Pesticide residue detection method based on ultraviolet resonance Raman

Abstract

The invention discloses a pesticide residue detection method based on ultraviolet resonance Raman, which adopts an ultraviolet resonance Raman technology, utilizes ultraviolet light to excite specific chromophores or aromatic compounds in pesticide molecules based on a Raman scattering effect to generate a resonance effect so as to enhance a Raman signal; and then, identifying types and structures of pesticide molecules by analyzing characteristic peaks and vibration modes of the Raman spectrum. The method has the advantages of in-situ and nondestructive detection, high detection sensitivity, weak background fluorescence interference and the like during pesticide residue detection.

Description

Technical Field

[0001] The invention relates to a food pesticide residue detection technology, in particular to a pesticide residue detection method based on ultraviolet resonance Raman. Background Art

[0002] At present, food safety has always been a hot social issue that people are widely concerned about. With the rapid development of modern agriculture, the problem of pesticide residues in food has become increasingly prominent. Pesticide pollution has become one of the main hidden dangers in the field of food safety, causing serious harm to human health and the ecological environment. Therefore, strengthening the detection of food pesticide pollution has become a top priority in current food safety management.

[0003] At present, there are three main methods for detecting pesticide residues in fruits and vegetables: chromatography, mass spectrometry and spectroscopy. Among them, spectral detection technology is a commonly used non-destructive detection technology. This technology is based on the spectral characteristics of different structures of pesticide molecules and combines with chemometric methods to detect whether agricultural products have pesticide residues. With the continuous development of spectral detection technology, more and more scholars have begun to apply spectral technology to the research of pesticide residue detection.

[0004] At present, the application of spectral technology in pesticide residue detection mainly includes near-infrared spectroscopy, hyperspectral imaging, Raman spectroscopy, and fluorescence spectroscopy. Spectral technology has high accuracy and sensitivity in pesticide residue detection, and plays an extremely important role in qualitative and quantitative detection of fresh fruits for pesticide residues. It has broad application prospects and will be more and more widely used in the field of pesticide residue detection.

[0005] Raman spectroscopy is a non-destructive detection method based on molecular vibration. It can detect pesticide residues by analyzing Raman spectral images in samples. However, compared with high-infrared spectroscopy and near-infrared spectroscopy, the signal intensity of the spectrum is weak, and it is easily interfered by fluorescence. It is also sensitive to the interference of the sample's surrounding environment. Factors such as moisture and impurities may affect the accuracy and reliability of the detection results, and it is necessary to rely on enhanced substrates to improve the spectral signal.

[0006] Prior art 1:

[0007] Raman scattering (RS) refers to the phenomenon that when monochromatic light is irradiated on a substance, the scattered light contains components with lower and higher frequencies than the incident light in addition to the elastic component with the same frequency as the incident light. Surface enhanced Raman scattering (SERS) is the phenomenon that the Raman signal of molecules is greatly enhanced near the rough surface of certain metals such as gold and silver.

[0008] like Figure 1As shown in the figure, the process of pesticide residue detection based on surface enhanced Raman spectroscopy (SERS) can be summarized into the following main steps:

[0009] (1) Pre-treatment to extract pesticide residues, including sample collection, sample processing, extraction of pesticide residues, enrichment and purification.

[0010] (2) SERS substrate preparation: select the appropriate SERS substrate material according to the detection requirements and make necessary modifications to the substrate to improve its adsorption capacity and detection sensitivity.

[0011] (3) Performing SERS detection, the enriched and purified pesticide residue sample is dripped onto the SERS substrate, and the Raman spectrum of the sample is collected using a surface enhanced Raman spectrometer. The collected spectrum is analyzed, including characteristic peak identification, intensity comparison, etc.

[0012] (4) Data processing and result determination: The collected spectral data are preprocessed, such as denoising and baseline correction, and feature information is extracted from the preprocessed spectral data. The extracted feature information is matched with the known pesticide residue spectral database. Finally, the presence of pesticide residues in the sample, as well as the type and concentration of the residues, are determined based on the matching results.

[0013] Disadvantages of prior art 1:

[0014] Surface enhanced Raman technology has the following disadvantages in the detection of pesticide residues:

[0015] 1. Signal-to-noise ratio and interference factors

[0016] The signal enhancement of SERS technology relies on the surface enhancement effect of metal nanoparticles (such as Au, Ag, etc.), which can significantly increase the intensity of Raman signals, but may also introduce additional noise, resulting in a relatively low signal-to-noise ratio, which may affect the accuracy and reliability of detection.

[0017] The stability of SERS signals is affected by many factors. By using nanoparticles as an enhanced substrate, the SERS technology can enhance the Raman signal intensity by 4-11 orders of magnitude. However, its signal enhancement depends on the interaction between nanoparticles and the molecules to be tested, which may be affected by factors such as nanoparticle morphology, distribution, and molecular adsorption. These factors may lead to the instability of SERS signals, thereby affecting the accuracy of the detection results.

[0018] 2. Preparation and Reproducibility

[0019] SERS technology requires the preparation of high-quality and uniform metal nanoparticles as enhanced substrates, which usually involves complex chemical synthesis and characterization processes. The quality of substrate preparation directly affects the intensity and stability of SERS signals.

[0020] Since the enhancement of SERS signal depends on the "hot spot" effect between metal nanoparticles, and the distribution and stability of "hot spots" may be affected by many factors, the reproducibility of SERS technology is relatively poor, which may lead to differences in detection results obtained at different times or under different experimental conditions.

[0021] 3. Matrix effect and scope of application

[0022] When detecting pesticide residues in complex matrices (such as soil, food, and fruits and vegetables), SERS technology may be interfered by other compounds in the matrix, resulting in the accuracy of the detection results being affected. This matrix effect may limit the applicability of SERS technology in certain application scenarios.

[0023] Although SERS technology has the advantages of high sensitivity and high selectivity in pesticide residue detection, its application scope may be relatively limited due to its preparation complexity and reproducibility challenges, as well as the influence of matrix effects. In some specific cases, it may be necessary to combine other detection technologies to obtain more accurate detection results.

[0024] In summary, surface-enhanced Raman spectroscopy has some disadvantages in the detection of pesticide residues, which are specifically manifested in signal-to-noise ratio and interference factors, preparation and reproducibility, matrix effect and scope of application. Therefore, when choosing a detection technology, it is necessary to weigh and choose according to the specific application scenarios and needs.

[0025] Prior art 2:

[0026] Fluorescence spectroscopy is an important method for studying the spectral characteristics of substances in an excited state. Its principle is mainly based on the fluorescence effect. Fluorescence is the process of a substance absorbing electromagnetic radiation and then being excited. The excited atoms or molecules re-emit radiation with the same or different wavelength as the excitation radiation during the de-excitation process. When the excitation light source stops irradiating the sample, the re-emission process stops immediately. This re-emitted light is called fluorescence. Fluorescence spectroscopy is to draw a fluorescence spectrum by detecting the wavelength and intensity of the fluorescent light emitted by the substance in an excited state, and then analyze the properties and characteristics of the sample.

[0027] like Figure 2 As shown in the figure, the process of pesticide residue detection based on fluorescence spectroscopy can be summarized into the following key steps:

[0028] (1) Sample preparation and pretreatment, including sample collection, sample cleaning, sample homogenization, and extraction of pesticide residues.

[0029] (2) Perform instrument inspection on the fluorescence spectrometer to ensure that it is operating normally and that components such as the light source and detector are working stably. Set the optimal excitation wavelength, emission wavelength range, scanning speed and other parameters of the fluorescence spectrometer according to the properties of the pesticide to be tested and the detection requirements.

[0030] (3) Conduct fluorescence spectrum detection, add the extracted pesticide residue sample to the sample pool of the fluorescence spectrometer, or use an appropriate method to mix the sample with a fluorescent reagent and then conduct detection, use a fluorescence spectrometer to collect the fluorescence spectrum data of the sample, and pre-process the collected fluorescence spectrum data, such as denoising, baseline correction, etc., to improve the accuracy and reliability of the data.

[0031] (4) Finally, data analysis and result judgment are performed. According to the fluorescence characteristics of the pesticide, the characteristic peaks in the fluorescence spectrum are identified. By comparing the intensity of the characteristic peaks with the fluorescence spectrum of the pesticide standard with known concentration, the residual concentration of the pesticide in the sample is calculated.

[0032] Disadvantages of the second prior art:

[0033] Fluorescence spectroscopy has the following disadvantages in the detection of pesticide residues:

[0034] 1. Relatively weak selectivity

[0035] Fluorescence spectroscopy is mainly based on the fluorescence phenomenon produced by substances under the excitation of light of a specific wavelength. However, in practical applications, multiple pesticide residues may have similar fluorescence characteristics, which leads to certain difficulties in distinguishing different types of pesticide residues by fluorescence spectroscopy, which limits its application in some complex samples.

[0036] 2. Susceptible to interference substances

[0037] During the detection process of fluorescence spectroscopy technology, there may be interference from other fluorescent substances in the sample, which may affect the accuracy of the test results. In particular, when there are substances with similar fluorescent characteristics to pesticide residues in the sample, false detection or missed detection may occur.

[0038] 3. Sensitivity needs to be improved

[0039] Although fluorescence spectroscopy has high sensitivity, when detecting extremely low concentrations of pesticide residues, the fluorescence signal may be affected by other components in the sample (such as scattering particles, color interferents, etc.), resulting in high background fluorescence, and its sensitivity may not meet the requirements.

[0040] 4. Sample preparation is complicated

[0041] When fluorescence spectroscopy is used to detect pesticide residues, it is usually necessary to perform a relatively complex pretreatment and preparation process on the sample, such as grinding, extraction, purification, etc. This not only increases the time cost of detection, but its detection efficiency may also be affected by sample preparation and instrument operation.

[0042] In summary, fluorescence spectroscopy technology has some disadvantages in pesticide residue detection, which are specifically manifested in selectivity, interference factors, sensitivity, sample preparation, etc. Therefore, when choosing a detection technology, it is necessary to weigh and choose according to the specific application scenarios and needs.

[0043] In view of this, the present invention is proposed. Summary of the invention

[0044] The purpose of the present invention is to provide a method for detecting pesticide residues based on ultraviolet resonance Raman to solve the above technical problems existing in the prior art.

[0045] The objective of the present invention is achieved through the following technical solutions:

[0046] The pesticide residue detection method based on ultraviolet resonance Raman of the present invention adopts ultraviolet resonance Raman technology, based on the Raman scattering effect, and uses ultraviolet light to excite specific chromophores or aromatic compounds in pesticide molecules to produce a resonance effect, thereby enhancing the Raman signal;

[0047] Then, the type and structure of the pesticide molecules are identified by analyzing the characteristic peaks and vibration modes of the Raman spectrum.

[0048] Compared with the prior art, the pesticide residue detection method based on ultraviolet resonance Raman provided by the present invention has the advantages of in-situ and non-destructive detection, high detection sensitivity, and weak background fluorescence interference when detecting pesticide residues. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a simplified flow chart of the prior art 1.

[0050] Figure 2 It is a simplified flow chart of the second prior art.

[0051] Figure 3 A simplified schematic diagram of the process of pesticide residue detection based on UV resonance Raman provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention; it is obvious that the described embodiments are only part of the embodiments of the present invention, not all of the embodiments, which does not constitute a limitation of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the protection scope of the present invention.

[0053] First, the terms that may be used in this article are explained as follows:

[0054] The term “and / or” means that either or both of them can be realized at the same time. For example, X and / or Y means both “X” or “Y” and “X and Y”.

[0055] The terms "include", "comprises", "contains", "has" or other descriptions with similar semantics should be interpreted as non-exclusive inclusion. For example, including certain technical feature elements (such as raw materials, components, ingredients, carriers, dosage forms, materials, dimensions, parts, components, mechanisms, devices, steps, procedures, methods, reaction conditions, processing conditions, parameters, algorithms, signals, data, products or products, etc.) should be interpreted as including not only certain technical feature elements explicitly listed, but also other technical feature elements known in the art that are not explicitly listed.

[0056] The term "consisting of..." means excluding any technical feature elements not explicitly listed. If this term is used in a claim, it will make the claim closed, so that it does not contain technical feature elements other than the technical feature elements explicitly listed, except for the conventional impurities related to them. If this term only appears in a clause of a claim, it only limits the elements explicitly listed in the clause, and the elements recorded in other clauses are not excluded from the overall claim.

[0057] The contents not described in detail in the examples of the present invention belong to the prior art known to professionals in the field. If no specific conditions are specified in the examples of the present invention, the conditions are carried out according to the conventional conditions in the field or the conditions recommended by the manufacturer. If the manufacturer is not specified in the reagents or instruments used in the examples of the present invention, they are all conventional products that can be purchased commercially.

[0058] The pesticide residue detection method based on ultraviolet resonance Raman of the present invention adopts ultraviolet resonance Raman technology, based on the Raman scattering effect, and uses ultraviolet light to excite specific chromophores or aromatic compounds in pesticide molecules to produce a resonance effect, thereby enhancing the Raman signal;

[0059] Then, the type and structure of the pesticide molecules are identified by analyzing the characteristic peaks and vibration modes of the Raman spectrum.

[0060] Includes steps:

[0061] A. Sample preparation:

[0062] The pesticide preparation to be tested is diluted with ultrapure water to prepare three solutions with different concentrations as standard solutions;

[0063] The pesticide preparation to be tested is diluted with ultrapure water to prepare solutions of various concentrations as comparative samples;

[0064] The plant materials to be tested were washed with ultrapure water, and each group of samples was soaked in three different concentrations of pesticide standard solutions, and then taken out and drained;

[0065] B. Instrument Setup and Calibration:

[0066] 1) Instrument selection: Select wavelength range 190~520nm, spectral range 350~4000cm -1 , UV resonance Raman spectroscopy detection system with a spectral resolution of 0.08nm;

[0067] 2) Parameter setting: According to the characteristics of the pesticide molecules to be tested and the properties of the sample, set the following parameters: excitation wavelength, laser energy, and acquisition exposure time;

[0068] 3) Instrument calibration: Use standard samples or reference materials to calibrate the spectrometer to ensure the accuracy and reliability of the test results;

[0069] C. Spectrum Collection:

[0070] 1) Sample placement: Place the processed sample on the displacement platform of the detection system, ensuring that the sample surface is flat and maintains an appropriate distance from the detection probe.

[0071] 2) Spectral acquisition:

[0072] The sample generates a corresponding Raman signal under the action of the excitation light, and the Raman spectrum is collected according to the preset parameters;

[0073] Three parallel samples were set for the samples soaked in the standard solution of each concentration of each pesticide to be tested, and each sample was tested three times to take the average value as the final Raman spectrum data;

[0074] During the acquisition process, pay attention to the display interface of the spectrometer to ensure the stability and accuracy of the spectral data;

[0075] The required Raman images and spectral data are obtained through computer analysis and processing;

[0076] D. Spectral analysis:

[0077] 1) Data preprocessing:

[0078] Perform pre-processing such as denoising and smoothing on the collected Raman spectrum data;

[0079] 2) Identification of pesticide residue types:

[0080] Extract characteristic spectrum segments from spectral data. These characteristic peaks usually correspond to specific chemical bonds or functional groups of pesticide molecules. Then use these extracted characteristic spectrum segments as input features of the pesticide residue type identification model, and analyze and compare the output results of the model.

[0081] The classification performance of the model was evaluated based on four evaluation indicators: accuracy, recall, precision, and F1 score, and the method and model that can quickly, non-destructively, and accurately identify the type of pesticide in the sample to be tested was selected;

[0082] 3) Pesticide residue concentration detection:

[0083] Extract characteristic spectrum segments from the preprocessed spectral data, and then use the extracted characteristic spectrum segments as input features of the concentration detection model, and analyze and compare the model output results;

[0084] The classification performance of the model was evaluated based on four evaluation indicators: accuracy, recall, precision, and F1 score, and the model that could quickly, nondestructively, and accurately detect the concentration of pesticide residues in samples was selected.

[0085] In step A:

[0086] The pesticide preparation to be tested was diluted with ultrapure water to prepare 0.1, 10, and 100 mg / kg solutions as standard solutions;

[0087] The pesticide preparation to be tested was diluted with ultrapure water to prepare solutions of 0.1, 0.5, 0.9, 1.4, 10.0, 15.0, 20.0, 80.0, 90.0, and 100.0 mg / kg as control samples;

[0088] The plant materials to be tested were washed with ultrapure water, and each group of samples was immersed in three pesticide standard solutions of different concentrations for 3 minutes.

[0089] In step D:

[0090] 1) Spectral data preprocessing methods include any of the following: multivariate scatter correction (MSC), standard normal transformation (SNV), SG smoothing method, second-order derivative method, airPLS and SG-airPLS method;

[0091] 2) Identification of pesticide residue types:

[0092] The UVE, SPA, and CARS methods are used to extract characteristic spectral segments from spectral data. These characteristic peaks usually correspond to specific chemical bonds or functional groups of pesticide molecules. These extracted characteristic spectral segments are then used as input features for the SVM, RF, and 1DCNN pesticide residue type recognition models.

[0093] 3) Pesticide residue concentration detection:

[0094] The UVE, SPA and CARS methods are used to extract characteristic spectral segments from the preprocessed spectral data, and then the extracted characteristic spectral segments are used as input features of the PLSR and RF concentration detection models.

[0095] In summary, the pesticide residue detection method based on ultraviolet resonance Raman in the embodiment of the present invention adopts ultraviolet resonance Raman technology. The ultraviolet resonance Raman technology is based on the Raman scattering effect, and uses ultraviolet light to excite specific chromophores or aromatic compounds in pesticide molecules to produce a resonance effect, thereby enhancing the Raman signal. By analyzing the characteristic peaks and vibration modes of the Raman spectrum, the type and structure of the pesticide molecule can be identified.

[0096] UV resonance Raman technology is an advanced molecular spectroscopy analysis technology that combines the advantages of UV excitation and resonance Raman effect and can solve a variety of technical problems, such as complex sample processing, low selectivity and sensitivity, and susceptibility to interference.

[0097] Most existing technologies require complex sample pretreatment or destruction of sample structure when detecting pesticide residues in complex matrices such as food and agricultural products; they have low sensitivity when detecting trace pesticide residues and cannot detect pesticide molecules at lower concentrations; actual samples often contain multiple components, which may interfere with the spectrum and lead to inaccurate test results. For example, pigments, tea polyphenols and other substances contained in agricultural products may interfere with SERS detection of permeable pesticides. The ultraviolet resonance Raman technology has the advantages of in-situ and non-destructive detection, high detection sensitivity, and weak background fluorescence interference when detecting pesticide residues, which can solve the above problems.

[0098] In order to more clearly demonstrate the technical solution and technical effects provided by the present invention, the embodiments of the present invention are described in detail with specific embodiments below.

[0099] Example 1

[0100] like Figure 3 As shown:

[0101] When using UV resonance Raman technology to detect pesticide residues, the following steps are mainly followed:

[0102] 1. Sample preparation

[0103] The pesticide preparations to be tested were diluted with ultrapure water to prepare solutions of 0.1, 10, and 100 mg / kg as standard solutions. The pesticide preparations to be tested were diluted with ultrapure water to prepare solutions of 0.1, 0.5, 0.9, 1.4, 10.0, 15.0, 20.0, 80.0, 90.0, and 100.0 mg / kg as control samples. The plant materials to be tested were washed with ultrapure water, and each group of samples was soaked in three pesticides of different concentrations for 3 minutes, then taken out and drained.

[0104] 2. Instrument Setup and Calibration

[0105] 1. Instrument selection: Select an ultraviolet resonance Raman spectroscopy detection system with a wavelength range of 190-520 nm, a spectral range of 350-4000 cm-1, and a spectral resolution of 0.08 nm.

[0106] 2. Parameter setting: According to the characteristics of the pesticide molecules to be tested and the properties of the sample, set appropriate parameters such as excitation wavelength, laser energy, acquisition exposure time, etc.

[0107] 3. Instrument calibration: Use standard samples or reference materials to calibrate the spectrometer to ensure the accuracy and reliability of the test results.

[0108] 3. Spectral Collection

[0109] 1. Sample placement: Place the processed sample on the displacement platform of the detection system, ensuring that the sample surface is flat and maintains an appropriate distance from the detection probe.

[0110] 2. Spectral acquisition: The sample generates corresponding Raman signals under the action of excitation light, and the Raman spectrum is collected according to the preset parameters. Three parallel samples are set for each pesticide and each concentration, and each sample is tested three times to take the average value as the final Raman spectrum data. During the acquisition process, pay attention to the display interface of the spectrometer to ensure the stability and accuracy of the spectrum data. The required Raman image and spectrum data are obtained after computer analysis and processing.

[0111] 4. Spectral Analysis

[0112] 1. Data preprocessing: Select appropriate spectral data preprocessing methods to perform denoising and smoothing on the collected Raman spectral data to improve data quality, such as multivariate scattering correction (MSC), standard normal transformation (SNV), SG smoothing method, second-order derivative method, airPLS and SG-airPLS.

[0113] 2. Identification of pesticide residue types: Use UVE, SPA, CARS and other methods to extract characteristic spectral segments from spectral data. These characteristic peaks usually correspond to specific chemical bonds or functional groups of pesticide molecules. Then use these extracted characteristic spectral segments as input features of pesticide residue type identification models such as SVM, RF, 1DCNN, etc. The output results of the models are analyzed and compared, and the classification performance of the models is evaluated according to four evaluation indicators (accuracy, recall rate, precision and F1 score). Select methods and models that can quickly, non-destructively and accurately identify the types of pesticides in the test samples.

[0114] 3. Pesticide residue concentration detection: UVE, SPA, CARS and other methods were used to extract characteristic spectral segments from the preprocessed spectral data. The extracted characteristic spectral segments were then used as input features for concentration detection models such as PLSR and RF. The model output results were analyzed and compared, and the classification performance of the model was evaluated based on four evaluation indicators (accuracy, recall rate, precision and F1 score), and the model that can quickly, non-destructively and accurately detect the pesticide residue concentration of the sample was selected.

[0115] The ultraviolet resonance Raman technology of the present invention brings beneficial effects in the following aspects:

[0116] 1. Signal enhancement and background suppression

[0117] By using UV excitation, significant signal enhancement can be achieved. This is because the effective efficiency of Raman scattering is inversely proportional to the fourth power of the excitation wavelength, so UV excitation can provide a stronger signal. UV resonance Raman technology can capture Raman spectra below the fluorescence window wavelength, thereby effectively reducing or eliminating background fluorescence. This is especially important for complex samples (such as pesticide residue samples containing multiple fluorescent impurities).

[0118] 2. Analytical specificity and selectivity

[0119] It has higher analytical specificity and selectivity. When the energy of the excitation laser corresponds exactly to the electronic transition within the molecule being examined, the signal is significantly enhanced, which is called resonance Raman spectroscopy. Resonance Raman technology can selectively enhance the signal from specific subgroups in macromolecules, so that molecules in complex sample matrices are favorably excited. UV resonance Raman technology identifies pesticide residues by analyzing the vibration information of molecules. It is not interfered by other substances in the sample, has higher selectivity, and can more accurately identify target pesticide residues.

[0120] 3. Sample processing and detection efficiency

[0121] UV resonance Raman technology can rapidly detect samples under non-destructive conditions without the need for complex sample preparation. This technology can significantly improve detection sensitivity through the resonance effect and is suitable for the detection of trace pesticide residues. It is efficient and fast. In terms of pesticide residue detection, UV resonance Raman technology has demonstrated its unique advantages. For example, it can be used to detect pesticide residues such as bisbenzimidazole in bananas.

[0122] In summary, UV resonance Raman technology has significant advantages over surface enhanced Raman technology and fluorescence spectroscopy technology in the detection of pesticide residues, including signal enhancement and background suppression, analytical specificity and selectivity, sample processing and detection efficiency, etc. These advantages make UV resonance Raman technology have a wider application prospect and higher detection accuracy in the field of pesticide residue detection.

[0123] Key technical points of the present invention:

[0124] The two biggest disadvantages of Raman spectroscopy are: the signal intensity is weak, requiring a high-power laser light source and a sensitive detector; the signal may be affected by the fluorescence and strong scattering of the sample.

[0125] The ultraviolet resonance Raman detection technology combines the advantages of ultraviolet light excitation and resonance Raman to overcome the shortcomings of traditional Raman spectroscopy technology. The present invention provides a specific solution for detecting pesticide residues based on ultraviolet resonance Raman.

[0126] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with the technical field within the technical scope disclosed in the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims. The information disclosed in the background technology section of this article is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as an admission or in any form that the information constitutes prior art known to those skilled in the art.

Claims

1. A method for detecting pesticide residues based on ultraviolet resonance Raman, characterized in that: The ultraviolet resonance Raman technology is used. Based on the Raman scattering effect, ultraviolet light is used to excite specific chromophores or aromatic compounds in pesticide molecules to produce a resonance effect, thereby enhancing the Raman signal. Then, the type and structure of the pesticide molecules are identified by analyzing the characteristic peaks and vibration modes of the Raman spectrum.

2. The method for detecting pesticide residues based on ultraviolet resonance Raman according to claim 1, characterized in that: Includes steps: A. Sample preparation: The pesticide preparation to be tested is diluted with ultrapure water to prepare three solutions with different concentrations as standard solutions; The pesticide preparation to be tested is diluted with ultrapure water to prepare solutions of various concentrations as comparative samples; The plant materials to be tested were washed with ultrapure water, and each group of samples was soaked in three different concentrations of pesticide standard solutions, and then taken out and drained; B. Instrument Setup and Calibration: 1) Instrument selection: Select wavelength range 190~520nm, spectral range 350~4000cm -1 , UV resonance Raman spectroscopy detection system with a spectral resolution of 0.08nm; 2) Parameter setting: According to the characteristics of the pesticide molecules to be tested and the properties of the sample, set the following parameters: excitation wavelength, laser energy, and acquisition exposure time; 3) Instrument calibration: Use standard samples or reference materials to calibrate the spectrometer to ensure the accuracy and reliability of the test results; C. Spectrum Collection: 1) Sample placement: Place the processed sample on the displacement platform of the detection system, ensuring that the sample surface is flat and maintains an appropriate distance from the detection probe. 2) Spectral acquisition: The sample generates a corresponding Raman signal under the action of the excitation light, and the Raman spectrum is collected according to the preset parameters; Three parallel samples were set for the samples soaked in the standard solution of each concentration of each pesticide to be tested, and each sample was tested three times to take the average value as the final Raman spectrum data; During the acquisition process, pay attention to the display interface of the spectrometer to ensure the stability and accuracy of the spectral data; The required Raman images and spectral data are obtained through computer analysis and processing; D. Spectral analysis: 1) Data preprocessing: Perform pre-processing such as denoising and smoothing on the collected Raman spectrum data; 2) Identification of pesticide residue types: Extract characteristic spectrum segments from spectral data. These characteristic peaks usually correspond to specific chemical bonds or functional groups of pesticide molecules. Then use these extracted characteristic spectrum segments as input features of the pesticide residue type identification model, and analyze and compare the output results of the model. The classification performance of the model was evaluated based on four evaluation indicators: accuracy, recall, precision, and F1 score, and the method and model that can quickly, non-destructively, and accurately identify the type of pesticide in the sample to be tested was selected; 3) Pesticide residue concentration detection: Extract characteristic spectrum segments from the preprocessed spectral data, and then use the extracted characteristic spectrum segments as input features of the concentration detection model, and analyze and compare the model output results; The classification performance of the model was evaluated based on four evaluation indicators: accuracy, recall, precision, and F1 score, and the model that could quickly, nondestructively, and accurately detect the concentration of pesticide residues in samples was selected.

3. The method for detecting pesticide residues based on ultraviolet resonance Raman according to claim 2, characterized in that: In step A: The pesticide preparation to be tested was diluted with ultrapure water to prepare 0.1, 10, and 100 mg / kg solutions as standard solutions; The pesticide preparation to be tested was diluted with ultrapure water to prepare solutions of 0.1, 0.5, 0.9, 1.4, 10.0, 15.0, 20.0, 80.0, 90.0, and 100.0 mg / kg as control samples; The plant materials to be tested were washed with ultrapure water, and each group of samples was immersed in three pesticide standard solutions of different concentrations for 3 minutes.

4. The method for detecting pesticide residues based on ultraviolet resonance Raman according to claim 3, characterized in that: In step D: 1) Spectral data preprocessing methods include any of the following: multivariate scatter correction (MSC), standard normal transformation (SNV), SG smoothing method, second-order derivative method, airPLS and SG-airPLS method; 2) Identification of pesticide residue types: The UVE, SPA, and CARS methods are used to extract characteristic spectral segments from spectral data. These characteristic peaks usually correspond to specific chemical bonds or functional groups of pesticide molecules. These extracted characteristic spectral segments are then used as input features for the SVM, RF, and 1DCNN pesticide residue type recognition models. 3) Pesticide residue concentration detection: The UVE, SPA and CARS methods are used to extract characteristic spectral segments from the preprocessed spectral data, and then the extracted characteristic spectral segments are used as input features of the PLSR and RF concentration detection models.

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