Detection Of Dopamine And Adenine In Aqueous Solution And Urine Based On SERS Technology

Biomarkers refer to a type of indicators that can be used to determine the physiological state of an organism.The detection of biomarkers has always been one of the hot issues in biomedicine.Most of the commonly used detection methods now have shortcomings such as long detection time and expensive equipment,which cannot be satisfied with the rapid on-site detection of biomarkers.Surface Enhanced Raman Scattering（SERS）technology,with its advantages of portability,low cost,fast detection,and high accuracy,satisfies the medical requirements for biomarker detection.This thesis is based on SERS technology,combined with machine learning algorithms,to achieve rapid and accurate detection and qualitative and semi-quantitative analysis of two biomarkers of adenine and dopamine.Based on density functional theory,the molecular spatial structure of the two biomarkers was optimized,and their theoretical Raman spectra were calculated.The theoretical Raman spectrum,the preliminary SERS spectrum,the blank control spectrum and the conventional Raman spectrum of solid powder were compared,and the characteristic peaks were determined and assigned to the characteristic peaks.The silver sol was prepared as the SERS detection substrate,and the morphology and particle size of the silver sol were characterized by scanning electron microscopy and ultraviolet-visible-near-infrared spectrophotometer.Rhodamine 6G（R6G）was used as the probe molecule to characterize the silver sol.The enhancement factor of silver sol is7.22×10⁵,and the relative standard deviation（RSD）of the test results obtained by continuous testing to characterize its stability is 2.29%.SERS was used to detect adenine and dopamine in aqueous and urine solutions.The detection limit of adenine aqueous solution and urine solution are both 0.1μg/m L,and the detection limit of dopamine aqueous solution and urine solution are both 5μg/m L.Semi-quantitative analysis of the two biomarkers showed that the recovery rates of adenine aqueous solution and urine fluctuated between 95.0%～106.0%and 95.0%～102.5%,respectively,and the RSD was less than 3%.The recovery rate of dopamine aqueous solution and urine fluctuated between 97.3%～106.0%and 94.7%～106.8%,respectively,and the RSD was less than 6%.The principal component analysis（PCA）algorithm and the t-SNE algorithm are used to reduce the dimensionality and visualization of the adenine aqueous solution and urine solution data sets.Three models of KNN,SVM,and MLP were used for qualitative and semi-quantitative analysis of adenine urine solution data set.In the qualitative analysis,the accuracy and f₁-score of the KNN model and the SVM model are both 100%;in the semi-quantitative analysis,the PCA-SVM model performs best,and the accuracy of the semi-quantitative analysis of urine solution is 94.2%,and the f₁-score is 94.2%.