Research On Photoacoustic Signal Enhancement Based On AuPNs And Feature Extraction Algorithm

Photoacoustic effect detection technology has very broad research prospects in the field of non-invasive blood glucose detection due to its strong safety and penetrability.This topic mainly analyzes the principle of the photoacoustic effect,collecting the photoacoustic signal of the glucose solution by building an experimental system,analyzing the characteristic relationship between the signal and the concentration,and building an algorithm model of the characteristic and the concentration,and using the model to predict the concentration of the photoacoustic signal The accuracy of the value is used as an evaluation criterion for the pros and cons of the model.The analysis of the prediction results uniformly uses Clark Error Grid(CEG)analysis.The main research contents of this thesis are as follows:1.This thesis studies the basic theory of photoacoustic effect,mainly including the principle of photoacoustic effect and the mathematical model of the relationship between signal and sample concentration,which provides a theoretical basis for subsequent experimental system construction and photoacoustic signal collection and analysis.According to the principle of photoacoustic effect,a photoacoustic signal acquisition experimental system is established.2.In the feature extraction algorithm,this thesis is different from the research direction of most researchers,and mainly studies the relationship between the original signal and the concentration without any preprocessing.Therefore,this article is to study the time domain photoacoustic waveform spectrum method and BP neural network analysis of the relationship between photoacoustic signal and concentration as the research object.The time-domain photoacoustic waveform spectrum method is mainly to construct the relationship model between the photoacoustic signal set and the concentration transformation and the signal.Finding out the most relevant component matrix L and the corresponding score evaluation matrix S through principal component analysis,and obtain the concentration regression coefficient by regression fitting the signal score matrix S and the corresponding concentration value c.The signal to be measured can be The concentration value is predicted by the concentration regression coefficient.The prediction results are distributed in another 91.07% of the A area,8.93% of the B area,and no results are distributed in the C,D,and E areas.BP neural network learns the mapping relationship between signal and concentration autonomously through its own powerful nonlinear fitting ability.This article mainly analyzes the signal composition,designs the neural network structure,puts the training data into the model for training,and finally obtains the relationship model between the signal and the concentration.The concentration value of the signal to be measured can be predicted through the network model.The predicted results are distributed in the A area and 3.3% in the B area.No results are distributed in the C,D,and E intervals.3.In this thesis,gold nanoparticles are used as the photoacoustic signal enhancer,and the photoacoustic signal of the mixed solution is measured by mixing the gold nanoparticle solution with the glucose solution respectively.The results show that using the time-domain photoacoustic waveform spectroscopy method and BP neural network analysis,the results of the photoacoustic signal of the gold nanoparticles with Au-Si O2 added are significantly better than the results without the addition of the gold nanoparticle solution.The best effect is that all prediction results are distributed in area A,and no results are distributed in other areas.