The Research Of Prediction Model In Detecting Glucose Solutions Concentration

The Near Infrared Region extended from 750 to 2500nm and can be used for quantitative measurement of organic functional groups, especially C-H,O-H,N-H and C=O. The near-infrared continuous non-invasive method used a beam of NIR light through the blood vesselregion of human, and extracted the correlating information of blood glucose from the spectrum. The sticking point was how to improve the ratio of signal to noise in order to distinguish the very low glucose absorption and utilize complex statistics theory to establish the prediction model of blood glucose. In this thesis, we discussed the algorithms of processing experiment data based and the methods of outlier detection.According to the absorption peak of glucose molecule in near-infrared overtone band, 1610nm was chose as the signal wavelength while 1200nm and 1350nm were chose as the reference wavelengths in order to eliminate the interfere substance's effect. In the thesis,the modeling methods had been discussed to determine glucose concentrations based on near-infrared three wavelengths system in aqueous glucose solutions. The model experiment that glucose concentration interval was 100mg/dL and the glucose concentration range was 0-500mg/dL was set up.The model had been established based on partial least-square algorithm. When it was used to predict the glucose contents of validation set, the root mean square error of prediction (RMSEP) equaled 17.08mg/dL and the correlation coefficient between prediction values and reference values attained 0.998; while using logarithmic ratio of absorbency difference method to set up prediction model, the RMSEP reached54.94mg/dL and the correlation coefficient equaled 0.949 merely. The results suggested that the model based on PLS algorithm has lesser RMSEP, it also validates the system's feasibility primarilyOn the other hand, outlier detection methods was discussed in this paper.when used to process the raw experiment data before the PLS model was set up, it can make the prediction model optimize and get smaller RMSEP .The model experiment that glucose concentration interval was 20mg/dL and the glucose concentration range was 0-300mg/dL was set up. The model had been established based on partial least-square algorithm. It was used to predict the glucose contents, the R and RMSEP were 0.569 and 75.86mg/dL before the outlier samples were eliminated. After the outlier samples were eliminated, the R and RMSEP were 0.959 and 23.22mg/dL respectively. The results suggested that optimized prediction model could be obtained when PLS algorithm and outlier detection methods were combined...