Improving Noninvasive Blood Glucose Measurement Accuracy by Applying Genetic Algorithm to Partial Least Square Regression Model

Non-invasive blood glucose measurement would ease everyday life of diabetic patients and may cut the cost involved in their treatments. This project aims at developing a non-invasive blood glucose measurement using NIR (near infrared) spectroscopic device. NIR spectra data and blood glucose levels were collected from 45 participants, resulting 90 samples (75 samples for calibration and 15 samples for testing) in this project. These samples were then used to develop a predictive model using Interval Partial Least Square (IPLS) regression method. The results obtained from this project indicate that the handheld micro NIR has potential use for rapid non-invasive blood glucose monitoring. The coefficient of determination (R 2 ) obtained for calibration/training and testing dataset are respectively 0.9 and 0.91.

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Mechanical Parameter Analysis of Rock Material via PLSR Model

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.467-469.1826 ◽

2011 ◽

Vol 467-469 ◽

pp. 1826-1831 ◽

Cited By ~ 2

Author(s):

Zao Bao Liu ◽

Wei Ya Xu ◽

Fei Xu ◽

Lin Wei Wang

Keyword(s):

Regression Model ◽

Principal Component ◽

Partial Least Square ◽

Rock Material ◽

Least Square ◽

Parameter Analysis ◽

Partial Least Square Regression ◽

Mechanical Parameter ◽

Least Square Regression

Mechanical parameter analysis is a complicated issue since it is influenced by many factors. Closely related with the influencing factors of compressibility coefficients of rock material (sandstone), this article first introduces the way to process partial least square regression (PLSR) analysis. The process of carrying out PLSR is divided into six steps as for analysis and prediction of the regression model, which are data preparation, principle collection, regression model for first principle component, secondary principle analysis, establishment of final regression model and number determination of principal component l. And then introduces PLSR for application of analysis and prediction of compressibility coefficients with 30 experiment samples. Seven prediction samples are carried out by PLSR with the training process of 30 samples. The result shows PLSR has good accuracy in prediction under the condition that the model is properly deprived based on certain experimental samples. Finally, some conclusions are made for further study on both mechanical parameters and partial least square regression method.

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Application of Coupling Model of Projection Pursuit Partial Least-Square Regression Based on Real Coded Accelerating Genetic Algorithm in Land Use Change Forecasting

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.347-353.1774 ◽

2011 ◽

Vol 347-353 ◽

pp. 1774-1777 ◽

Cited By ~ 1

Author(s):

Hai Tao Wang ◽

Zhen Wen Xu ◽

Bin Wang ◽

Heng Li

Keyword(s):

Genetic Algorithm ◽

Land Use ◽

Land Use Change ◽

Projection Pursuit ◽

Coupling Model ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Least Square Regression ◽

Accelerating Genetic Algorithm

In study of Land Use Change forecasting, lots of methods have been developed ,such as Markov model、BP algorithm、Canonical correlation analysis, least-squares regression analysis ,but these methods have deficiency in decision and often inadequate in sample size. In response to these deficiencies，projection pursuit Partial Least-Square Regression based on real coded accelerating genetic algorithm model is developed to analyze and predict land use change in Yanji City. The computation results show that the relative error of Coupling Model of Partial Least-Square Regression Based on Projection Pursuit is smaller than traditional Partial Least-Square Regression model’s, and it has improved the prediction precision evidently.

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