Applied Physics B

, 124:75 | Cite as

Implementation of an integrating sphere for the enhancement of noninvasive glucose detection using quantum cascade laser spectroscopy

  • Alexandra Werth
  • Sabbir Liakat
  • Anqi Dong
  • Callie M. Woods
  • Claire F. Gmachl
Article
First Online:
Received:
Accepted:
  • 45 Downloads
Part of the following topical collections:
  1. Mid-infrared and THz Laser Sources and Applications

Abstract

An integrating sphere is used to enhance the collection of backscattered light in a noninvasive glucose sensor based on quantum cascade laser spectroscopy. The sphere enhances signal stability by roughly an order of magnitude, allowing us to use a thermoelectrically (TE) cooled detector while maintaining comparable glucose prediction accuracy levels. Using a smaller TE-cooled detector reduces form factor, creating a mobile sensor. Principal component analysis has predicted principal components of spectra taken from human subjects that closely match the absorption peaks of glucose. These principal components are used as regressors in a linear regression algorithm to make glucose concentration predictions, over 75% of which are clinically accurate.

This is a preview of subscription content, log in to check access

Notes

Acknowledgements

The authors would like to thank the Wendy and Eric Schmidt Foundation and the National Science Foundation (Grant no. EEC-0540832) and Daylight Solutions, Inc. in San Diego, CA. Additionally, we acknowledge Kevin Bors, Jessica Doyle, and Laura Xu, for their contributions to this research. The research conducted with human subjects presented in this article has been done while maintaining full compliance with regulations set by Princeton University’s Industrial Review Board (IRB).

References

  1. 1.
    C.D. Mathers, D. Loncar, Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med. 3(11), e442 (2006) CrossRefGoogle Scholar
  2. 2.
    Centers for Disease Control and Prevention, National Diabetes Statistics Report (US Department of Health and Human Services, Atlanta, GA, 2017)Google Scholar
  3. 3.
    M.M. Finucane, G.A. Stevens, M.J. Cowan, G. Danaei, J.K. Lin, C.J. Paciorek, G.M. Singh, H.R. Gutierrez, Y. Lu, A.N. Bahalim, F. Farzadfar, L.M. Riley, M. Ezzati, National, regional, and global trends in fasting plasma glucose and diabetes prevalence since 1980: systematic analysis of health examination surveys and epidemiological studies with 370 country-years and 2.7 million participants. Lancet 378(9785), 31–40 (2011)CrossRefGoogle Scholar
  4. 4.
    E.A. Gale, The rise of childhood type 1 diabetes in the 20th century. Diabetes 51(12), 3353–3361 (2002)CrossRefGoogle Scholar
  5. 5.
    S. Wild, G. Roglic, A. Green, R. Sicree, H. King, Global prevalence of diabetes estimates for the year 2000 and projections for 2030. Diabetes Care 27(5), 1047–1053 (2004)CrossRefGoogle Scholar
  6. 6.
    X. Jouven, R.N. Lemaitre, T.D. Rea, N. Sotoodehnia, J.P. Empana, D.S. Siscovick, Diabetes, glucose level, and risk of sudden cardiac death. Eur. Heart J. 26(20), 2142–2147 (2005)CrossRefGoogle Scholar
  7. 7.
    P.J. Randle, P.B. Garland, C.N. Hales, E.A. Newsholme, The glucose fatty-acid cycle, it’s role in insulin sensitivity, and the metabolic disturbances of diabetes mellitus. Lancet 281(7285), 785–789 (1963)CrossRefGoogle Scholar
  8. 8.
    C. Hsueh, M. Janyasupab, Y. Lee, C. Liu, Electrochemical glucose sensors, in Encyclopedia of applied electrochemistry, ed. by G. Kreysa, K. Ota, R.F. Savinell (Springer, New York, NY, 2014), pp. 479–485Google Scholar
  9. 9.
    J. Wang, Glucose biosensors: 40 years of advance and challenges. Electroanalysis 13(12), 983–988 (2001)CrossRefGoogle Scholar
  10. 10.
    A. Heller, B. Feldman, Electrochemical glucose sensors and their applications in diabetes management. Chem. Rev. 108(7), 2482–2505 (2008)CrossRefGoogle Scholar
  11. 11.
    R.A. Croce Jr., S. Vaddiraju, J. Kondo, Y. Wang, L. Zuo, K. Zhu, S.K. Islam, D.J. Burgess, F. Papadimitrakopulos, F.C. Jain, A miniaturized transcutaneous system for continuous glucose monitoring. Biomed. Devices 15(1), 151–160 (2013)Google Scholar
  12. 12.
    Y.J. Heo, S. Takeuchi, Towards smart tattoos: implantable biosensors for continuous glucose monitoring. Adv. Healthc. Mater. 2(1), 43–56 (2013)CrossRefGoogle Scholar
  13. 13.
    S.K. Thennadil, J.L. Rennert, B.J. Wenzel, K.H. Hazen, T.L. Ruchti, M.B. Block, Comparison of glucose concentration in interstitial fluid, and capillary and venous blood during rapid changes in blood glucose levels. Diabetes Technol. Ther. 3(3), 357–365 (2001)CrossRefGoogle Scholar
  14. 14.
    O.S. Khalil, Noninvasive glucose measurement technologies: an update from 1999 to the dawn of the New Millenium. Diabetes Technol. Ther. 6(5), 660–697 (2004)CrossRefGoogle Scholar
  15. 15.
    J.A. Tamada, M. Lesho, M.J. Tierney, Keeping watch on glucose. Spectr. IEEE 39(4), 52–57 (2002)CrossRefGoogle Scholar
  16. 16.
    J. Pickup, L. McCartney, O. Rolinski, D. Birch, In vivo glucose sensing for diabetes management: progress toward noninvasive monitoring. BMJ 319, 1289 (1999)CrossRefGoogle Scholar
  17. 17.
    H.M. Heise, R. Marbarch, A. Bittner, T. Koschinsky, Clinical chemistry and near-infrared spectroscopy: multicomponent assay for human plasma and its evaluation for the determination of blood substrates. J. Near Infrared Spectrosc. 6, 361–374 (1998)ADSCrossRefGoogle Scholar
  18. 18.
    K. Mauro, M. Tsurugi, M. Tamura, Y. Ozaki, In vivo noninvasive measurement of blood glucose by near-infrared diffuse-reflectance spectroscopy. Appl. Spectrosc. 57, 1236–1244 (2003)ADSCrossRefGoogle Scholar
  19. 19.
    K. Mauro, T. Oota, M. Tsurugi, T. Nakagawa, H. Arimoto, M. Tamura, Y. Ozaki, Y. Yamada, New methodology to obtain a calibration model for noninvasive near-infrared blood glucose monitoring. Appl. Spectrosc. 60(4), 441–449 (2006)ADSCrossRefGoogle Scholar
  20. 20.
    N. Dingari, I. Barman, G. Singh, J. Kang, R. Dasari, M. Feld, Investigation of the specificity of Raman spectroscopy in non-invasive blood glucose measurements. Anal. Bioanal. Chem. 400, 2871–2880 (2011)CrossRefGoogle Scholar
  21. 21.
    H. Ullah, B. Davoudi, A. Mariampillai, G. Hussain, M. Ikram, I.A. Vitkin, Quantification of glucose levels in flowing blood using M-mode swept source optical coherence tomography. Laser Phys. 22(4), 797–804 (2012)ADSCrossRefGoogle Scholar
  22. 22.
    Q.L. Zhao, J.L. Si, Z.Y. Guo, H.J. Wei, H.Q. Yang, G.Y. Wu, S.S. Xie, X.Y. Li, X. Guo, H.Q. Zhong, L.Q. Li, Quantifying glucose permeability and enhanced light penetration in ex vivo human normal and cancerous esophagus tissues with optical coherence tomography. Laser Phys. Lett. 8(1), 71–77 (2011)ADSCrossRefGoogle Scholar
  23. 23.
    R. Liu, W. Chen, X. Gu, R.K. Wang, K. Xu, Chance correlation in non-invasive glucose measurement using near-infrared spectroscopy. J. Phys. D Appl. Phys. 38(15), 2675 (2005)ADSCrossRefGoogle Scholar
  24. 24.
    G. Wysocki, R.F. Curl, F.K. Tittel, R. Maulini, J.M. Bulliard, J. Faist, Widely tunable mode-hop free external cavity quantum cascade laser for high resolution spectroscopic applications. Appl. Phys. B 81(6), 769–777 (2005)ADSCrossRefGoogle Scholar
  25. 25.
    R.F. Curl, F. Capasso, C. Gmachl, A.A. Kosterev, B. McManus, R. Lewicki, M. Pusharsky, G. Wysocki, F.K. Tittel, Quantum cascade lasers in chemical physics. Chem. Phys. Lett. 487(1), 1–18 (2010)ADSCrossRefGoogle Scholar
  26. 26.
    A. Schwaighofer, M. Brandstetter, B. Lendl, Quantum cascade lasers (QCLs) in biomedical spectroscopy. Chem. Soc. Rev. 46, 5903–5924 (2017)CrossRefGoogle Scholar
  27. 27.
    F.K. Tittel, D. Richter, A. Fried, Mid-infrared laser applications and spectroscopy. Topics Appl. Phys 89, 445–515 (2003)Google Scholar
  28. 28.
    M.K. Chowdhury, A. Srivastava, N. Sharma, S. Sharma, Challenges and countermeasures in optical noninvasive blood glucose detection. IJIRSET 2(1), 329–334 (2013)Google Scholar
  29. 29.
    S. Yu, D. Li, H. Chong, C. Sun, K. Xu, Continuous glucose determination using fiber-based tunable mid-infrared laser spectroscopy. Opt. Lasers Eng. 55, 78–83 (2014)CrossRefGoogle Scholar
  30. 30.
    N. Jahangin, A. Bahrampour, M. Taraz, Non-invasive optical techniques for determination of blood glucose levels: a review article. Iran J Med Phys 11(2.3), 224–232 (2014)Google Scholar
  31. 31.
    M.A. Pleitez, T. Lieblein, O. Hertzberg, H. von Lilienfeld-Toal, W. Mantele, In vivo noninvasive monitoring of glucose concentration in human epidermis by mid-infrared pulsed photoacoustic spectroscopy. Anal. Chem. 85, 1013–1020 (2013)CrossRefGoogle Scholar
  32. 32.
    M.A. Pleitez, O. Hertzberg, A. Bauer, M. Seeger, T. Lieblein, H.V. Lilienfeld-Toal, W. Mantele, Photothermal deflectrometry enhanced by total internal reflection enables non-invasive glucose monitoring in human epidermis. Analyst 140(2), 483–488 (2015)ADSCrossRefGoogle Scholar
  33. 33.
    S. Liakat, K.A. Bors, L. Xu, C.M. Woods, J. Doyle, C.F. Gmachl, Noninvasive in vivo glucose sensing on human subjects using mid-infrared light. Biomed. Opt. Express 5(7), 2397–2404 (2014)CrossRefGoogle Scholar
  34. 34.
    S. Wold, K. Esbensen, P. Geladi, Principal component analysis. Chemometr. Intell. Lab. Syst. 2, 37–52 (1987)CrossRefGoogle Scholar
  35. 35.
    D.G. Goebel, Generalized integrating sphere theory. Appl. Opt. 6(1), 125–128 (1967)ADSCrossRefGoogle Scholar
  36. 36.
    T.J. Farrell, M.S. Patterson, B. Wilson, A diffusion theory model of spatially resolved, steady-state diffuse reflectance for the noninvasive determination of tissue optical properties in vivo. Med. Phys. 19(4), 879–888 (1992)CrossRefGoogle Scholar
  37. 37.
    L. Porres, A. Holland, L. Palsson, A.P. Monkman, C. Kemp, A. Beeby, Absolute measurements of photoluminescence quantum yields of solutions using an integrating sphere. J Fluorescence 16(2), 267–272 (2006)CrossRefGoogle Scholar
  38. 38.
    S. Liakat, A.P. Michel, K.A. Bors, C. Gmachl, Mid-infrared (λ = 8.4–9.9 µm) light scattering from porcine tissue. Appl. Phys. Lett. 101(9), 093705 (2012)ADSCrossRefGoogle Scholar
  39. 39.
    Labsphere, Integrating sphere theory and applications. Technical guide, PB-16011-000 Rev.00 (2017)Google Scholar
  40. 40.
    Y. Du, X.H. Hu, M. Cariveau, X. Ma, G.W. Kalmus, J.Q. Lu, Optical properties of porcine skin dermis between 900 nm and 1500 nm. Phys. Med. Biol. 46(1), 0031–9155 (2001)CrossRefGoogle Scholar
  41. 41.
    A.P.M. Michel, S. Liakat, K. Bors, C.F. Gmachl, In vivo measurement of mid-infrared light scattering from human skin. Biomed. Opt. Express 4(4), 520–530 (2013)CrossRefGoogle Scholar
  42. 42.
    K.R. Foster, R. Koprowski, J.D. Skufca, Machine learning, medical diagnosis, and biomedical engineering research—commentary. Biomed. Eng Online 13(1), 94 (2014)CrossRefGoogle Scholar
  43. 43.
    W.L. Clarke, D. Cox, L.A. Gonder-Frederick, W. Carter, S.L. Pohl, Evaluating clinical accuracy of systems for self-monitoring of blood glucose. Diabetes Care 10(5), 622–628 (1987)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electrical EngineeringPrinceton UniversityPrincetonUSA
  2. 2.Princeton IdentityHamilton TownshipUSA
  3. 3.Palo AltoUSA
  4. 4.Department of Electrical and Computer EngineeringDuke UniversityDurhamUSA

Personalised recommendations