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

  • Lijun Xu
  • Jianhong Chen
  • Xiqin Zhang
  • Joon Hock Yeo
  • Lijun Jiang

Abstract

Near infrared (NIR) absorption spectroscopy is a promising technique to noninvasively quantify blood glucose level. In order to extract the glucose signal out of the noisy background, Partial Least Squares (PLS) was utilized to create calibration models that relate the absorption spectra to glucose concentrations. A research grade Fourier Transformed Infrared (FTIR) spectrometer configured with a NIR quartz beam-splitter was used in this investigation. Genetic Algorithm (GA) was implemented to search the most appropriate modeling parameters such as wavelengths within NIR range for PLS regression. Using GA method to optimize the wavelength selection by applying the PLS-based calibration model could greatly enhance the prediction capacity and improve the measurement accuracy.

Keywords

Genetic Algorithm Calibration Model Partial Little Square Regression Partial Little Square Regression Model Genetic Algorithm Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. [1]
    Roger J. McNichols, Gerard L. Cote, 2000, Optical glucose sensing in biological fluids: an overview, Journal of Biomedical Optics, 5 (1), 5–16.Google Scholar
  2. [2]
    R. W. Waynant, V. M.Chenault, 1998, Overview of Non-Invasive Fluid Glucose Measurement Using Optical Techniques to Maintain Glucose Control in Diabetes Mellitus, IEEE Lasers and Electro-Optics Society Newsletter, 12 (2), 3–6.Google Scholar
  3. [3]
    Jason J. Burmeister, Mark A. Arnold, Gary W. Small, 1998, Spectroscopic Considerations for Noninvasive Blood Glucose Measurements with Near Infrared Spectroscopy, IEEE Lasers and Electro-Optics Society Newsletter, 12 (2), 6–9.Google Scholar
  4. [4]
    Mark A. Arnold, Gary W. Small, 1998, Data Handling Issues for Near-Infrared Glucose Measurements, IEEE Lasers and Electro-Optics Society Newsletter, 12 (2), 16–18.Google Scholar
  5. [5]
    F. M Ham., I. N. Kostanic., G. M. Cohen., et al, 1997, Determination of glucose concentrations in an aqueous matrix from NIR spectra using optimal time-domain filtering and partial least-squares regression IEEE Transactions on Biomedical Engineering, 44 (6), 475–485.CrossRefGoogle Scholar
  6. [6]
    H. M. Heise, 1996, Technology for non-invasive monitoring of glucose 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Amsterdam, 52159–2161.Google Scholar
  7. [7]
    F. M Ham., G. M. Cohen.., K. Patel K., et al, 1994, Multivariate determination of glucose using NIR spectra of human blood serum, 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2, 818–819.Google Scholar
  8. [8]
    R. E. Shaffer, G. W. Small, M. A. Arnold, 1996, Genetic Algorithm-Based Protocol for Coupling Digital Filtering and Partial Least-Squares Regression: Application to the Near-Infrared Analysis of Glucose in Biological Matrices Anal. Chem68 2663–2675 .Google Scholar
  9. [9]
    A. S. Bangalore, R. E. Shaffer, G. W. Small, et al, 1996, Genetic Algorithm-Based Method for Selecting Wavelengths and Model Size for Use with Partial Least-Squares Regression: Application to Near-Infrared Spectroscopy Anal. Chem. 68 4200–4212.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2004

Authors and Affiliations

  • Lijun Xu
    • 1
  • Jianhong Chen
    • 1
  • Xiqin Zhang
    • 1
  • Joon Hock Yeo
    • 1
  • Lijun Jiang
    • 2
  1. 1.Nanyang Technological UniversitySingapore
  2. 2.Institute for Infocomm ResearchSingapore

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