Method for rapidly detecting pesticide residues by using colloidal gold technology

Abstract

The invention discloses a method for rapidly detecting pesticide residues by using a colloidal gold technology, and belongs to the technical field of pesticide residue detection.The method comprises the steps that an optimized colloidal gold detection card containing a multi-target antibody and modified by Raman signals is prepared, an agricultural product sample is homogenized, extracted, purified and then subjected to gradient dilution, the detection card is dropwise added for a chromogenic reaction, and the pesticide residues are detected. An image is collected through an annular LED cassette, features are extracted through intelligent image processing, and quantitative analysis is carried out in combination with an improved algorithm model. After stability modification, the detection card can simultaneously detect organophosphorus, pyrethroid and carbamate pesticides, the detection limit is less than or equal to 0.003 mg / kg, the detection limit change is less than or equal to 40% after the detection card is stored at 37 DEG C for one month, and the cross reaction rate is less than or equal to 10%. The whole process is shorter than or equal to 45 minutes, operation is convenient, interference resistance is high, and the method is suitable for on-site rapid quantitative detection and can meet the requirements of grassroots supervision, farmer market self-inspection and the like.

Description

Technical Field

[0001] This invention belongs to the field of pesticide residue detection technology, specifically relating to a method for rapid detection of pesticide residues using colloidal gold technology. It is applicable to the rapid quantitative detection of various pesticide residues such as organophosphates and pyrethroids in agricultural products such as fruits, vegetables, and grains. Background Technology

[0002] Pesticide residues are a key issue affecting the quality and safety of agricultural products. Among them, organophosphates and pyrethroids are widely used, making their residue detection a major concern. Existing methods for pesticide residue detection mainly include laboratory detection techniques such as gas chromatography and liquid chromatography, as well as rapid detection techniques such as colloidal gold immunochromatography.

[0003] While laboratory testing techniques offer high accuracy, they suffer from drawbacks such as expensive equipment, complex operation, long testing cycles, and reliance on specialized personnel, failing to meet the demands for rapid on-site testing. Existing colloidal gold detection technologies, though simple to operate and low-cost, still have significant limitations: some methods are only applicable to single pesticides (such as methyl isofenphos), resulting in a narrow scope of application; some rely on manual observation of colorimetric results, leading to poor quantitative accuracy and susceptibility to subjective factors; some detection cards lack sufficient sensitivity to detect low concentrations of residues; and image processing algorithms during the detection process are complex or lacking, failing to effectively eliminate interference factors, thus requiring improvements in detection reliability.

[0004] To address the shortcomings of the existing technologies, this invention proposes a rapid colloidal gold detection method that integrates optimized preparation of detection cards, gradient dilution, intelligent image processing, and automatic quantitative analysis, enabling rapid, accurate, and convenient detection of various pesticide residues. Summary of the Invention

[0005] The purpose of this invention is to provide a power supply for traction substations to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A method for rapid detection of pesticide residues using colloidal gold technology includes: S1. Preparation of an optimized colloidal gold detection card; S11: Preparation of colloidal gold solution: Colloidal gold particles with a particle size of 30-40 nm were synthesized by the trisodium citrate reduction method, and the absorbance OD520 was controlled to be 1.5-2.0. S12: Labeled antibody / antigen: Mix a mixture of various pesticide-specific antibodies with colloidal gold solution at a molar ratio of 1:10-1:20, adjust the pH to 8.0-9.0, and purify by gradient centrifugation to obtain colloidal gold-labeled probes; S13: Sample pad preparation: The glass fiber membrane is immersed in PBS buffer containing 0.5%-1% BSA, 0.05% Tween-20, 0.1% CHAPS and pH 7.4, and then dried to form a sample pad; S14: Binding pad preparation: Colloidal gold-labeled probes are sprayed onto a polyester fiber membrane and vacuum dried; simultaneously, Raman signal molecules (4-mercaptobenzoic acid) are modified on the probe surface to improve detection sensitivity; S15: Nitrocellulose membrane treatment: Parallel coating of the membrane with a detection line (T line) and a control line (C line), with the T line immobilizing a mixture of multiple pesticide conjugate proteins and the C line immobilizing anti-antibodies; S16: Chromatographic assembly: The sample pad, conjugate pad, nitrocellulose membrane and absorbent pad are overlapped by 2mm and pasted onto the PVC backing plate, and then cut into detection card units.

[0007] S2, Sample pretreatment and gradient dilution; S21: Take the edible part of the agricultural product to be tested, wash it clean, air dry it naturally, chop it up, and homogenize it to obtain a homogenate. S22: Add 3-5 times the volume of extraction solvent (acetonitrile, methanol or acetone) to the homogenate, mix thoroughly, let stand, and take the supernatant; S23: Take 5 portions of 10g supernatant and add 10, 50, 100, 500, and 1000 times the diluent (petroleum ether, n-hexane, or benzene) respectively, and mix them evenly to obtain five sets of diluted solutions.

[0008] S3, Colorimetric Reaction and Image Acquisition S31: Take 80 μL of each dilution solution and add it to the detection port of the optimized colloidal gold detection card. React at room temperature for 10-15 minutes. S32: Place the reacted test card in a dark box with a ring LED light source, and capture a color image through the top camera. The image resolution is set to 640×480 pixels in RGB color mode.

[0009] S4, Intelligent Image Processing and Feature Extraction; S41: Image preprocessing: Perform grayscale processing on the acquired RGB image to remove background noise; S42: Image segmentation: A simplified improved remora optimization algorithm is used to generate candidate segmentation thresholds and perform boundary correction. The optimal threshold is selected by combining a greedy strategy to segment the T-line and C-line regions and determine their boundary positions. S43: Feature Extraction: Calculate the color feature data of the T-line and C-line by averaging the RGB channels, use Euclidean distance to measure color similarity, and combine Raman signal intensity data to form a comprehensive feature set.

[0010] S5. Automatic quantitative analysis and result output; S51: Input the comprehensive feature set into the preset concentration analysis model (generated based on the improved NSGA-Ⅱ algorithm). S52: The model calculates the precise concentration of pesticide residues in agricultural products by comparing the characteristic data corresponding to different dilutions with the preset standard curve. S53: Output the detection results via smart terminal, including whether the standard is exceeded and the specific concentration value.

[0011] As a further technical solution of the present invention, in step S1, the colloidal gold solution is synthesized by the reduction method of trisodium citrate, with a particle size of 30-40 nm and an absorbance OD520 of 1.5-2.0.

[0012] As a further technical solution of the present invention, in step S1, the molar ratio of antibody to colloidal gold is 1:10-1:20, and after labeling, it is purified by gradient centrifugation and the pH is adjusted to 8.0-9.0.

[0013] As a further technical solution of the present invention, in step S1, the blocking solution for sample pad treatment contains 0.5%-1% BSA, 0.05% Tween-20, 0.1% CHAPS and PBS buffer at pH 7.4.

[0014] As a further technical solution of the present invention, in step S2, the extraction solvent is one of acetonitrile, methanol or acetone, and the dilution solvent is one of petroleum ether, n-hexane or benzene, with gradient dilution ratios of 10 times, 50 times, 100 times, 500 times and 1000 times.

[0015] As a further technical solution of the present invention, in step S3, the color development reaction time is 10-15 minutes, the dark box is equipped with a ring LED light source, the image acquisition resolution is 640×480 pixels, and the color mode is RGB.

[0016] As a further technical solution of the present invention, the test card can simultaneously detect organophosphate and pyrethroid pesticide residues.

[0017] As a further technical solution of the present invention, the Raman signal molecule is 4-mercaptobenzoic acid, and the modification method is as follows: take 1-2 ml of colloidal gold-labeled probe, add 1 μL of 1 mmol / L 4-mercaptobenzoic acid, shake at room temperature, centrifuge at high speed, remove the supernatant and resuspend.

[0018] Compared with the prior art, the beneficial effects of the present invention are: High multi-target specificity: Through antibody compatibility optimization, the cross-reactivity rate to chlorpyrifos, cypermethrin, and carbofuran is all <5%, and there is no cross-reactivity to unrelated pesticides; Excellent stability: After the test card is stored at 4℃ for 6 months, 25℃ for 3 months, and 37℃ for 1 month, the detection limit changes by ≤10% (while the detection limit of traditional test cards increases by more than 50% after 1 month of storage at 37℃). High quantitative accuracy: The detection limits for chlorpyrifos, cypermethrin, and carbofuran are 0.002 mg / kg, 0.003 mg / kg, and 0.001 mg / kg, respectively, all of which are lower than the national standard limits; Strong anti-interference capability: The image processing algorithm improves the anti-interference rate of sample pad impurities and light source fluctuations by 40%, and the detection error of complex matrices is <8%; More convenient to operate: pre-processing time is reduced to 30 minutes, and the entire detection process (including result output) takes ≤45 minutes. Attached Figure Description

[0019] Figure 1 This is an overall flowchart of the detection method of the present invention; Figure 2 This is a structural diagram of a test card that indicates the material and dimensions of each component. Figure 3 This is a comparison chart of stability test results; Figure 4 The bar chart shows the results of the specificity verification. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are merely one circuit embodiment of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Please see Figure 1-4 As shown, a method for rapid detection of pesticide residues using colloidal gold technology includes the following steps: S1. Preparation of an optimized multi-target colloidal gold detection card; S11: Preparation of colloidal gold solution: Colloidal gold particles with a particle size of 32-38 nm were synthesized by trisodium citrate-tannic acid composite reduction method, and the absorbance OD520 was controlled to be 1.6-1.9. The solution was stored at 4℃ in the dark for later use. S12: Multi-target antibody labeling: Anti-chlorpyrifos monoclonal antibody, anti-cypermethrin monoclonal antibody, and anti-carbofuran monoclonal antibody were mixed at a volume ratio of 2:1.5:1, and mixed with colloidal gold solution at a molar ratio of 1:12-1:18. The pH was adjusted to 8.2-8.6 with 0.1 mol / L K2CO3, and 1% BSA was added for blocking for 30 minutes. After centrifugation at 10000 rpm for 15 min (the upper 50% of the supernatant was discarded, and the lower precipitate was resuspended) the colloidal gold-labeled probe was obtained. S13: Raman signal molecule modification: Add 1 μL of 1 mmol / L 4-mercaptobenzoic acid to the labeled probe, shake at room temperature for 40 minutes, centrifuge at 12000 rpm for 20 minutes, remove the supernatant and resuspend in PBS buffer containing 0.02% NaN3 to obtain colloidal gold-MBA-multi-antibody composite probe. S14: Sample pad stability modification: The glass fiber membrane was impregnated in PBS buffer (pH 7.4) containing 0.8% BSA, 0.05% Tween-20, 0.12% CHAPS and 0.03% sorbitol, and then vacuum dried at 37°C for 2 hours; S15: Nitrocellulose membrane treatment: Coating the NC membrane using a "segmented spraying method": Detection line (T line): Chlorpyrifos-BSA conjugate (0.7 mg / mL), cypermethrin-BSA conjugate (0.6 mg / mL), and carbofuran-BSA conjugate (0.8 mg / mL) are mixed in a 1:1:1 ratio, and the spraying amount is 1 μL / cm. Quality control line (C line): Goat anti-mouse IgG antibody, spraying amount is 1.2 μL / cm; After drying at 37°C for 3 hours, place in a desiccator containing 50% RH for 12 hours to equilibrate. S16: Chromatography Assembly: The sample pad, conjugate pad, NC membrane, and absorbent pad are overlapped by 2.0±0.1mm and pasted onto the PVC backing. The sample pad, conjugate pad, NC membrane, and absorbent pad are cut into 5.0±0.1mm wide detection card units and vacuum-packed with aluminum foil (including desiccant).

[0022] S2. Sample pretreatment and gradient dilution (optimizing extraction efficiency). S21: Sample homogenization: Take 20g of the edible part of the agricultural product, add 5mL of deionized water, and homogenize with a high-speed homogenizer for 2 minutes to obtain a homogenate. S22: Extraction: Add 4 times the volume of acetonitrile to the homogenate, vortex mix for 3 minutes, centrifuge at 4000 rpm for 10 minutes, and take the supernatant; S23: Purification and dilution: Take 5 mL of the supernatant, add 3 mL of petroleum ether, shake for 1 minute and let stand to separate the layers, take the lower layer of aqueous phase; take 5 10 g portions of the aqueous phase and dilute them with petroleum ether by 10 times, 50 times, 100 times, 500 times and 1000 times respectively to obtain five sets of diluents (each set of diluents needs to be filtered through a 0.22 μm organic phase filter membrane to remove impurities).

[0023] S3, colorimetric reaction and standardized image acquisition; S31: Reaction: Take 80 μL of each dilution solution and add it vertically to the sample well of the test card. React at room temperature (20-25℃) and humidity (40%-60%RH) for 12 ± 1 minutes (reaction time fluctuation ≤5%). S32: Image Acquisition: Place the detection card into the "ring-shaped LED light source dark box" (light source wavelength 520nm, illuminance 500±20lux), and the camera at the top of the dark box (resolution 12 million pixels, fixed focal length) takes a vertical picture. The image format is JPG and it is stored as RGB three-color channel data.

[0024] S4, anti-interference intelligent image processing; S41: Preprocessing: Perform "Gaussian filtering (kernelsize=3×3) + white balance correction" on the RGB image to remove background noise and light source color cast; S42: Image segmentation: using an improved remora optimization algorithm + adaptive threshold correction. Generate 50 candidate segmentation thresholds and calculate the inter-class variance (fitness value) for each threshold. The initial optimal threshold is corrected by ±3 boundary values, and an inverse threshold is generated by combining the lens imaging inverse learning strategy (scaling factor K=1.2). A greedy strategy was used to select the optimal threshold, and the regions of T-line (width 1.0±0.1mm) and C-line (width 1.0±0.1mm) were segmented. S43: Feature Extraction Color characteristics: Calculate the RGB mean values ​​of the T-line and C-line, and convert them to saturation in the HSV color space; Raman characteristics: The Raman peak intensity (wavelength 1580 cm⁻¹, corresponding to the MBA characteristic peak) in the T-line region was collected using the built-in Raman spectroscopy module in the dark box. Comprehensive feature set.

[0025] S5. Multi-model fusion quantitative analysis; S51: Model Construction: Based on the improved NSGA-II algorithm, combined with "Support Vector Regression (SVR)," a quantitative model of "comprehensive feature-concentration" for multiple types of pesticides was trained (the training set consisted of 100 sets of pesticide standard data with known concentrations). S52: Concentration Calculation: Input the comprehensive feature set of the sample to be tested into the model and output the residue concentration of each pesticide (if the concentration of a pesticide is <0.001mg / kg, it is judged as "not detected"). S53: Result output: The smart terminal (mobile phone / tablet) displays "pesticide type - concentration - whether it exceeds the standard" and generates a test report (including image, time and location). Detailed Implementation

[0026] I. Experimental Materials and Equipment Materials: Vegetables and fruits to be tested (such as cabbage and apples), chloroauric acid (HAuCl4), trisodium citrate, various pesticide-specific antibodies (anti-chlorpyrifos, anti-cypermethrin, etc.), 4-mercaptobenzoic acid, BSA, Tween-20, CHAPS, PBS buffer, acetonitrile, petroleum ether, nitrocellulose membrane, glass fiber membrane, PVC backing, etc. Equipment: homogenizer, high-speed centrifuge, vacuum drying oven, ring LED light source dark box, camera, smart terminal (equipped with self-developed concentration analysis software), and cutting equipment.

[0027] II. Preparation of Detection Cards Preparation of colloidal gold solution: Boil 100 ml of 0.01% HAuCl4 solution, add 1.5 ml of 1% trisodium citrate, react for 15 minutes, and after cooling, the particle size is measured to be 35 nm and the OD520 is 1.8. Preparation of colloidal gold labeled probes: 10 μg of a mixture of antibodies against multiple pesticides was added to 1 ml of colloidal gold solution, the pH was adjusted to 8.5, and the mixture was centrifuged at 12000 rpm for 20 minutes to obtain the labeled probes after purification; 1 μL of 1 mmol / L 4-mercaptobenzoic acid was added to the probes, the mixture was shaken at room temperature, centrifuged at high speed, and then resuspended in deionized water to complete the Raman signal modification. Sample pad treatment: Immerse the glass fiber membrane in pH 7.4 PBS buffer containing 1% BSA, 0.05% Tween-20, and 0.1% CHAPS, and dry at 37°C; Nitrocellulose membrane treatment: Chlorpyrifos-BSA conjugate and cypermethrin-BSA conjugate were mixed at a 1:1 ratio (concentration 0.8 mg / ml) and sprayed onto the T line of the NC membrane; anti-mouse antibody (concentration 1.0 mg / ml) was sprayed onto the C line. Assembly: The sample pad, conjugate pad, nitrocellulose membrane, and absorbent pad are sequentially attached to the PVC backing and cut into 5mm wide test card units.

[0028] III. Sample Testing Process Sample pretreatment: Take 100g of cabbage sample, wash and dry it, then chop it and homogenize it; add 300ml of acetonitrile, mix and let stand for 30 minutes, then take the supernatant; Serial dilution: Take 5 portions of 10g supernatant and add 100g, 500g, 1000g, 5000g, and 10000g of petroleum ether respectively, and mix well to obtain 10x, 50x, 100x, 500x, and 1000x dilutions; Colorimetric reaction: Take 80 μL of each dilution solution, add it to the detection port of the detection card, and react at room temperature for 12 minutes; Image acquisition: Place the detection card into the dark box, turn on the ring LED light source, capture the color image through the camera and transmit it to the smart terminal; Intelligent analysis: The terminal software performs grayscale processing and threshold segmentation on the image, extracts the RGB features and Raman signal intensity of the T and C lines, inputs them into the concentration analysis model, and outputs the detection results within 30 seconds: the residual concentration of chlorpyrifos in the cabbage is 0.003 mg / kg, which does not exceed the standard.

[0029] IV. Verification of Test Results The same cabbage sample was compared and detected using the method of this invention and gas chromatography. The results showed that the detection error of the two methods was less than 5%, proving the accuracy of the method of this invention.

[0030] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0031] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for rapid detection of pesticide residues using colloidal gold technology, characterized in that, include: S1. Preparation of an optimized colloidal gold detection card; S11: Preparation of colloidal gold solution; S12: Labeled antibody / antigen: Mix a mixture of pesticide-specific antibodies with colloidal gold solution in molar, and purify by gradient centrifugation to obtain colloidal gold labeled probe; S13: Sample pad preparation: The glass fiber membrane is immersed in PBS buffer and dried to form a sample pad; S14: Binding pad preparation: Colloidal gold-labeled probes are sprayed onto a polyester fiber membrane and vacuum dried; simultaneously, Raman signal molecules are modified on the probe surface to improve detection sensitivity; S15: Nitrocellulose membrane treatment: Parallel coating of the membrane with a detection line and a control line, the detection line is defined as the T line, the control line is defined as the C line, the T line is fixed with a mixture of multiple pesticide conjugate proteins, and the C line is fixed with anti-antibodies; S16: Chromatographic assembly: The sample pad, conjugation pad, nitrocellulose membrane and absorbent pad are overlapped by 2mm and pasted onto the PVC backing plate, and then cut into detection card units; S2, Sample pretreatment and gradient dilution; S21: Take the edible part of the agricultural product to be tested, wash it clean, air dry it naturally, chop it up, and homogenize it to obtain a homogenate. S22: Add 3-5 times the volume of extraction solvent to the homogenate, mix thoroughly, let stand, and take the supernatant; S23: Take 5 portions of 10g supernatant, add 10 times, 50 times, 100 times, 500 times, and 1000 times the dilution solvent, mix well to obtain five sets of dilutions; S3, Colorimetric reaction and image acquisition; S31: Take 80 μL of each dilution solution and add it to the detection port of the optimized colloidal gold detection card. React at room temperature for 10-15 minutes. S32: Place the reacted test card in a dark box with a ring LED light source and capture an RGB image using the top camera; S4, Intelligent Image Processing and Feature Extraction; S41: Image preprocessing: Perform grayscale processing on the acquired RGB image to remove background noise; S42: Image segmentation: A simplified improved remora optimization algorithm is used to generate candidate segmentation thresholds and perform boundary correction. The optimal threshold is selected by combining a greedy strategy to segment the T-line and C-line regions and determine their boundary positions. S43: Feature Extraction: Calculate the color feature data of T-line and C-line by averaging the RGB channels, use Euclidean distance to measure color similarity, and combine Raman signal intensity data to form a comprehensive feature set; S5. Quantitative analysis and result output; S51: Input the comprehensive feature set into the preset concentration analysis model, which is generated based on the improved NSGA-Ⅱ algorithm; S52: The model calculates the precise concentration of pesticide residues in agricultural products by comparing the characteristic data corresponding to different dilutions with the preset standard curve. S53: Output the detection results via smart terminal, including whether the standard is exceeded and the specific concentration value.

2. The method for rapid detection of pesticide residues using colloidal gold technology according to claim 1, characterized in that, In step S1, the colloidal gold solution is synthesized by the reduction method of trisodium citrate, with a particle size of 30-40 nm and an absorbance OD520 of 1.5-2.

0.

3. The method for rapid detection of pesticide residues using colloidal gold technology according to claim 1, characterized in that, In step S1, the molar ratio of antibody to colloidal gold is 1:10-1:

20. After labeling, the antibody is purified by gradient centrifugation and the pH is adjusted to 8.0-9.

0.

4. The method for rapid detection of pesticide residues using colloidal gold technology according to claim 1, characterized in that, In step S1, the blocking solution for sample pad treatment contains 0.5%-1% BSA, 0.05% Tween-20, 0.1% CHAPS and PBS buffer at pH 7.

4.

5. The method for rapid detection of pesticide residues using colloidal gold technology according to claim 1, characterized in that, In step S2, the extraction solvent is one of acetonitrile, methanol or acetone, and the dilution solvent is one of petroleum ether, n-hexane or benzene.

6. The method for rapid detection of pesticide residues using colloidal gold technology according to claim 1, characterized in that, In step S3, the color development reaction time is 10-15 minutes, the dark box is equipped with a ring LED light source, the image acquisition resolution is 640×480 pixels, and the color mode is RGB.

7. The method for rapid detection of pesticide residues using colloidal gold technology according to claim 1, characterized in that, The Raman signal molecule is 4-mercaptobenzoic acid. The modification method is as follows: take 1-2 ml of colloidal gold-labeled probe, add 1 μL of 1 mmol / L 4-mercaptobenzoic acid, shake at room temperature, centrifuge at high speed, remove the supernatant and resuspend.

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