Research On The Detection Mechanism And Method Of Trace Pesticide Residues In Tea Based On Surface-Enhanced Raman Scattering Spectroscopy

Currently,pesticide residue in tea is a significant issue affecting the sustainable development of tea industry.Pesticide residue detection is an important means to ensure the quality and safety of tea.Traditional detection methods,such as mass spectrometry,often have the disadvantages of complex steps,high cost and long time,which cannot meet the needs of rapid on-site detection.Surface-enhanced Raman spectroscopy technology has attracted much attention due to its advantages of high sensitivity,high speed and simple sample processing.It has great application potential in the field of pesticide residue detection.However,the application of surface-enhanced Raman spectroscopy in the detection of tea pesticides also faces some problems,such as the influence of complex detection environment,the difficulty of some pesticide molecules to be enhanced by the substrate,and the interference of impurity peaks in the detection of the real tea samples.Therefore,this thesis mainly focuses on the detection mechanism and method of surface-enhanced Raman spectroscopy in trace pesticide residues in tea,and systematically studies the preparation of efficient substrate,the optimization of detection environment,the detection of pesticide with weak Raman signal,the detection of systemic pesticide residues in tea,and the processing of pesticide Raman signal,so as to provide research basis for the rapid detection of pesticide residues in tea.The main research contents and results are as follows.1.Preparation of novel SERS substrate and optimization of pesticide residue detection conditionsThe silver/reduced graphene oxide composite substrate was prepared by using silver nitrate and graphene as raw materials and ascorbic acid as reducing agent.The optimal addition amount of silver nitrate was 0.5 mL,and the diameter of nanoparticles was about 60 nm.The silver nanoparticles could distribute evenly in the upper and lower layers of graphene.The SERS enhancement effect of pure silver nano substrate and composite substrate was compared by the enhanced experiment of probe molecule and the three-dimensional finite difference time domain method.It was proved that the composite substrate had better enhancement ability,and the maximum enhancement factor was 1.39×10⁹.The effects of different pH and solvents on SERS pesticide detection were studied by using flower-shaped silver nanoparticles as the enhanced substrate and ethion pesticide as the research object.It was found that the optimized environmental conditions could keep the structural integrity of pesticide molecules,promote the charge transfer between pesticide molecules and substrate,and enhance the excitation resonance.The optimal detection pH range was 6.66-11.11,and the most suitable solvent was acetone.2.Research on SERS detection mechanism and method of weak Raman signal pesticidesOrganochlorine pesticides,a kind of typical pesticide with weak Raman signal in tea,were selected as the research object,and flower-shaped silver nanoparticles were used as the enhanced substrate.The bridging molecules with amphiphilic properties,such as diquat and lucigenin,were screened out by constructing the bridging model.Using the concentrated-droplet SERS detection method,the bridging molecules could adsorb on the surface of silver nanoparticles and capture organochlorine pesticide molecules throughπ-πbond interaction.The resonate between the bridging molecules and pesticide molecules under laser irradiation could form to produce identifiable enhanced Raman signals.The binding energy between bridging molecule and pesticide molecule was calculated by high-throughput density functional theory.It was found that diquat was suitable for SERS detection of most organochlorine pesticide molecules,such as hexachlorocyclohexane,endosulfan,tetradifon,etc.,while lucigenin was suitable for the detection of individual organochlorine pesticide molecules,such as OP’-DDT.The results showed that the addition of chloride ions could promote the aggregation of silver nanoparticles,reduce the interparticle distance,and increase the number of hot spots and the SERS enhancement effect of pesticides with weak Raman signal.The appropriate concentration of chloride ions was 10^-3mol/L.3.Research on SERS detection mechanism and method of systemic pesticideSystemic pesticides can penetrate into tea tissue and form internal pesticide residues.SERS detection is easily interfered by pigments,polyphenols and other substances in tea,so it is necessary to extract pesticide residues before detection.Based on the Raman characteristics of systemic pesticide,chlorpyrifos,a typical systemic pesticide in tea,was selected as the research object and the flower-shaped silver nanoparticles were used as the enhanced substrate.The quantitative detection model of chlorpyrifos standard samples was established by principal component regression analysis.The model was y=-0.000861406x+13.95313,and the regression coefficient R²was 95.045%.Furthermore,different concentrations of chlorpyrifos emulsion were used to simulate the field spraying and then the fresh leaves of tea were picked to make dry tea samples.The pre-treatment methods of SERS detection were optimized and compared.The best pretreatment steps were grinding,ultrasonic,centrifugation and extraction.The results of SERS and GC-MS were basically consistent.The relative error range was 0.69%-27.72%,and the average relative error was 15.21%,which verified the reliability and practicability of the pretreatment technology in SERS detection of systemic pesticides.4.Research on Raman signal processing methods of tea pesticidesThe pretreatment algorithm,classification algorithm and quantitative detection model algorithm of Raman spectrum signals of pesticides were studied.Based on the mathematical expression and noise sources of Raman signal,the processing effect of three baseline subtraction algorithms and three noise removal algorithms on the Raman signals of pesticides were studied.It was found that wavelet transform had the best baseline and noise subtraction function.Among the four classification algorithms,K-nearest neighbor classification algorithm could classify the Raman spectrum data of pesticides accurately with a classification accuracy of up to 97%.The SERS spectra of fenvalerate pesticide were obtained by using silver/reduced graphene oxide composite material as the enhanced substrate.After the SERS data of fenvalerate pesticide were preprocessed by the above optimal algorithms,the regression model of fenvalerate was studied by using one-dimensional linear regression and principal component analysis.It was found that the best fitting degree and the minimum error of PCA linear regression model were obtained by selecting three Raman peaks of fenvalerate,which is suitable for SERS detection and data processing of samples with unknown fenvalerate pesticide residues.