scholarly journals Improved stability of blood glucose measurement in humans using near infrared spectroscopy

2011 ◽  
Vol 25 (3-4) ◽  
pp. 137-145 ◽  
Author(s):  
C. F. So ◽  
Joanne W. Y. Chung ◽  
Maggie S. M. Siu ◽  
Thomas K. S. Wong
Keyword(s):  
Infrared Spectroscopy ◽  
Blood Glucose ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Spectral Response ◽  
Nir Spectroscopy ◽  
Blood Glucose Measurement ◽  
Blood Glucose Monitoring ◽  
Glucose Measurement ◽  
Prediction Errors

Near infrared (NIR) spectroscopy has become a promising technique for blood glucose monitoring. However, an appropriate model of spectral response in humans is yet to be determined because of the reliability problem. In this study, 48 subjects were recruited. The subjects' left forearms were scanned using near infrared spectroscopy to obtain NIR spectra. Simultaneously, a blood sample of glucose was drawn. A new method based on Monte Carlo approach is applied for partial least squares (PLS), named as PLSMC, is proposed. A large numbers of models are built from calibration subsets which are randomly selected from the whole calibration set in order to minimize the noises. It is then determining the mean value over the models with high correlation and small prediction errors. The results show that the method can enhance the stability of PLS model. Also, the performance of the PLSMCshows more accurate prediction results as compared with conventional PLS.

scholarly journals Non-Invasive Blood Glucose Estimation using Handheld Near Infra-Red Device

2019 ◽  
Vol 8 (3S) ◽  
pp. 16-19 ◽  
Keyword(s):  
Blood Glucose ◽  
Near Infrared ◽  
Blood Glucose Measurement ◽  
Partial Least Square ◽  
Least Square ◽  
Blood Glucose Monitoring ◽  
Glucose Measurement ◽  
Coefficient Of Determination ◽  
Diabetic Patients ◽  
Non Invasive

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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