifos, dimethoate, and acephate.

8. The use according to claim 1, characterized in that, The concentration of the pesticide-containing solution in the pesticide detection process is 10-100 nM.

9. The use according to claim 1, characterized in that, The amount of iron porphyrin modified covalent organic framework nanoszyme is 0.5-2 mg·mL -1 .

10. A method for detecting pesticides based on iron porphyrin modified covalent organic framework nanosensor array, characterized in that, The method comprises the following steps: A sensing element of an iron porphyrin-modified COF nanoszyme is provided; A multi-channel pesticide colorimetric detection system is constructed using the sensing element, and the absorbance values of different pesticides are detected according to a plurality of pesticide standard solutions with different concentration gradients, H2O2, and a color developing agent; The obtained absorbance values are input into a multi-layer perceptron MLP, a random forest RFC, a support vector machine SVM, and a K nearest neighbor KNN model for training and recognition to obtain a training data matrix of different pesticides.

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