Biochemical Detection And Spectral Analysis Based On Au-Au NRs Composite Structure SERS Substrate

Surface enhanced Raman scattering（SERS）technology has been widely used in the fields of biochemistry,medicine,food,etc.due to its advantages of fast,nondestructive and high sensitivity,as well as the characteristics of"molecular fingerprint"peaks.The preparation of SERS substrates with high performance is of great significance for achieving high-efficiency detection of target analytes.Furthermore,the qualitative and quantitative analysis of Raman spectra has great guiding significance for the rapid identification and accurate content determination of substances.This paper mainly focuses on the qualitative and quantitative analysis of Raman spectra,including the improvement of substrate performance,target detection,target qualitative identification and quantitative analysis.The main research work is as follows:1.A method of preparing Au-Au NRs composite structure by combining OAD process and galvanic-cell-reaction was studied.The composite structure models in different reaction stages were constructed,and the strength and density of the SESR"hot spot"of the model were calculated using FDTD algorithm,which proved that the existence of nano-particle structure was feasible to improve the properties of the substrate.Then,a large area of gold-nano-particle structure was driven deposition by galvanic-cell-reaction on the surface of the Au NRs substrate prepared using OAD process.Using BPE as the Raman-labeled molecule and adopting the method of dropping detection,the detection limit of BPE on the optimized Au-Au NRs composite structure substrate could reach 10^-11M,which was increased by three orders of magnitude compared with Au NRs substrate.2.Based on the Au-Au NRs composite structure substrate,the SERS detection of four pesticides including thiram,thiabendazole,carbendazim and phosmet was realized.Combining SERS technology and 1D-CNN model,qualitative identification of the detected object was analysis.Meanwhile,a qualitative classification model of RF-1D-CNN was proposed which could accurately identify thiram even in the case of multivariate mixing system.Then,the real detection environment was simulated.Using filter paper wiping,substrate detection and model analysis,the highly sensitive detection and automatic identification of about 24ng/cm²thiram residues in cucumber surface were realized.3.In order to provide more effective and robust information,quantitative analysis using relative Raman scattering intensity was carried out.Au-Au NRs composite structure substrates were used to detect the binary and ternary mixed systems of thiram,4-MBA and 1,4BDT.Based on the established external calibration curve,the molecular composition of multi-component system was effectively predicted.When molecules with known concentration are selected or added as Raman internal standard,the accurate prediction of target molecular concentration can be realized.And based on the prediction results,a chromatic barcode was developed that could read the complex composition of the sample directly which realized the identification of multi-component system.4.In order to improve the efficiency of quantitative analysis,machine learning algorithm was used.Based on the 1D-CNN model,the accurate concentration prediction of thiram single component system was realized,which proved the powerful ability of1D-CNN model in quantitative analysis.In order to further improve the accuracy of quantitative analysis,research was carried out in combination with the internal standard method.The binary mixture system of thiram and 1,4-BDT was taken as the research object.1D-CNN model also showed the best prediction effect on thiram in the test set,and realized the accurate prediction of unknown sample concentration.The RMSEP was0.044 and Q²was 0.982.