Evaluation of accuracy dependence of Raman spectroscopic models on the ratio of calibration and validation points for non-invasive glucose sensing

Abstract

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.

Graphical abstract

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Change history

  • 07 January 2019

    The authors would like to bring to the reader’s attention that the Clarke error grid plot presented in Fig. 3 was generated using codes adapted from following reference.

  • 07 January 2019

    The authors would like to bring to the reader?s attention that the Clarke error grid plot presented in Fig.?3 was generated using codes adapted from following reference.

References

  1. 1.

    American Diabetes A. Economic costs of diabetes in the U.S. in 2012. Diabetes Care. 2013;36(4):1033–46.

    Article  Google Scholar 

  2. 2.

    Wild S, Roglic G, Green A, Sicree R, King H. Global prevalence of diabetes: estimates for the year 2000 and projections for 2030. Diabetes Care. 2004;27(5):1047–53.

    Article  Google Scholar 

  3. 3.

    Brownlee M. Biochemistry and molecular cell biology of diabetic complications. Nature. 2001;414(6865):813–20.

    CAS  Article  Google Scholar 

  4. 4.

    Zimmet P, Alberti KG, Shaw J. Global and societal implications of the diabetes epidemic. Nature. 2001;414(6865):782–7.

    CAS  Article  Google Scholar 

  5. 5.

    American Diabetes A. Standards of medical care in diabetes—2009. Diabetes Care. 2009;32(Suppl 1):S13–61.

    Article  Google Scholar 

  6. 6.

    Olansky L, Kennedy L. Finger-stick glucose monitoring: issues of accuracy and specificity. Diabetes Care. 2010;33(4):948–9.

    Article  Google Scholar 

  7. 7.

    Khalil OS. Spectroscopic and clinical aspects of noninvasive glucose measurements. Clin Chem. 1999;45(2):165–77.

    CAS  PubMed  Google Scholar 

  8. 8.

    Bruen D, Delaney C, Florea L, Diamond D. Glucose sensing for diabetes monitoring: recent developments. Sensors 2017; 17(8).

  9. 9.

    Chen C, Zhao XL, Li ZH, Zhu ZG, Qian SH, Flewitt AJ. Current and emerging technology for continuous glucose monitoring. Sensors 2017 ;17(1).

  10. 10.

    Pickup JC, Hussain F, Evans ND, Rolinski OJ, Birch DJS. Fluorescence-based glucose sensors. Biosens Bioelectron. 2005;20(12):2555–65.

    CAS  Article  Google Scholar 

  11. 11.

    Vashist SK. Non-invasive glucose monitoring technology in diabetes management: a review. Anal Chim Acta. 2012;750:16–27.

    CAS  Article  Google Scholar 

  12. 12.

    Wang HC, Lee AR. Recent developments in blood glucose sensors. J Food Drug Anal. 2015;23(2):191–200.

    CAS  Article  Google Scholar 

  13. 13.

    Khalil OS. Non-invasive glucose measurement technologies: an update from 1999 to the dawn of the new millennium. Diabetes Technol Ther. 2004;6(5):660–97.

    CAS  Article  Google Scholar 

  14. 14.

    Kong CR, Barman I, Dingari NC, Kang JW, Galindo L, Dasari RR, et al. A novel non-imaging optics based Raman spectroscopy device for transdermal blood analyte measurement. AIP Adv. 2011;1(3):32175.

    Article  Google Scholar 

  15. 15.

    Berger AJ, Koo TW, Itzkan I, Feld MS. An enhanced algorithm for linear multivariate calibration. Anal Chem. 1998;70(3):623–7.

    CAS  Article  Google Scholar 

  16. 16.

    Berger AJ, Koo TW, Itzkan I, Horowitz G, Feld MS. Multicomponent blood analysis by near-infrared Raman spectroscopy. Appl Opt. 1999;38(13):2916–26.

    CAS  Article  Google Scholar 

  17. 17.

    Enejder AM, Scecina TG, Oh J, Hunter M, Shih WC, Sasic S, et al. Raman spectroscopy for noninvasive glucose measurements. J Biomed Opt. 2005;10(3):031114.

    Article  Google Scholar 

  18. 18.

    Hanlon EB, Manoharan R, Koo TW, Shafer KE, Motz JT, Fitzmaurice M, et al. Prospects for in vivo Raman spectroscopy. Phys Med Biol. 2000;45(2):R1–59.

    CAS  Article  Google Scholar 

  19. 19.

    Koo TW, Berger AJ, Itzkan I, Horowitz G, Feld MS. Reagentless blood analysis by near-infrared Raman spectroscopy. Diabetes Technol Ther. 1999;1(2):153–7.

    CAS  Article  Google Scholar 

  20. 20.

    Shih WC, Bechtel KL, Rebec MV. Noninvasive glucose sensing by transcutaneous Raman spectroscopy. J Biomed Opt. 2015;20(5):051036.

    Article  Google Scholar 

  21. 21.

    Pandey R, Paidi SK, Valdez TA, Zhang C, Spegazzini N, Dasari RR, et al. Noninvasive monitoring of blood glucose with Raman spectroscopy. Acc Chem Res. 2017;50(2):264–72.

    CAS  Article  Google Scholar 

  22. 22.

    Shao J, Lin M, Li Y, Li X, Liu J, Liang J, et al. In vivo blood glucose quantification using Raman spectroscopy. PLoS One. 2012;7(10):e48127.

    CAS  Article  Google Scholar 

  23. 23.

    Scholtes-Timmerman MJ, Bijlsma S, Fokkert MJ, Slingerland R, van Veen SJ. Raman spectroscopy as a promising tool for noninvasive point-of-care glucose monitoring. J Diabetes Sci Technol. 2014;8(5):974–9.

    CAS  Article  Google Scholar 

  24. 24.

    Lundsgaard-Nielsen SM, Pors A, Banke SO, Henriksen JE, Hepp DK, Weber A. Critical-depth Raman spectroscopy enables home-use non-invasive glucose monitoring. PLoS One. 2018;13(5):e0197134.

    Article  Google Scholar 

  25. 25.

    Lipson J, Bernhardt J, Block U, Freeman WR, Hofmeister R, Hristakeva M, et al. Requirements for calibration in noninvasive glucose monitoring by Raman spectroscopy. J Diabetes Sci Technol. 2009;3(2):233–41.

    Article  Google Scholar 

  26. 26.

    Spegazzini N, Barman I, Dingari NC, Pandey R, Soares JS, Ozaki Y, et al. Spectroscopic approach for dynamic bioanalyte tracking with minimal concentration information. Sci Rep. 2014;4:7013.

    CAS  Article  Google Scholar 

  27. 27.

    Qi D, Berger AJ. Chemical concentration measurement in blood serum and urine samples using liquid-core optical fiber Raman spectroscopy. Appl Opt. 2007;46(10):1726–34.

    CAS  Article  Google Scholar 

  28. 28.

    Lui H, Zhao J, McLean D, Zeng H. Real-time Raman spectroscopy for in vivo skin cancer diagnosis. Cancer Res. 2012;72(10):2491–500.

    CAS  Article  Google Scholar 

  29. 29.

    Clarke WL, Cox D, Gonderfrederick LA, Carter W, Pohl SL. Evaluating clinical accuracy of systems for self-monitoring of blood-glucose. Diabetes Care. 1987;10(5):622–8.

    CAS  Article  Google Scholar 

  30. 30.

    Stockl D, Dewitte K, Fierens C, Thienpont LM. Evaluating clinical accuracy of systems for self-monitoring of blood glucose by error grid analysis—comment on constructing the “upper A-line”. Diabetes Care. 2000;23(11):1711–2.

    CAS  Article  Google Scholar 

  31. 31.

    Giavarina D. Understanding Bland Altman analysis. Biochem Med. 2015;25(2):141–51.

    Article  Google Scholar 

  32. 32.

    Bland JM, Altman DG. Measuring agreement in method comparison studies. Stat Methods Med Res. 1999;8(2):135–60.

    CAS  Article  Google Scholar 

  33. 33.

    Galvao RK, Araujo MC, Jose GE, Pontes MJ, Silva EC, Saldanha TC. A method for calibration and validation subset partitioning. Talanta. 2005;67(4):736–40.

    CAS  Article  Google Scholar 

  34. 34.

    Daszykowski M, Walczak B, Massart DL. Representative subset selection. Anal Chim Acta. 2002;468(1):91–103.

    CAS  Article  Google Scholar 

  35. 35.

    Freckmann G, Schmid C, Baumstark A, Rutschmann M, Haug C, Heinemann L. Analytical performance requirements for systems for self-monitoring of blood glucose with focus on system accuracy: relevant differences among ISO 15197:2003, ISO 15197:2013, and current FDA recommendations. J Diabetes Sci Technol. 2015;9(4):885–94.

    CAS  Article  Google Scholar 

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Acknowledgments

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.

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Correspondence to Anandhi Upendran or Jeon Woong Kang.

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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

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Keywords

  • Diabetes
  • Raman spectroscopy
  • Glucose sensing
  • Partial least squares regression