Phosphate hydrolysis nano-enzyme sensing array-based p-nitrophenol pesticide identification and detection method

Abstract

The invention belongs to the technical field of analytical chemistry and pesticide residue detection, and relates to a phosphate hydrolysis nano-enzyme sensing array-based p-nitrophenol pesticide identification and detection method, which comprises the following steps of: performing five groups of parallel data matrixes on each type of p-nitrophenol pesticide to distinguish single / five types of p-nitrophenol pesticides with different concentrations; establishing a discrimination model of different concentrations / types of pesticides by using the data matrix, and obtaining an HCA map and an LDA score map of the p-nitrophenol pesticides through hierarchical clustering analysis and linear discriminant analysis; after the absorbance value of the sample to be detected is measured, the sample to be detected can be identified and detected through comparison. According to the present invention, by using the inherent dynamic difference existing during the catalytic hydrolysis of different p-nitrophenol pesticides by CeO2, a plurality of reaction time points are selected to measure the absorbance change, the time-resolved sensing array fingerprint is constructed, and the type identification and the concentration quantification of a variety of p-nitrophenol pesticides and the mixture thereof are achieved by combining the pattern identification algorithm, such that the sensitivity is high, and the detection result is accurate. The measurable concentration range is 1-50 mu g / mL.

Description

Technical Field

[0001] The present invention belongs to the technical field of analytical chemistry and pesticide residue detection, and relates to the detection of p-nitrophenol pesticides, and in particular to a method for identifying and detecting p-nitrophenol pesticides based on a phosphate hydrolysis nanoenzyme sensor array. Background Art

[0002] Organophosphorus pesticides (OPs) are widely used in agricultural production to control plant diseases, insect pests, and weeds. However, excessive use of OPs can lead to varying degrees of residues in crops, posing a potential threat to human health and the environment. OPs are diverse and can be categorized by toxicity: highly toxic, moderately toxic, and less toxic. For example, the commonly used p-nitrophenol pesticides cause neurological disorders by inhibiting acetylcholinesterase (AChE), and their harm to humans ranges from highly toxic to less toxic. In practice, different types of pesticides are often used interchangeably to achieve different effects and ensure crop yield and quality. Therefore, targeted pesticide use and residue standards should be established to guide their proper use and management by distinguishing different types of pesticides. This means that developing efficient methods and tools to distinguish between various OPs is crucial for ensuring food safety and environmental protection.

[0003] At present, the main identification and detection methods for p-nitrophenol pesticides include test strip method, fluorescence method and colorimetry. For example:

[0004] Chinese patent CN119413783A discloses a methyl paraoxon pesticide detection test strip based on oxygen vacancy-ceria and its preparation method, belonging to the field of biosensor technology. The key feature of this test strip is that it is loaded with a large amount of oxygen vacancy-ceria. Under specific conditions, the oxygen vacancy-ceria nanozyme can exhibit phosphatase-like activity, which can cleave the PO and PS bonds of organophosphorus pesticides, promoting the hydrolysis of methyl paraoxon to the yellow product p-nitrophenol. The detection limit of the nanozyme system is explored, thereby realizing portable, visual, and quantitative detection of methyl paraoxon pesticide.

[0005] Chinese patent CN113092749A discloses a fluorescence ratiometric immunoassay method for fenitrothion. The method involves coating a fenitrothion coating precursor in a microplate and fusing a fenitrothion nanobody with an alkaline phosphatase (Nb-ALP) recognition and signal amplification element. Fenitrothion in the sample competes with the coating precursor for binding to the Nb-ALP. Excess drug and antibody are then washed away. The Nb-ALP attached to the microplate catalyzes the hydrolysis of trisodium L-ascorbic acid-2-phosphate (AAP) to produce ascorbic acid (AA). Subsequently, o-phenylenediamine (OPD) and chitosan-modified platinum nanoparticles (Ch-Pt NPs) are added to the plate, and the AA is competitively oxidized by the Ch-Pt NPs, which have oxidative-like properties. Among them, OPD is oxidized to the fluorescent substance 2,3-diaminophenol (DAP) with a fluorescence emission peak of 568nm; and the oxidized AA reacts with OPD to form a quinoxaline derivative (DFQ) with a fluorescence emission peak of 430nm; therefore, the content of fenitrothion in the sample is more sensitively reflected in the fluorescence ratio of 430nm to 568nm, achieving the purpose of highly sensitive detection of fenitrothion pesticide.

[0006] Chinese patent CN116920838A discloses a gold-mesoporous cerium oxide heterostructure nanozyme and its preparation method and application, as well as a method for detecting organophosphorus pesticides, which relates to the technical field of nanozyme materials. The gold-mesoporous cerium oxide heterostructure nanozyme provided by the present invention comprises mesoporous cerium oxide nanorods and gold nanoparticles loaded onto the surface of the mesoporous cerium oxide nanorods, wherein the mesoporous cerium oxide nanorods and the gold nanoparticles form a heterostructure. In the nanozyme provided by the present invention, the mesoporous cerium oxide nanorods have surface defects and a higher content of Ce. 4+ By modifying gold nanoparticles on mesoporous cerium oxide nanorods, the nanozyme has the synergistic catalytic ability of a heterogeneous catalyst and high phosphatase-like activity, and can detect organophosphorus pesticides such as methyl parathion, ethyl parathion, methyl paraoxon and ethyl paraoxon in dual modes of UV-visible and RGB values.

[0007] Although the above-mentioned disclosed methods for detecting p-nitrophenol pesticides have certain detection effects, they still have the following shortcomings and deficiencies:

[0008] (1) Some methods have limited detection sensitivity and are susceptible to environmental interference;

[0009] (2) Some methods are complex to operate and rely on precision instruments;

[0010] (3) The preparation process of some detection elements is complex and their versatility is limited.

[0011] Therefore, there is an urgent need to develop a detection method that is simple to operate, highly sensitive, has strong anti-interference ability, does not require complex labeling and precision instruments, and can simultaneously identify multiple types and concentrations of p-nitrophenol pesticides. Summary of the Invention

[0012] In response to the problems of limited sensitivity, complex operation, dependence on precision instruments, complex component preparation, poor versatility, and difficulty in simultaneously identifying multiple types and concentrations of pesticides in the above-mentioned prior art detection methods of p-nitrophenols, the present invention aims to provide a p-nitrophenol pesticide identification and detection method based on a phosphate hydrolysis nanoenzyme sensor array.

[0013] Technical Solution

[0014] A method for identifying and detecting p-nitrophenol pesticides based on a phosphate hydrolysis nanoenzyme sensor array comprises the following steps:

[0015] (1) Detection of different concentrations of single p-nitrophenol pesticides: 100 μL of p-nitrophenol pesticides and 100 μL of 5 mg / mL CeO2 were added to 800 μL of 50 mM Tris-HCl buffer and mixed thoroughly to make the final concentrations of p-nitrophenol pesticides in the system 1 μg / mL, 5 μg / mL, 10 μg / mL, 20 μg / mL, 40 μg / mL, and 50 μg / mL, respectively. The mixture was incubated at 90°C for 1 h. The absorbance at 405 nm was measured at incubation times of 20 min, 40 min, and 60 min. Five parallel groups of experiments were performed for each p-nitrophenol pesticide to obtain a data matrix (3 reaction times × 6 concentrations × 5 parallel samples) to distinguish different concentrations of single p-nitrophenol pesticides.

[0016] (2) Detection of different types of p-nitrophenol pesticides at the same concentration: 100 μL of 50 μg / mL p-nitrophenol pesticide and 100 μL of 5 mg / mL CeO2 were added to 800 μL of 50 mM Tris-HCl buffer and mixed thoroughly to make the final concentration of p-nitrophenol pesticide in the system 5 μg / mL. The system was incubated at 90°C for 1 h, and the absorbance at 405 nm was measured at incubation times of 20 min, 40 min, and 60 min. Five parallel groups were performed for each p-nitrophenol pesticide to obtain a data matrix (3 reaction times × 5 target substances × 5 parallel samples) to distinguish the five types of p-nitrophenol pesticides.

[0017] (3) using the data matrix obtained in step (1) to establish a discriminant model for pesticides of different concentrations, and using the data matrix obtained in step (2) to establish a discriminant model for pesticides of different types, and analyzing the obtained data by hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA) to obtain the HCA spectrum and LDA score graph of the p-nitrophenol pesticide;

[0018] (4) The samples to be tested were pretreated to obtain the corresponding vegetable extracts. After 20 min, 40 min, and 60 min of color development, the absorbance value at 405 nm was measured. Based on the established discrimination model, including the HCA spectrum and LDA score graph, the type and concentration of the p-nitrophenol pesticides in the samples to be tested can be identified and distinguished.

[0019] In a preferred embodiment of the present invention, in step (1), the p-nitrophenol pesticide is any one of methyl paraoxon, ethyl paraoxon, methyl parathion, ethyl parathion and fenitrothion.

[0020] In a preferred embodiment of the present invention, the pH value of the Tris-HCl buffer used in steps (1), (2) and (4) is 9.5.

[0021] In a preferred embodiment of the present invention, in step (4), the vegetable extract is an ethanol extract of cabbage.

[0022] In a preferred embodiment of the present invention, in step (4), the detection method can detect the concentration of the p-nitrophenol pesticide in the extract in a range of 1 to 50 μg / mL.

[0023] The CeO2 nanozyme disclosed in this invention exhibits excellent phosphate hydrolase-like activity, catalyzing the hydrolysis of monophosphates and removing phosphate groups from substrate molecules. Compared to redox nanozymes, one advantage of phosphate-hydrolyzing nanozymes is that they can directly provide a readable color signal (for example, hydrolyzing colorless methyl paraoxon to yellow p-nitrophenol (pNP) under alkaline conditions) without the need for additional labels or enzyme substrates. Because CeO2 nanozymes exhibit distinct hydrolysis kinetics for different p-nitrophenol pesticides, using reaction time as the sole variable can provide varying degrees of color signal for a variety of analytes. After pattern recognition processing using LDA and HCA, the color fingerprints of five commonly used p-nitrophenol pesticides (methyl paraoxon, ethyl paraoxon, methyl parathion, ethyl parathion, and fenitrothion) were well identified and quantified.

[0024] Hierarchical cluster analysis (HCA) is used to group data objects based on their characteristics, revealing the underlying structure and patterns of the data. Euclidean distance is often used as a distance metric between data points. A smaller Euclidean distance indicates greater similarity in the data.

[0025] Linear Discriminant Analysis (LDA) is a classic supervised learning method primarily used for dimensionality reduction and classification tasks. Its core idea is to project high-dimensional data into a low-dimensional space by finding a projection direction that keeps similar data as compact as possible and separates different data as much as possible.

[0026] Except for the single phosphate hydrolyzing nanozyme (CeO2), all other reagents used in the present invention are commercially available.

[0027] In this specification, the term "pNPP" is the abbreviation name of the compound "disodium 4-nitrophenylphosphate", and the two can be used interchangeably.

[0028] This study combines phosphate hydrolyzing nanoenzymes with a sensor array, proposing a kinetic-based hydrolysis strategy. Under identical reaction conditions, five common OPs analogs (methyl paraoxon, ethyl paraoxon, methyl parathion, ethyl parathion, and fenitrothion) exhibit distinct catalytic properties and kinetics. Using CeO2 nanoparticles as the sole sensing element, the catalytic hydrolysis reaction produces distinct color signals. This sensor array was then constructed, enabling the identification and quantification of nitrobenzene pesticides using linear discriminant analysis (LDA) and hierarchical cluster analysis (HCA).

[0029] Beneficial effects

[0030] The present invention utilizes the inherent kinetic differences when a single phosphate hydrolyzing nanozyme (CeO2) catalyzes the hydrolysis of different p-nitrophenol pesticides. By selecting multiple reaction time points to measure the absorbance changes, a time-resolved sensor array fingerprint is constructed. Combined with the pattern recognition algorithm (LDA / HCA), an organophosphorus hydrolyzing nanozyme encoding pattern driven by kinetic differences is constructed to identify the types and concentration quantification of various p-nitrophenol pesticides and their mixtures. The present invention only requires a single nanozyme material and a UV-visible spectrophotometer, without the need for complex labels and additional enzymes / substrates. The equipment requirements and operating procedures are relatively simple. The proposed method has good anti-interference performance and can effectively overcome the limitations of traditional enzyme inhibition methods and previously reported nanozyme sensors. In addition, the effectiveness of this method in real samples was verified, demonstrating its potential for high-throughput detection of multiple targets in complex matrices. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 UV spectrum of the nanozyme + disodium 4-nitrophenylphosphate system;

[0032] Figure 2 . Graph showing the absorbance of the reaction system at 405 nm versus time in the presence of five p-nitrophenol pesticides;

[0033] Figure 3 . Graph showing the sensor array's ability to discriminate between different concentrations of methyl paraoxon, where A: Linear discriminant analysis plot; B: Linear relationship between discriminant factor 1 and methyl paraoxon concentration.

[0034] Figure 4. Graphs showing the sensor array's ability to discriminate between different p-nitrophenol pesticides at 5 μg / mL. A: Linear discriminant analysis; B: Hierarchical cluster analysis.

[0035] Figure 5 Linear discriminant analysis plot of a multivariate mixture of p-nitrophenol pesticides;

[0036] Figure 6 A diagram showing the sensor array's ability to recognize various p-nitrophenol pesticides in the presence of biothiols, ascorbic acid, and metal ions.

[0037] Figure 7 . Graphs showing the effects of the sensor array on actual sample analysis, where A: discriminant graph for different p-nitrophenol pesticides at low concentrations in cabbage; B: discriminant graph for different p-nitrophenol pesticides at high concentrations in cabbage. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention will be described in detail below with reference to embodiments so that those skilled in the art can better understand the present invention, but the present invention is not limited to the following embodiments.

[0039] Example 1

[0040] Synthesis of phosphate hydrolyzing nanozymes and verification of their phosphatase-like activity

[0041] (1) Add 32 mL of 0.078 M NaOH solution and 434 mg (1 mmol) of Ce(NO3)3·6H2O to a 100 mL flat-bottom flask; place the mixture in air and stir at 700 rpm at room temperature for 22 h; collect the precipitate and wash it three times with deionized water and ethanol by centrifugation at 10,000 g for 5 min each; freeze-dry the washed precipitate to obtain CeO2 powder, which was dispersed in ultrapure water to prepare a solution with a concentration of 1 mg / mL for subsequent use;

[0042] (2) 800 μL of Tris-HCl buffer (50 mM, pH 8.0), 100 μL of CeO2 solution (1 mg / mL) and 100 μL of 4-nitrophenyl phosphate disodium (pNPP, 1 mM) were added to a 1.5 mL centrifuge tube, which was recorded as group 1 (experimental group); 900 μL of Tris-HCl buffer (50 mM, pH 8.0) and 100 μL of CeO2 solution (1 mg / mL) were added to a 1.5 mL centrifuge tube, which was recorded as group 2 (nanozyme control group); 900 μL of Tris-HCl buffer (50 mM, pH 8.0) and 100 μL of 4-nitrophenyl phosphate disodium (pNPP, 1 mM) were added to a 1.5 mL centrifuge tube, which was recorded as group 3 (substrate control group);

[0043] (3) Incubate each group for 30 min and record the spectrum at 300-500 nm using a UV-visible spectrophotometer.

[0044] Figure 1 The UV spectra of Tris-HCL buffer + CeO2 + pNPP, Tris-HCL buffer + CeO2 and Tris-HCL buffer + pNPP were recorded. It can be seen from the figure that the absorbance of Tris-HCL buffer + CeO2 + pNPP is the highest at 405nm, indicating that the synthesized CeO2 material has phosphatase-like activity and can effectively catalyze the hydrolysis of pNPP to produce p-nitrophenol (pNP).

[0045] Example 2

[0046] Effect of reaction time on the catalytic system of nanozymes + various p-nitrophenol pesticides

[0047] (1) Add 800 μL of Tris-HCl buffer (50 mM, pH 9.5), 100 μL of methyl paraoxon (100 μg / mL), and 100 μL of CeO2 (5 mg / mL) to a 1.5 mL centrifuge tube, and record the absorbance change at 405 nm for 60 min using a UV-visible spectrophotometer;

[0048] (2) Replace the p-nitrophenol pesticides with ethyl paraoxon, methyl parathion, ethyl parathion and cypermethrin, respectively. Other operations are the same as step (1).

[0049] The results are as follows Figure 2 Figure 4 shows the absorbance changes at 405 nm after the nanozyme-catalyzed system reacted with different p-nitrophenol pesticides for 60 minutes. The absorbance of the systems containing p-nitrophenol pesticides showed different upward trends over time, which is due to the different hydrolysis kinetics of CeO2 for different p-nitrophenol pesticides.

[0050] Based on these results, hierarchical cluster analysis (HCA) was used to analyze the absorbance curves of the five pesticides at 405 nm over time at different reaction times (e.g., measuring multiple time points at intervals). The Euclidean distance (or inter-class distance) between different pesticide types was calculated for any combination of two time points or for all time points. A number (three in this example) of characteristic time points that maximized the differentiation between different pesticide types (i.e., maximized inter-class distances or minimized intra-class distances) were selected for use in constructing a sensor array. The three time points with the largest Euclidean distances were selected to construct a time-resolved single-nanozyme sensor array.

[0051] Hierarchical cluster analysis (HCA) was implemented using Origin software. The specific steps are as follows: Open → Import relevant data → Select data → Statistics → Multivariate analysis → System cluster analysis → Open dialog box (modify parameters): Input - variable and observation value labels; Settings - Clustering method: Average, Distance type: Euclidean, Number of clusters: Number of sample categories; Output - Check the clustering stage, observation value and cluster center distance, centroid information → Output results.

[0052] The Euclidean distances of different p-nitrophenol pesticides at different reaction times were compared, and the time point that could maximize the distinction between different types of pesticides (e.g., the time point with a larger Euclidean distance) was selected as the characteristic reaction time of the sensor array.

[0053] Hierarchical cluster analysis yielded three distinct groups: one in which paraoxon and methyl parathion clustered together (10 min, 20 min), another in which methyl parathion and fenitrothion clustered together (30 min, 40 min), and a final group in which paraoxon and methyl parathion clustered together, while parathion and fenitrothion clustered together (50 min, 60 min). From these three groups, the reaction times with the largest Euclidean distances (20 min, 40 min, and 60 min) were selected as sensing variables for constructing a colorimetric sensor array.

[0054] Example 3

[0055] Time-resolved single-nanozyme sensor array for identification of p-nitrophenol pesticides of the same type at different concentrations

[0056] (1) Taking methyl paraoxon as an example, a series of concentrations of methyl paraoxon were prepared (the final concentrations in the system were 1 μg / mL, 5 μg / mL, 10 μg / mL, 20 μg / mL, 40 μg / mL, and 50 μg / mL, respectively). 800 μL of Tris-HCl buffer (50 mM, pH 9.5), 100 μL of a series of concentrations of methyl paraoxon, and 100 μL of 5 mg / mL CeO2 solution were added to a 1.5 mL centrifuge tube. Five parallel groups were made for each concentration. The absorbance at 405 nm was measured at 20, 40, and 60 min using a UV-visible spectrophotometer.

[0057] (2) Concentration identification of other para-nitrophenol pesticides: replace the series of concentrations of methyl paraoxon in step (1) with a series of concentrations of ethyl paraoxon, methyl parathion, ethyl parathion and cypermethrin, and keep other operations unchanged.

[0058] The results are as follows Figure 3As shown in A, the LDA results show that within the concentration range of 1 to 50 μg / mL, methyl paraoxon at each concentration was accurately distinguished without overlap. In addition, there was a strong linear correlation between the discrimination factor 1 (Factor 1) and the methyl paraoxon concentration ( Figure 3 B), indicating that this method can be used for the quantitative analysis of this pesticide.

[0059] Example 4

[0060] Time-resolved single-nanozyme sensor array for identification of different types of p-nitrophenol pesticides at the same concentration

[0061] (1) Taking the detection of 5 μg / mL of five p-nitrophenol pesticides (methyl paraoxon, ethyl paraoxon, methyl parathion, ethyl parathion and fenitrothion) as an example, add 800 μL of Tris-HCl buffer (50 mM, pH 9.5), 100 μL of 5 μg / mL of five p-nitrophenol pesticides (methyl paraoxon, ethyl paraoxon, methyl parathion, ethyl parathion and fenitrothion) and 100 μL of 5 mg / mL CeO2 solution to a 1.5 mL centrifuge tube. Make five parallel groups for each p-nitrophenol pesticide and measure the absorbance at 405 nm at 20, 40 and 60 min using a UV-visible spectrophotometer.

[0062] (2) Identification of p-nitrophenol pesticides at other concentrations: only the concentrations of various p-nitrophenol pesticides were changed so that the final concentrations in the system were 5 μg / mL, 10 μg / mL, 20 μg / mL, 40 μg / mL, and 50 μg / mL, respectively. Other operations were the same as step (1). That is, to verify the ability of the sensor array to distinguish pesticide types at different concentrations, five p-nitrophenol pesticide solutions with final concentrations of 5 μg / mL, 10 μg / mL, 20 μg / mL, 40 μg / mL, and 50 μg / mL were prepared according to the method in step (1) for detection (i.e., five parallel samples were set for each pesticide at each concentration), and the absorbance values at 405 nm were measured at 20, 40, and 60 min.

[0063] The results are as follows Figure 4 As shown in A, linear discriminant analysis (LDA) grouped the five similar points for each p-nitrophenol pesticide without any misclassification or error. Figure 4 The hierarchical cluster analysis (HCA) of B also showed the same analysis results.

[0064] The specific steps of Linear Discriminant Analysis (LDA): Open Origin software → Import relevant data → Select data → Statistics → Multivariate Analysis → Discriminant Analysis → Open dialog box (modify parameters): Input data - Training sample group: Select X column, Training sample: Select Y1-Yn Column; Settings - equal prior probability, linear discriminant function, typical discriminant analysis; Statistics - descriptive statistics; Output - typical score → output results.

[0065] Example 5

[0066] Time-resolved single-nanozyme sensor array for discrimination of multi-component mixtures

[0067] (1) Prepare a binary mixture of para-nitrophenol pesticides with a total concentration of 20 μg / mL (methyl paraoxon:ethyl paraoxon = 1:1, methyl paraoxon:ethyl paraoxon = 1:4, methyl paraoxon:ethyl paraoxon = 4:1, methyl parathion:cypermethrin = 1:1, methyl paraoxon:methyl parathion = 1:1, ethyl paraoxon:methyl parathion = 1:1);

[0068] (2) Add 800 μL of Tris-HCl buffer (50 mM, pH 9.5), 100 μL of each of the above multi-component mixtures, and 100 μL of 5 mg / mL CeO2 solution to a 1.5 mL centrifuge tube. Make five parallel sets of each concentration and measure the absorbance at 405 nm at 20, 40, and 60 min using a UV-visible spectrophotometer.

[0069] (3) Prepare ternary and quaternary mixtures of para-nitrophenol pesticides with a total concentration of 20 μg / mL (methyl paraoxon:ethyl paraoxon:methyl parathion = 1:2:1, methyl paraoxon:ethyl paraoxon:methyl parathion = 2:1:1, methyl paraoxon:ethyl paraoxon:methyl parathion = 3:1:1, methyl parathion:ethyl parathion:cypermethrin = 1:1:2, methyl parathion:ethyl parathion:cypermethrin = 3:1:1, methyl paraoxon:methyl parathion:cypermethrin = 1:1:2, ethyl paraoxon:methyl parathion:cypermethrin = 1:1:2, methyl paraoxon:ethyl parathion:cypermethrin = 1:2:1, methyl paraoxon:ethyl paraoxon:methyl parathion: Fenitrothion = 1:1:1:1, methyl paraoxon: ethyl paraoxon: methyl parathion: fenitrothion = 2:1:1:1, methyl paraoxon: ethyl paraoxon: methyl parathion: fenitrothion = 4:2:1:1, ethyl paraoxon: methyl parathion: ethyl parathion: fenitrothion = 1:2:3:2, ethyl paraoxon: methyl parathion: ethyl parathion: fenitrothion = 1:1:1:1), other operations are the same as step (2).

[0070] The results are as follows Figure 5As shown, a multivariate mixture of different p-nitrophenol pesticides was accurately identified by linear discriminant analysis (LDA) with an accuracy of 100%. This indicates that the time-resolved single nanoenzyme sensor array constructed by the present invention has excellent identification capabilities in complex systems. Specifically, a binary mixture of six p-nitrophenol pesticides in different proportions can be accurately identified ( Figure 5 A), including: methyl paraoxon:ethyl paraoxon = 1:1, methyl paraoxon:ethyl paraoxon = 1:4, methyl paraoxon:ethyl paraoxon = 4:1, methyl parathion: fenitrothion = 1:1, methyl paraoxon:methyl parathion = 1:1, ethyl paraoxon:methyl parathion = 1:1; ternary mixtures of eight different proportions of para-nitrophenol pesticides can be accurately identified ( Figure 5 B), including: methyl paraoxon:ethyl paraoxon:methyl parathion = 1:2:1, methyl paraoxon:ethyl paraoxon:methyl parathion = 2:1:1, methyl paraoxon:ethyl paraoxon:methyl parathion = 3:1:1, methyl parathion:ethyl parathion:fenthion = 1:1:2, methyl parathion:ethyl parathion:fenthion = 3:1:1, methyl paraoxon:methyl parathion:fenthion = 1:1:2, ethyl paraoxon:methyl parathion:fenthion = 1:1:2, methyl parathion:ethyl parathion:fenthion = 1:2:1; a quaternary mixture of five p-nitrophenol pesticides in different proportions could be accurately identified ( Figure 5 C), including: methyl paraoxon: ethyl paraoxon: methyl parathion: fenitrothion = 1:1:1:1, methyl paraoxon: ethyl paraoxon: methyl parathion: fenitrothion = 2:1:1:1, methyl paraoxon: ethyl paraoxon: methyl parathion: fenitrothion = 4:2:1:1, ethyl paraoxon: methyl parathion: ethyl parathion: fenitrothion = 1:2:3:2, ethyl paraoxon: methyl parathion: ethyl parathion: fenitrothion = 1:1:1:1.

[0071] Example 6

[0072] Evaluation of the Anti-interference Performance of Time-Resolved Single Nanoenzyme Sensor Arrays

[0073] (1) Add 700 μL of Tris-HCl buffer (50 mM, pH 9.5), 100 μL of 100 μg / mL of five para-nitrophenol pesticides (methyl paraoxon, ethyl paraoxon, methyl parathion, ethyl parathion, and fenitrothion), 100 μL of 50 ng / mL of biothiol, ascorbic acid, metal ions, and 100 μL of 5 mg / mL CeO2 solution to a 1.5 mL centrifuge tube. Set up five replicates for each pesticide under each interference condition. Measure the absorbance at 405 nm at 20, 40, and 60 min using a UV-visible spectrophotometer.

[0074] The results are as follows Figure 6 As shown in the figure, the LDA graph shows that the sensor array can still accurately identify various p-nitrophenol pesticides in the presence of interference from biothiols, ascorbic acid, and metal ions, indicating that the method has strong anti-interference ability. Specifically, the sensor array can eliminate the weak reducing interference of biothiols (including cysteine and glutathione) and accurately identify five p-nitrophenol pesticides ( Figure 6 A), and can also eliminate the strong reducing interference of ascorbic acid to accurately identify five kinds of p-nitrophenol pesticides ( Figure 6 B), can also eliminate different metal ions (including Ca 2+ Cr 3+ 、Fe 3+ 、Co 2+ 、Ni 2+ )'s oxidative interference to accurately identify five p-nitrophenol pesticides ( Figure 6 C).

[0075] Example 7

[0076] Detection of p-nitrophenol pesticides in cabbage using a time-resolved single-nanozyme sensor array

[0077] (1) Wash the cabbage repeatedly with deionized water, cut the washed cabbage into small pieces and place them in an appropriate amount of ethanol solution to homogenize and mash;

[0078] (2) After centrifugation (10,000 rpm, 5 min), the supernatant was collected and spiked with different proportions of p-nitrophenol pesticides to obtain the final test solution (divided into two groups, with the total concentration of p-nitrophenol pesticides controlled at 10 μg / mL and 50 μg / mL, respectively);

[0079] When spiking a single pesticide, the supernatant was collected after centrifugation (10,000 rpm, 5 min) and a single type of p-nitrophenol pesticide (methyl paraoxon, ethyl paraoxon, methyl parathion, ethyl parathion, or fenitrothion) was spiked into it to obtain two groups of final test solutions: the total concentration of p-nitrophenol pesticides in the first group was controlled at 10 μg / mL (i.e., each spiked solution contained one pesticide at a concentration of 10 μg / mL), and the total concentration of p-nitrophenol pesticides in the second group was controlled at 50 μg / mL (i.e., each spiked solution contained one pesticide at a concentration of 50 μg / mL);

[0080] When spiked with mixed pesticides, the supernatant was collected after centrifugation (10,000 rpm, 5 min) and p-nitrophenol pesticides were spiked into the supernatant at different ratios to obtain two groups of final test solutions: the total concentration of p-nitrophenol pesticides in the first group was controlled at 10 μg / mL (including methyl paraoxon, ethyl paraoxon, methyl parathion, methyl paraoxon:ethyl parathion = 1:1, ethyl paraoxon:fenthion = 1:1, and methyl paraoxon:ethyl paraoxon:ethyl parathion = 3:1:1); the total concentration of p-nitrophenol pesticides in the second group was controlled at 50 μg / mL (including methyl paraoxon, methyl parathion, ethyl parathion, methyl paraoxon:ethyl paraoxon = 1:1, ethyl paraoxon:fenthion = 1:1, and methyl paraoxon:ethyl paraoxon:fenthion = 3:1:1);

[0081] (3) Add 800 μL of Tris-HCl buffer (50 mM, pH 9.5), 100 μL of the test solution, and 100 μL of 5 mg / mL CeO2 solution to a 1.0 mL centrifuge tube. Measure the absorbance at 405 nm at 20 min, 40 min, and 60 min using a UV-visible spectrophotometer. Perform five replicates for each group.

[0082] The measurement results are as follows Figure 7 As shown in the figure, when the total content of p-nitrophenol pesticides in vegetables is low (total concentration of p-nitrophenol pesticides is 10 μg / mL), the sensor array can accurately identify different single p-nitrophenol pesticides (methyl paraoxon, ethyl paraoxon, methyl parathion), binary mixtures of p-nitrophenol pesticides with different ratios (methyl paraoxon: ethyl parathion = 1:1, ethyl paraoxon: fenitrothion = 1:1), and ternary mixtures of p-nitrophenol pesticides (methyl paraoxon: ethyl paraoxon: ethyl parathion = 3:1:1); Figure 7As shown in Figure B, when the total content of p-nitrophenol pesticides in vegetables is high (total p-nitrophenol pesticide concentration is 50 μg / mL), the sensor array can also accurately identify different single p-nitrophenol pesticides (methyl paraoxon, methyl parathion, ethyl parathion), binary mixtures of p-nitrophenol pesticides in different ratios (methyl paraoxon:ethyl paraoxon = 1:1, ethyl paraoxon:cyperthion = 1:1), and ternary mixtures of p-nitrophenol pesticides (methyl paraoxon:ethyl paraoxon:cyperthion = 3:1:1). In actual sample testing, the present invention can accurately identify different p-nitrophenol pesticides and their multi-component mixtures based on the p-nitrophenol pesticide content in vegetables.

[0083] In summary, the present invention combines machine learning technology to synthesize phosphate hydrolyzing nanozymes, using time as the only sensing unit. It does not require complex instruments and equipment and tedious operating steps, and can achieve low-cost analysis and high-accuracy detection of nitrophenol pesticides.

[0084] The above description is merely a preferred embodiment of the present invention and does not limit the scope of the present invention. Any equivalent structure or equivalent process transformation made by utilizing the present invention specification, or directly or indirectly applied to other related technical fields, is also included in the scope of the present invention.

Claims

1. A method for identifying and detecting p-nitrophenol pesticides based on a phosphate hydrolysis nanoenzyme sensor array, characterized in that: The steps include: (1) Detection of different concentrations of single p-nitrophenol pesticides: 100 μL of p-nitrophenol pesticides and 100 μL of 5 mg / mL CeO2 were added to 800 μL of 50 mM Tris-HCl buffer and mixed thoroughly to make the final concentrations of p-nitrophenol pesticides in the system 1 μg / mL, 5 μg / mL, 10 μg / mL, 20 μg / mL, 40 μg / mL, and 50 μg / mL, respectively. The mixture was incubated at 90°C for 1 h. The absorbance at 405 nm was measured at incubation times of 20 min, 40 min, and 60 min. Five parallel groups of experiments were performed for each p-nitrophenol pesticide to obtain a data matrix (3 reaction times × 6 concentrations × 5 parallel samples) to distinguish different concentrations of single p-nitrophenol pesticides. (2) Detection of different types of p-nitrophenol pesticides at the same concentration: 100 μL of 50 μg / mL p-nitrophenol pesticide and 100 μL of 5 mg / mL CeO2 were added to 800 μL of 50 mM Tris-HCl buffer and mixed thoroughly to make the final concentration of p-nitrophenol pesticide in the system 5 μg / mL. The system was incubated at 90°C for 1 h, and the absorbance at 405 nm was measured at incubation times of 20 min, 40 min, and 60 min. Five parallel groups were performed for each p-nitrophenol pesticide to obtain a data matrix (3 reaction times × 5 target substances × 5 parallel samples) to distinguish the five types of p-nitrophenol pesticides. (3) using the data matrix obtained in step (1) to establish a discriminant model for pesticides of different concentrations, and using the data matrix obtained in step (2) to establish a discriminant model for pesticides of different types, and analyzing the obtained data by hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA) to obtain the HCA spectrum and LDA score graph of the p-nitrophenol pesticide; (4) The samples to be tested were pretreated to obtain the corresponding vegetable extracts. After 20 min, 40 min, and 60 min of color development, the absorbance value at 405 nm was measured. Based on the established discrimination model, including the HCA spectrum and LDA score graph, the type and concentration of the p-nitrophenol pesticides in the samples to be tested can be identified and distinguished.

2. The method for identifying and detecting p-nitrophenol pesticides based on a phosphate hydrolyzing nanozyme sensor array according to claim 1, characterized in that: In step (1), the p-nitrophenol pesticide is any one of methyl paraoxon, ethyl paraoxon, methyl parathion, ethyl parathion and fenitrothion.

3. The method for identifying and detecting p-nitrophenol pesticides based on a phosphate hydrolyzing nanozyme sensor array according to claim 1, wherein: The pH value of the Tris-HCl buffer used in steps (1), (2) and (4) is 9.

5.

4. The method for identifying and detecting p-nitrophenol pesticides based on a phosphate hydrolyzing nanozyme sensor array according to claim 1, wherein: In step (4), the vegetable extract is an ethanol extract of cabbage.

5. The method for identifying and detecting p-nitrophenol pesticides based on a phosphate hydrolyzing nanozyme sensor array according to claim 4, characterized in that: In step (4), the detectable concentration range of the p-nitrophenol pesticide in the extract by the identification detection method is 1 to 50 μg / mL.

Images