access icon free Mathematical modelling and simulation analysis of a modified Butterworth van Dyke circuit model for non-invasive diabetes detection

In recent times, there is an intense need for a reliable non-invasive diabetes prediction system. Some of the researches in this field suggest that acetone gas in breath has a good correlation to blood glucose levels. Hence, acetone is emerging as a promising bio-marker in diabetes prediction. In this study, acetone levels are measured using quartz crystal microbalance sensor that has wide-scale application as a bio-sensor. It is a piezoelectric sensor which is used to detect and quantify mass variations. The resonant frequency of the sensor changes when there is a deposition of mass on the surface of the crystal. The shift in resonant frequency is directly proportional to the change in the mass concentration. To estimate the performance of this sensor, it is required to understand the sensor's electrical characteristics such as its conductance gain and admittance. This study studies these characteristics and evaluates the behaviour of the sensor in the presence of various acetone concentrations in breath sample for healthy, type 1 and type 2 diabetic subjects.

Inspec keywords: biosensors; microsensors; piezoelectric transducers; level measurement; bioMEMS; biomedical electronics; sugar; diseases; quartz crystal microbalances

Other keywords: admittance; blood glucose levels; acetone gas; reliable noninvasive diabetes prediction system; quartz crystal microbalance sensor; crystal surface; modified Butterworth van Dyke circuit model; mathematical modelling; noninvasive diabetes detection; biosensor; bio-marker; sensor electrical characteristics; simulation analysis; piezoelectric sensor; acetone level measurement; mass deposition; conductance gain; mass concentration; mass variations; resonant frequency

Subjects: Biophysical instrumentation and techniques; Mass and density measurement; Biosensors; Spatial variables measurement; Microsensors and nanosensors; MEMS and NEMS device technology; Micromechanical and nanomechanical devices and systems; Mass and density measurement; Sonic and ultrasonic transducers and sensors; Biomedical measurement and imaging; Level, flow and volume measurement; Biosensors

References

    1. 1)
      • 25. Amini, A., Bagheri, M.A., Montazer, G.A.: ‘Improving gas identification accuracy of a temperature-modulated gas sensor using an ensemble of classifiers’, Sens. Actuators B, Chem., 2013, 187, pp. 241246.
    2. 2)
      • 30. Arnau, A., Jimenez, Y., Sogorb, T.: ‘An extended Butterworth-Van Dyke model for quartz crystal microbalance applications in viscoelastic fluid media’, IEEE Trans. Ultrason. Ferroelectr. Freq. Control, 2001, 48, (5), pp. 13671382.
    3. 3)
      • 18. Staerz, A., Weimar, U., Barsan, N.: ‘Understanding the potential of WO3 based sensors for breath analysis’, Sensors2016, 16, pp. 116.
    4. 4)
      • 11. Trincavelli, M., Coradeschi, S., Loutfi, A., et al: ‘Direct identification of bacteria in blood culture samples using an electronic nose’, IEEE Trans. Biomed. Eng., 2010, 57, (12), pp. 28842890.
    5. 5)
      • 8. Manchukutty, S., Vasa, N.J., Agarwal, V., et al: ‘Dual photoionization source-based differential mobility sensor for trace gas detection in human breath’, IEEE Sens. J., 2015, 15, (9), pp. 48994904.
    6. 6)
      • 16. Saraoglu, H.M., Selvi, A.O., Ebeoglu, M.A., et al: ‘Electronic nose system based on quartz crystal microbalance sensor for blood glucose and hba1c levels from exhaled breath odor’, IEEE Sens. J., 2013, 13, (11), pp. 42294235.
    7. 7)
      • 31. Yao, Y., Xue, Y.: ‘Impedance analysis of quartz crystal microbalance humidity sensors based on nanodiamond/graphene oxide nanocomposite film’, Sens. Actuators B, Chem., 2015, 211, pp. 5258.
    8. 8)
      • 1. Parker, R.S., Doyle, F.J.III, Peppas, N.A.: ‘The intravenous route to blood glucose control’, IEEE Eng. Med. Biol. Mag., 2001, 20, (1), pp. 6573.
    9. 9)
      • 5. Fan, G.T., Yang, C.L., Lin, C.H., et al: ‘Applications of Hadamard transform-gas chromatography/mass spectrometry to the detection of acetone in healthy human and diabetes mellitus patient breath’, Talanta, 2014, 120, pp. 386390.
    10. 10)
      • 26. Turner, C., Walton, C., Hoashi, S., et al: ‘Breath acetone concentration decreases with blood glucose concentration in type I diabetes mellitus patients during hypoglycaemic clamps’, J. Breath Res., 2009, 3, (4), p. 46004.
    11. 11)
      • 4. Albisser, A.M., Spencer, W.J.: ‘Electronics and the diabetic’, IEEE Trans. Biomed. Eng., 1982, BME-29, (4), pp. 239248.
    12. 12)
      • 7. Senthilmohan, S.T., McEwan, M.J., Wilson, P.F., et al: ‘Real time analysis of breath volatiles using SIFT-MS in cigarette smoking’, Redox Rep., 2001, 6, (3), pp. 185187.
    13. 13)
      • 24. Deng, C., Zhang, J., Yu, X., et al: ‘Determination of acetone in human breath by gas chromatography-mass spectrometry and solid-phase microextraction with on-fiber derivatization’, J. Chromatogr. B, Anal. Technol. Biomed. Life Sci., 2004, 810, (2), pp. 269275.
    14. 14)
      • 6. Ueta, I., Mizuguchi, A., Okamoto, M., et al: ‘Determination of breath isoprene and acetone concentration with a needle-type extraction device in gas chromatography-mass spectrometry’, Clin. Chim. Acta, 2014, 430, pp. 156159.
    15. 15)
      • 13. Sun, M., Chen, Z., Gong, Z., et al: ‘Determination of breath acetone in 149 type 2 diabetic patients using a ringdown breath-acetone analyzer’, Anal. Bioanal. Chem., 2015, 407, (6), pp. 16411650.
    16. 16)
      • 20. Li, X.L., Lou, T.J., Sun, X.M., et al: ‘Highly sensitive WO3 hollow-sphere gas sensors’, Inorg. Chem., 2004, 43, (17), pp. 54425449.
    17. 17)
      • 19. Righettoni, M., Ragnoni, A., Guntner, A.T., et al: ‘Monitoring breath markers under controlled conditions’, J. Breath Res., 2015, 9, (4), p. 47101.
    18. 18)
      • 22. Tao, W., Lin, P., Ai, Y., et al: ‘Multichannel quartz crystal microbalance array: fabrication, evaluation, application in biomarker detection’, Anal. Biochem., 2016, 494, pp. 8592.
    19. 19)
      • 12. Wang, C., Mbi, A., Shepherd, M.: ‘A study on breath acetone in diabetic patients using a cavity ringdown breath analyzer: exploring correlations of breath acetone with blood glucose and glycohemoglobin A1C’, IEEE Sens. J., 2010, 10, (1), pp. 5463.
    20. 20)
      • 29. Nakamoto, T., Kobayashi, T.: ‘Development of circuit for measuring both Q variation and resonant frequency shift of quartz crystal microbalance’, IEEE Trans. Ultrason. Ferroelectr. Freq. Control, 1994, 41, (6), pp. 806811.
    21. 21)
      • 17. Atay, S., Piskin, K., Ylmaz, F., et al: ‘Quartz crystal microbalance based biosensors for detecting highly metastatic breast cancer cells via their transferrin receptors’, Anal. Methods, 2016, 8, pp. 153161.
    22. 22)
      • 28. Wu, H., Zhao, G., Zu, H., et al: ‘Real-time monitoring of platelet activation using quartz thickness-shear mode resonator sensors’, Biophys. J., 2016, 110, (3), pp. 669679.
    23. 23)
      • 2. Turner, C.: ‘Potential of breath and skin analysis for monitoring blood glucose concentration in diabetes’, Expert Rev. Mol. Diagn., 2011, 11, (5), pp. 497503.
    24. 24)
      • 10. James, D., Scott, S.M., Ali, Z., et al: ‘Chemical sensors for electronic nose systems’, Microchimica Acta, 2005, 149, pp. 117.
    25. 25)
      • 27. Saraog, X., Lu, H.M., Koçan, M.: ‘Determination of blood glucose level-based breath analysis by a quartz crystal microbalance sensor array’, IEEE Sens. J., 2010, 10, (1), pp. 104109.
    26. 26)
      • 15. Wang, C., Surampudi, A.B.: ‘An acetone breath analyzer using cavity ringdown spectroscopy: an initial test with human subjects under various situations’, Meas. Sci. Technol., 2008, 19, (10), p. 105604.
    27. 27)
      • 21. Wang, L., Teleki, A., Pratsinis, S.E., et al: ‘Ferroelectric WO3 nanoparticles for acetone selective detection’, Chem. Mater., 2008, 20, (15), pp. 47944796.
    28. 28)
      • 3. Makaram, P., Owens, D., Aceros, J.: ‘Trends in nanomaterial-based non-invasive diabetes sensing technologies’, Diagnostics, 2014, 4, (2), pp. 2746.
    29. 29)
      • 9. Moorhead, K.T., Lee, D., Chase, J.G., et al: ‘Classifying algorithms for SIFT-MS technology and medical diagnosis’, Comput. Methods Programs Biomed., 2008, 89, (3), pp. 226238.
    30. 30)
      • 23. Cao, W., Duan, Y.: ‘Breath analysis: potential for clinical diagnosis and exposure assessment’, Clin. chem., 2006, 52, pp. 800811.
    31. 31)
      • 14. Wang, Z., Wang, C., Lathan, P.: ‘Breath acetone analysis of diabetic dogs using a cavity ringdown breath analyzer’, IEEE Sens. J., 2014, 14, (4), pp. 11171123.
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