Optical monitoring of blood glucose levels for non-invasive diagnosis is a growing area of research. Recent efforts in this direction have been inclined towards reducing the requirement of calibration framework. Here, we are presenting a systematic investigation on the influence of variation in the ratio of calibration and validation points on the prospective predictive accuracy of spectral models. A fiber-optic probe coupled Raman system has been employed for transcutaneous measurements. Limit of agreement analysis between serum and partial least square regression predicted spectroscopic glucose values has been performed for accurate comparison. Findings are suggestive of strong predictive accuracy of spectroscopic models without requiring substantive calibration measurements.
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This work is supported by NIH P41-EB015871-30 and Samsung Advanced Institute of Technology (Seoul, South Korea). PTCS acknowledge support from U01-NS090438-03, R21-NS091982-01, R01-HL121386-03, the Singapore-MIT Alliance 2 (Cambridge, MA, USA), the Biosym IRG of Singapore-MIT Alliance Research and Technology Center (Cambridge, MA, USA), and Hamamatsu Corporation (Hamamatsu City, Japan). AU thanks Professor Elizabeth J. Parks (Department of Nutrition and Exercise Physiology, and Division of Gastroenterology and Hepatology, School of Medicine, University of Missouri-Columbia) for providing the YSI analyzer and Nhan T Le (Department of Nutrition and Exercise Physiology) for helping us with calibration and use of the instrument. Intramural Funding for this work was provided by Office of Medical Research, School of Medicine, University of Missouri-Columbia.
Conflict of interest
The authors declare that they have no conflict of interest.
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Singh, S.P., Mukherjee, S., Galindo, L.H. et al. Evaluation of accuracy dependence of Raman spectroscopic models on the ratio of calibration and validation points for non-invasive glucose sensing. Anal Bioanal Chem 410, 6469–6475 (2018). https://doi.org/10.1007/s00216-018-1244-y
- Raman spectroscopy
- Glucose sensing
- Partial least squares regression