Analytical and Bioanalytical Chemistry

, Volume 411, Issue 1, pp 63–77 | Cite as

Non-invasive monitoring of blood glucose using optical methods for skin spectroscopy—opportunities and recent advances

  • Sven Delbeck
  • Thorsten Vahlsing
  • Steffen Leonhardt
  • Gerald Steiner
  • H. Michael HeiseEmail author


Diabetes mellitus is a widespread disease with greatly rising patient numbers expected in the future, not only for industrialized countries but also for regions in the developing world. There is a need for efficient therapy, which can be via self-monitoring of blood glucose levels to provide tight glycemic control for reducing the risks of severe health complications. Advancements in diabetes technology can nowadays offer different sensor approaches, even for continuous blood glucose monitoring. Non-invasive blood glucose assays have been promised for many years and various vibrational spectroscopy-based methods of the skin are candidates for achieving this goal. Due to the small spectral signatures of the glucose hidden among a largely variable background, the largest signal-to-noise ratios and multivariate calibration are essential to provide the method applicability for self-monitoring of blood glucose. Besides multiparameter approaches, recently presented devices based on photoplethysmography with wavelengths in the visible and near-infrared range are evaluated for their potential of providing reliable blood glucose concentration predictions.

Graphical abstract


Non-invasive glucose sensing Vibrational spectroscopy Photoplethysmography Color sensing Multivariate calibration Validation studies 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    The Diabetes Control and Complications Trial Research Group. The effect of intensive treatment of diabetes on the development of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Engl J Med. 1993;329(14):977–86.CrossRefGoogle Scholar
  2. 2.
    The Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study Group. Continuous glucose monitoring and intensive treatment of type 1 diabetes. N Engl J Med. 2008;359(14):1464–76.CrossRefGoogle Scholar
  3. 3.
    American Diabetes Association. Glycemic targets: standards of medical care in diabetes—2018. Diabetes Care. 2018;
  4. 4.
    Chamberlain JJ, Rhinehart AS, Shaefer CF Jr, Neuman A. Diagnosis and management of diabetes: synopsis of the 2016 American Diabetes Association Standards of Medical Care in Diabetes. Ann Intern Med. 2016;164(8):542–52.PubMedCrossRefGoogle Scholar
  5. 5.
    National Institute for Health Research. New and emerging non-invasive glucose monitoring technologies. In: Horizon Scanning Research & Intelligence Centre. University of Birmingham. 2016. Accessed 11 July 2018.
  6. 6.
    Corabian P, Chojecki D. Exploratory brief on glucose monitoring technologies. In: IHE report. Institute of Health and Economics. 2017. Accessed 11 July 2018.
  7. 7.
    Cappon G, Acciaroli G, Vettoretti M, Facchinetti A, Sparacino G. Wearable continuous glucose monitoring sensors: a revolution in diabetes treatment. Electronics. 2017;6(3):65.CrossRefGoogle Scholar
  8. 8.
    Kim J, Campbell AS, Wang J. Wearable non-invasive epidermal glucose sensors: a review. Talanta. 2018;177:163–70.PubMedCrossRefGoogle Scholar
  9. 9.
    Acciaroli G, Vettoretti M, Facchinetti A, Sparacino G. Calibration of minimally invasive continuous glucose monitoring sensors: state-of-the-art and current perspectives. Biosensors. 2018;8(1):E24.PubMedCrossRefGoogle Scholar
  10. 10.
    Lin T, Gal A, Mayzel Y, Horman K, Bahartan K. Non-invasive glucose monitoring: a review of challenges and recent advances. Curr Trends Biomedical Eng Biosci. 2017;6(5) CTBEB.MS.ID):555696. Scholar
  11. 11.
    Uwadaira Y, Ikehata A. Noninvasive blood glucose measurement. In: Bagchi D, Nair S, editors. Nutritional and therapeutic interventions for diabetes and metabolic syndrome. 3rd ed. New York: Elsevier; 2018. p. 489–504.CrossRefGoogle Scholar
  12. 12.
    Smith JL, The pursuit of noninvasive glucose: “hunting the deceitful turkey,” 6th edition;; Accessed 16 Sept 2018.
  13. 13.
    Vahlsing T, Delbeck S, Leonhardt S, Heise HM. Noninvasive monitoring of blood glucose using color-coded photoplethysmographic images of the illuminated fingertip within the visible and near-infrared range: opportunities and questions. J Diabetes Sci Technol 2018; online first, doi:
  14. 14.
    Dehennis A, Mortellaro MA, Ioacara S. Multisite study of an implanted continuous glucose sensor over 90 days in patients with diabetes mellitus. J Diabetes Sci Technol. 2015;9(5):951–6.PubMedPubMedCentralCrossRefGoogle Scholar
  15. 15.
    Caduff A, Zanon M, Zakharov P, Mueller M, Talary M, Krebs A, et al. First experiences with a wearable multisensory in an outpatient glucose monitoring study, part I: the user’s view. J Diabetes Sci Technol. 2018;12(3):562–8.PubMedCrossRefGoogle Scholar
  16. 16.
    Zanon M, Mueller M, Zakharov P, Talary M, Donath M, Stahel WA, et al. First experiences with a wearable multisensor device in a noninvasive continuous glucose monitoring study at home, part II: the investigator’s view. J Diabetes Sci Technol. 2018;12(3):554–61.PubMedCrossRefGoogle Scholar
  17. 17.
    Liu R, Chen W, Gu X, Wang RK, Xu K. Chance correlation in non-invasive glucose measurement using near-infrared spectroscopy. J Phys D Appl Phys. 2005;38:2675–81.CrossRefGoogle Scholar
  18. 18.
    Zhang W, Liu R, Zhang W, Jia H, Xu K. Discussion on the validity of NIR spectral data in non-invasive blood glucose sensing. Biomed Optics Exp. 2013;4:789–802.CrossRefGoogle Scholar
  19. 19.
    Heise HM, Lampen P, Marbach R. Near-infrared reflection spectroscopy for non-invasive monitoring of glucose—established and novel strategies for multivariate calibration. In: Tuchin VV, editor. Handbook of optical sensing of glucose in biological fluids and tissues. Boca Raton: CRC Press; 2009. p. 115–56.Google Scholar
  20. 20.
    Arnold MA, Small GW. Noninvasive glucose sensing. Anal Chem. 2005;77(17):5429–39.PubMedCrossRefGoogle Scholar
  21. 21.
    Parkes JL, Slatin SL, Pardo S, Ginsberg BH. A new consensus error grid to evaluate the clinical significance of inaccuracies in the measurement of blood glucose. Diabetes Care. 2000;23(8):1143–8.PubMedCrossRefGoogle Scholar
  22. 22.
    Jendrike N, Baumstark A, Kamecke U, Haug C, Freckmann G. ISO 15197: 2013 evaluation of a blood glucose monitoring system’s measurement accuracy. J Diabetes Sci Technol. 2017;11(6):1275–6.PubMedPubMedCentralCrossRefGoogle Scholar
  23. 23.
    Pfützner A, Strobl S, Demircik F, Redert L, Pfützner J, Pfützner AH, Lier A. Evaluation of a new noninvasive glucose monitoring device by means of standardized meal experiments. J Diabetes Sci Technol. 2018, Online First;
  24. 24.
    Breton MD, Kovatchev BP. Impact of blood glucose self-monitoring errors on glucose variability, risk for hypoglycaemia, and average glucose control in type 1 diabetes: an in silico study. J Diabetes Sci Technol. 2010;4(3):562–70.PubMedPubMedCentralCrossRefGoogle Scholar
  25. 25.
    Heise HM. Glucose measurements by vibrational spectroscopy. In: Chalmers JM, Griffiths PR, editors. Handbook of vibrational spectroscopy, Vol. 5 (Applications in Life, Pharmaceutical and Natural Sciences). Chichester: Wiley; 2002. p. 3280–94.Google Scholar
  26. 26.
    Heise HM, Marbach R. Human oral mucosa studies with varying blood glucose concentration by non-invasive ATR-FT-IR-spectroscopy. Cell Mol Biol. 1998;44(6):899–912.PubMedGoogle Scholar
  27. 27.
    Amerov AK, Chen J, Arnold MA. Molar absorptivities of glucose and other biological molecules in aqueous solutions over the first overtone and combination regions of the near-infrared spectrum. Appl Spectrosc. 2004;58(10):1195–204.PubMedCrossRefGoogle Scholar
  28. 28.
    Heise HM. Near-infrared spectrometry for in vivo glucose sensing. In: Fraser DM, editor. Biosensors in the body: continuous in vivo monitoring. Chichester: John Wiley & Sons; 1997. p. 79–116.Google Scholar
  29. 29.
    Norris KH. Possible medical applications of NIR. In: Murray I, Cowe IA, editors. Making light work: advances in near infrared spectroscopy. Weinheim: Wiley-VCH; 1992. p. 596.Google Scholar
  30. 30.
    Kohl M, Essenpreis M, Cope M. The influence of glucose concentration upon the transport of light in tissue-simulating phantoms. Phys Med Biol. 1995;40(7):1267–87.PubMedCrossRefGoogle Scholar
  31. 31.
    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.PubMedPubMedCentralCrossRefGoogle Scholar
  32. 32.
    Barman I, Kong C-R, Singh GP, Dasari RR, Feld MS. An accurate spectroscopic calibration for non-invasive glucose monitoring by modelling the physiological glucose dynamics. Anal Chem. 2010;82:6104–14.PubMedPubMedCentralCrossRefGoogle Scholar
  33. 33.
    Shih WC, Bechtel KL, Rebec MV. Noninvasive glucose sensing by transcutaneous Raman spectroscopy. J Biomed Opt. 2015;20(5):051036.PubMedPubMedCentralCrossRefGoogle Scholar
  34. 34.
    Mäntele W, Hertzberg O, Bauer A, Küderle A, Pleitez MA. Depth-selective photothermal IR spectroscopy of skin: potential application for non-invasive glucose measurement. Analyst. 2017;142(3):495–502.PubMedCrossRefGoogle Scholar
  35. 35.
    Mäntele W, Bauer A, Hertzberg O, Küderle A, Strobel D, Pleitez MA. IR-spectroscopy of skin in vivo: optimal skin sites and properties for non-invasive glucose measurement by photoacoustic and photothermal spectroscopy. J Biophotonics. 2018;11(1):e201600261.CrossRefGoogle Scholar
  36. 36.
    Werth A, Liakat S, Dong A, Woods CM, Gmachl CF. Implementation of an integrating sphere for the enhancement of noninvasive glucose detection using quantum cascade laser spectroscopy. Appl Phys B Lasers Opt. 2018;124:75. Scholar
  37. 37.
    Clarke WL. The original Clarke error grid analysis (EGA). Diabetes Technol Ther. 2005;7(5):776–9.PubMedCrossRefGoogle Scholar
  38. 38.
    Schönhals A, Tholl H, Glasmacher M, Kröger-Lui N, Rucci A, Petrich W. Optical properties of porcine dermis in the mid-infrared absorption band of glucose. Analyst. 2017;142(8):1235–43.PubMedCrossRefGoogle Scholar
  39. 39.
    Marbach R, Koschinsky T, Gries A, Heise HM. Noninvasive blood glucose assay by near-infrared diffuse reflectance spectroscopy of the human inner lip. Appl Spectrosc. 1993;47(7):875–81.CrossRefGoogle Scholar
  40. 40.
    Knobbe EJ, Buckingham B. The extended Kalman filter for continuous glucose monitoring. Diabetes Technol Ther. 2005;7(1):15–27.PubMedCrossRefGoogle Scholar
  41. 41.
    Koutny T. Blood glucose level reconstruction as a function of transcapillary glucose transport. Comput Biol Med. 2014;53:171–8.PubMedCrossRefGoogle Scholar
  42. 42.
    Cobelli C, Schiavon M, Man CD, Basu A, Basu R. Interstitial fluid glucose is not just a shifted-in-time but a distorted mirror of blood glucose: insight from an in silico study. Diabetes Technol Ther. 2016;18(8):505–11.PubMedPubMedCentralCrossRefGoogle Scholar
  43. 43.
    Xu J, Huang P, Qin Y, Jiang D, Chen H-Y. Analysis of intracellular glucose at single cells using electrochemiluminescence imaging. Anal Chem. 2016;88(9):4609–12.PubMedCrossRefGoogle Scholar
  44. 44.
    Heise HM. In vivo assay of glucose. In: Meyers RA, editor. Encyclopedia of analytical chemistry: instrumentation and applications, Vol.1. Chichester: Wiley; 2000. p. 56–83.Google Scholar
  45. 45.
    Heise HM, Haiber S, Licht M, Ihrig DF, Moll C, Stücker M. Recent progress in non-invasive diabetes screening by diffuse reflectance near-infrared skin spectroscopy. Proc of SPIE. 2006;6093(609310):1–9.Google Scholar
  46. 46.
    Qu J, Wilson BC. Monte Carlo modeling studies of the effect of physiological factors and other analytes on the determination of glucose concentration in vivo by near infrared optical absorption and scattering measurements. J Biomed Opt. 1997;2(3):319–25.PubMedCrossRefGoogle Scholar
  47. 47.
    Tarumi M, Shimada M, Murakami T, Tamura M, Shimada M, Arimoto H, et al. Simulation study of in vitro glucose measurement by NIR spectroscopy and a method of error reduction. Phys Med Biol. 2003;48(15):2373–90.PubMedCrossRefGoogle Scholar
  48. 48.
    Marbach R, Heise HM. On the efficiency of algorithms for multivariate linear calibration used in analytical spectroscopy. TrAC Trends Anal Chem. 1992;11(8):270–5.CrossRefGoogle Scholar
  49. 49.
    Olesberg JT, Liu L, Van Zee V, Arnold MA. In vivo near-infrared spectroscopy of rat skin tissue with varying blood glucose levels. Anal Chem. 2006;78(1):215–23.PubMedCrossRefGoogle Scholar
  50. 50.
    Arnold MA, Liu L, Olesberg JT. Selectivity assessment of noninvasive glucose measurements based on the analysis of multivariate calibration vectors. J Diabetes Sci Technol. 2007;1(4):454–62.PubMedPubMedCentralCrossRefGoogle Scholar
  51. 51.
    Arnold MA, Alexeeva NV. Impact of tissue heterogeneity on noninvasive near-infrared glucose measurements in interstitial fluid of rat skin. J Diabetes Sci Technol. 2010;4(5):1041–54.PubMedPubMedCentralCrossRefGoogle Scholar
  52. 52.
    Heise HM, Marbach R, Bittner A, Koschinsky T. Clinical chemistry and near-infrared spectroscopy: multicomponent assay for human plasma and its evaluation for the determination of blood substrates. J Near Infrared Spectrosc. 1998;6(1):361–74.CrossRefGoogle Scholar
  53. 53.
    Heise HM, Bittner A, Marbach R. Near-infrared reflectance spectroscopy for non-invasive monitoring of metabolites. Clin Chem Lab Med. 2000;38(2):137–45.PubMedCrossRefGoogle Scholar
  54. 54.
    Maruo K, Yamada Y. Near-infrared noninvasive blood glucose prediction without using multivariate analyses: introduction of imaginary spectra due to scattering change in the skin. J Biomed Opt. 2015;20(4):047003.PubMedCrossRefGoogle Scholar
  55. 55.
    Uwadaira Y, Adachi N, Ikehata A, Kawano S. Factors affecting the accuracy of non-invasive blood glucose measurements by short-wavelength near infrared spectroscopy in the determination of the glycaemic index of foods. J Near Infrared Spectrosc. 2010;18(5):291–300.CrossRefGoogle Scholar
  56. 56.
    Bae J, Druzhin VV, Anikanov AG, Afanasyev SV, Shchekin A, Medvedev AS, Morozov AV, Kim D, Kim SK, Moon H, Jang H, Shim J, Park J. A miniaturized near infrared spectrometer for non-invasive sensing of bio-markers as a wearable healthcare solution. Proc. SPIE 10116, MOEMS and Miniaturized Systems XVI. 2017;
  57. 57.
    Tamura T, Maeda Y, Sekine M, Yoshida M. Wearable photoplethysmographic sensors—past and present. Electronics. 2014;3(2):282–302.CrossRefGoogle Scholar
  58. 58.
    Sidorov IS, Romashko RV, Koval VT, Giniatullin R, Kamshilin AA. Origin of infrared light modulation in reflectance-mode photoplethysmography. PLoS One. 2016;11(10):e0165413.PubMedPubMedCentralCrossRefGoogle Scholar
  59. 59.
    Sangiorgi S, Manelli A, Congiu T, Bini A, Pilato G, Reguzzoni M, et al. Microvascularization of the human digit as studied by corrosion casting. J Anat. 2004;204(2):123–31.PubMedPubMedCentralCrossRefGoogle Scholar
  60. 60.
    Elgendi M. On the analysis of fingertip photoplethysmogram signals. Curr Cardiol Rev. 2012;8(1):14–25.PubMedPubMedCentralCrossRefGoogle Scholar
  61. 61.
    Yamakoshi Y, Matsumura K, Yamakoshi T, Lee J, Rolfe P, Kato Y, et al. Side-scattered finger-photoplethysmography: experimental investigations toward practical noninvasive measurement of blood glucose. J Biomed Optics. 2017;22(6):67001.CrossRefGoogle Scholar
  62. 62.
    Ramasahayam S, Arora L, Chowdhury SR. FPGA based smart system for non invasive blood glucose sensing using photoplethysmography and online correction of motion artifact. In: Postolache OA, et al., editors. Sensors for everyday life. Basel: Springer International Publishing; 2017. p. 1–21.Google Scholar
  63. 63.
    Monte-Moreno E. Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques. Artif Intell Med. 2011;53(2):127–38.PubMedCrossRefGoogle Scholar
  64. 64.
    Segman Y. Device and method for noninvasive glucose assessment. J Diabetes Sci Technol. 2018, Online First;
  65. 65.
    Keske MA, Dwyer RM, Russel RD, Blackwood SJ, Brown AA, Hu D, et al. Regulation of microvascular flow and metabolism: an overview. Clin Exp Pharmacol Physiol. 2017;44(1):143–9.PubMedCrossRefGoogle Scholar
  66. 66.
    Nishidate I, Tanaka N, Kawase T, Maeda T, Yuasa T, Aizu Y, et al. Noninvasive imaging of human skin hemodynamics using a digital red-green-blue camera. J Biomed Opt. 2011;16(8):086012.PubMedCrossRefGoogle Scholar
  67. 67.
    Segman Y. New method for computing optical hemodynamic blood pressure. J Clin Exp Cardiolog. 2016;7:492.Google Scholar
  68. 68.
    Ji W, Rhodes PA. Spectral color characterization of digital cameras: a review. Proc. SPIE 8332, Optoelectronic Sensing and Imaging. 2012;
  69. 69.
    Park C, Kang MG. Color restoration of RGBN multispectral filter array sensor images based on spectral decomposition. Sensors (Basel). 2016;16(5):719.CrossRefGoogle Scholar
  70. 70.
    Petrov GI, Doronin A, Whelan HT, Meglinski I, Yakovlev VV. Human tissue color as viewed in high dynamic range optical spectral transmission measurements. Biomed Opt Express. 2012;3(9):2154–61.PubMedPubMedCentralCrossRefGoogle Scholar
  71. 71.
    Pfützner A, Klonoff DC, Pardo S, Parkes JL. Technical aspects of the Parkes error grid. J Diabetes Sci Technol. 2013;7(5):1275–80.PubMedPubMedCentralCrossRefGoogle Scholar
  72. 72.
    DIN EN ISO15197:2015: In vitro diagnostic test systems—requirements for blood glucose monitoring systems for self-testing in managing diabetes mellitus (Engl. version ISO 15197:2013).Google Scholar

Copyright information

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

Authors and Affiliations

  • Sven Delbeck
    • 1
  • Thorsten Vahlsing
    • 2
    • 3
  • Steffen Leonhardt
    • 3
  • Gerald Steiner
    • 4
  • H. Michael Heise
    • 1
    Email author
  1. 1.Interdisciplinary Center for Life SciencesSouth-Westphalia University of Applied SciencesIserlohnGermany
  2. 2.Bundesanstalt für Materialforschung und -prüfung (BAM), Acoustic and Electromagnetic MethodsBerlinGermany
  3. 3.Chair for Medical Information Technology, Helmholtz Institute of Biomedical EngineeringRWTH Aachen UniversityAachenGermany
  4. 4.Faculty of Medicine Carl Gustav Carus, Clinical Sensoring and MonitoringTechnical University of DresdenDresdenGermany

Personalised recommendations