Non-Invasive Blood Glucose Monitoring by Means of near Infrared Spectroscopy: Methods for Improving the Reliability of the Calibration Models

The feasibility of using near infrared reflection spectroscopy for non-invasive blood glucose monitoring is discussed. Spectra were obtained using a diode-array spectrometer with a fiberoptic measuring head with a wavelength ranging from 800 nm to 1350 nm. Calibration was performed using partial least-squares regression and radial basis function networks. The results of different methods used to evaluate the quality of the recorded spectra in order to improve the reliability of the calibration models, are presented.

[1]  P. Jansson,et al.  Characterization by microdialysis of intracellular glucose level in subcutaneous tissue in humans. , 1988, The American journal of physiology.

[2]  P. Reichard,et al.  The effect of long-term intensified insulin treatment on the development of microvascular complications of diabetes mellitus. , 1993, The New England journal of medicine.

[3]  Klaus Danzer,et al.  Application of Near-Infrared Spectroscopy for Non-Invasive Determination of Blood/Tissue Glucose Using Neural Networks , 1995 .

[4]  D. Cox,et al.  Evaluating Clinical Accuracy of Systems for Self-Monitoring of Blood Glucose , 1987, Diabetes Care.

[5]  T. Deckert,et al.  Prognosis of diabetics with diabetes onset before the age of thirty-one. II. Factors influencing the prognosis. , 1978, Diabetologia.

[6]  E. V. Thomas,et al.  Noninvasive glucose monitoring in diabetic patients: a preliminary evaluation. , 1992, Clinical chemistry.

[7]  L.M.C. Buydens,et al.  Robustness analysis of radial base function and multi-layered feed-forward neural network models , 1995 .

[8]  Mark J. L. Orr,et al.  Regularization in the Selection of Radial Basis Function Centers , 1995, Neural Computation.

[9]  G. Spanner,et al.  New concept for the non-invasive determination of physiological glucose concentrations using modulated laser diodes , 1996, Analytical and bioanalytical chemistry.

[10]  H. Doorenbos,et al.  Development of a Wearable Glucose Sensor; Studies in Healthy Volunteers and in Diabetic Patients , 1991, The International journal of artificial organs.

[11]  E. V. Thomas,et al.  Partial least-squares methods for spectral analyses. 1. Relation to other quantitative calibration methods and the extraction of qualitative information , 1988 .

[12]  I. Mühlhauser,et al.  Predictors of Glycaemic Control in Type 1 Diabetic Patients after Participation in an Intensified Treatment and Teaching Programme , 1994, Diabetic medicine : a journal of the British Diabetic Association.

[13]  CentresMark,et al.  Regularisation in the Selection of Radial Basis Function , 1995 .

[14]  M. Stone.  Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .

[15]  David M. Haaland,et al.  Partial least-squares methods for spectral analyses. 2. Application to simulated and glass spectral data , 1988 .

[16]  Shang-Liang Chen,et al.  Orthogonal least squares learning algorithm for radial basis function networks , 1991, IEEE Trans. Neural Networks.

[17]  S. Genuth,et al.  The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. , 1993, The New England journal of medicine.

[18]  J. Sawers,et al.  Falsely low blood glucose readings by a blood glucose meter system. , 1994, Diabetic medicine : a journal of the British Diabetic Association.

[19]  Annick M. Leroy,et al.  Robust Regression and Outlier Detection. , 1989 .

[20]  H. Heise,et al.  Noninvasive Blood Glucose Assay by Near-Infrared Diffuse Reflectance Spectroscopy of the Human Inner Lip , 1993 .