Miniature Differential Mobility Spectrometry (DMS) Advances towards Portable Autonomous Health Diagnostic Systems

  • Weixiang Zhao
  • Abhinav Bhushan
  • Michael Schivo
  • Nicholas J. Kenyon
  • Cristina E. Davis

Abstract

Many modern analytical instruments, such as mass spectrometry, have been developed to provide insight into the biochemical content of many different biological sample types. Typically these instruments are large bench-top machines that have very high sensitivity and specificity for the compounds they detect. However, these instruments are not mobile or autonomous, and they require highly trained personnel to operate. There have been many developments in the area of miniature chemical sensors that can maintain performance levels observed in large traditional bio-analytical instruments, but are low-power and potentially mobile and autonomous in function. Miniature differential mobility spectrometry (DMS) is a small instrument that can potentially be used in point-of-care diagnostic applications. This chapter will review the significant advances in this emerging research area, and provide insight as to how these systems could be further improved and adapted for use in autonomous health monitoring and sensing systems.

Keywords

differential mobility spectrometry (DMS) non-invasive disease diagnostics breath analysis chemometrics breath analysis 

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References

  1. 1.
    Miller, R.A., et al.: A MEMS radio-frequency ion mobility spectrometer for chemical vapor detection. Sensors and Actuators A: Physical 91(3), 301–312 (2001)CrossRefGoogle Scholar
  2. 2.
    Nazarov, E.G., et al.: Miniature differential mobility spectrometry using atmospheric pressure photoionization. Analytical Chemistry 78(13), 4553–4563 (2006)CrossRefGoogle Scholar
  3. 3.
    Krylov, E.V., Nazarov, E.G., Miller, R.A.: Differential mobility spectrometer: Model of operation. International Journal of Mass Spectrometry 266(1-3), 76–85 (2007)CrossRefGoogle Scholar
  4. 4.
    Basanta, M., et al.: Increasing analytical space in gas chromatography-differential mobility spectrometry with dispersion field amplitude programming. Journal of Chromatography A 1173(1-2), 129–138 (2007)CrossRefGoogle Scholar
  5. 5.
    Eiceman, G.A., et al.: Pattern recognition analysis of differential mobility spectra with classification by chemical family. Analytica Chimica Acta 579(1), 1–10 (2006)CrossRefGoogle Scholar
  6. 6.
    Kolakowski, B.M., Mester, Z.: Review of applications of high-field asymmetric waveform ion mobility spectrometry (FAIMS) and differential mobility spectrometry (DMS). Analyst 132(9), 842–864 (2007)CrossRefGoogle Scholar
  7. 7.
    Shvartsburg, A.A., et al.: Ultrafast Differential Ion Mobility Spectrometry at Extreme Electric Fields in Multichannel Microchips. Analytical Chemistry 81(15), 6489–6495 (2009)CrossRefGoogle Scholar
  8. 8.
    Eiceman, G.A., et al.: Separation of ions from explosives in differential mobility spectrometry by vapor-modified drift gas. Analytical Chemistry 76(17), 4937–4944 (2004)CrossRefGoogle Scholar
  9. 9.
    Eiceman, G.A.: Detection of explosives by differential mobility spectrometry and GC DMS. Abstracts of Papers of the American Chemical Society- ANYL 230, 320 (2005)Google Scholar
  10. 10.
    Morgan, J.T.: Differential mobility spectrometry applications in homeland security, clinical diagnostics and drug discovery. In: ASME International Mechanical Engineering Congress and Exposition, ASME, Chicago (2006)Google Scholar
  11. 11.
    Eiceman, G.A., et al.: Differential mobility spectrometry of chlorocarbons with a micro-fabricated drift tube. Analyst 129(4), 297–304 (2004)CrossRefGoogle Scholar
  12. 12.
    Borsdorf, H., Nazarov, E.G., Miller, R.A.: Time-of-flight ion mobility spectrometry and differential mobility spectrometry: A comparative study of their efficiency in the analysis of halogenated compounds. Talanta 71(4), 1804–1812 (2007)CrossRefGoogle Scholar
  13. 13.
    Krebs, M.D., et al.: Detection of biological and chemical agents using differential mobility spectrometry (DMS) technology. IEEE Sensors Journal 5(4), 696–703 (2005)CrossRefGoogle Scholar
  14. 14.
    Kanu, A.B., Thomas, C.L.P.: The presumptive detection of benzene in water in the presence of phenol with an active membrane-UV photo-ionisation differential mobility spectrometer. Analyst 131(9), 990–999 (2006)CrossRefGoogle Scholar
  15. 15.
    Schmidt, H., et al.: Microfabricated differential mobility spectrometry with pyrolysis gas chromatography for chemical characterization of bacteria. Analytical Chemistry 76(17), 5208–5217 (2004)CrossRefGoogle Scholar
  16. 16.
    Prasad, S., et al.: Analysis of bacterial strains with pyrolysis-gas chromatography/differential mobility spectrometry. Analyst 131(11), 1216–1225 (2006)CrossRefGoogle Scholar
  17. 17.
    Prasad, S., et al.: Analysis of bacteria by pyrolysis gas chromatography-differential mobility spectrometry and isolation of chemical components with a dependence on growth temperature. Analyst 132(10), 1031–1039 (2007)CrossRefGoogle Scholar
  18. 18.
    Prasad, S., et al.: Constituents with independence from growth temperature for bacteria using pyrolysis-gas chromatography/differential mobility spectrometry with analysis of variance and principal component analysis. Analyst 133(6), 760–767 (2008)CrossRefGoogle Scholar
  19. 19.
    Cheung, W., et al.: Discrimination of bacteria using pyrolysis-gas chromatography-differential mobility spectrometry (Py-GC-DMS) and chemometrics. Analyst 134(3), 557–563 (2009)CrossRefGoogle Scholar
  20. 20.
    Ayer, S., Zhao, W.X., Davis, C.E.: Differentiation of Proteins and Viruses Using Pyrolysis Gas Chromatography Differential Mobility Spectrometry (PY/GC/DMS) and Pattern Recognition. IEEE Sensors Journal 8(9-10), 1586–1592 (2008)CrossRefGoogle Scholar
  21. 21.
    Krebs, M.D., et al.: Novel technology for rapid species-specific detection of Bacillus spores. Biomolecular Engineering 23(2-3), 119–127 (2006)CrossRefGoogle Scholar
  22. 22.
    Davis, C.E.: Spore biomarker detection using a MEMS differential mobility spectrometer. In: IEEE Transducers Proceedings (2003)Google Scholar
  23. 23.
    Shnayderman, M., et al.: Species-specific bacteria identification using differential mobility spectrometry and bioinformatics pattern recognition. Analytical Chemistry 77(18), 5930–5937 (2005)CrossRefGoogle Scholar
  24. 24.
    Lambertus, G.R., et al.: Silicon microfabricated column with microfabricated differential mobility spectrometer for GC analysis of volatile organic compounds. Analytical Chemistry 77(23), 7563–7571 (2005)CrossRefGoogle Scholar
  25. 25.
    Lambertus, G.R., et al.: Rapid determination of complex mixtures by dual-column gas chromatography with a novel stationary phase combination and spectrometric detection. Journal of Chromatography A 1135(2), 230–240 (2006)CrossRefGoogle Scholar
  26. 26.
    Rainsberg, M.R., Harrington, P.D.B.: Thermal desorption solid-phase microextraction inlet for differential mobility spectrometry. Applied Spectroscopy 59(6), 754–762 (2005)CrossRefGoogle Scholar
  27. 27.
    Levin, D.S., et al.: Rapid separation and quantitative analysis of peptides using a new nanoelectrospray-differential mobility spectrometer-mass spectrometer system. Analytical Chemistry 78(15), 5443–5452 (2006)CrossRefGoogle Scholar
  28. 28.
    Levin, D.S., et al.: Characterization of gas-phase molecular interactions on differential mobility ion behavior utilizing an electrospray ionization-differential mobility-mass spectrometer system. Analytical Chemistry 78(1), 96–106 (2006)CrossRefGoogle Scholar
  29. 29.
    Levin, D.S., et al.: Using a nanoelectrospray-differential mobility spectrometer-mass spectrometer system for the analysis of oligosaccharides with solvent selected control over ESI aggregate ion formation. Journal of the American Society for Mass Spectrometry 18(3), 502–511 (2007)CrossRefGoogle Scholar
  30. 30.
    Shvartsburg, A.A., et al.: Ultrafast Differential Ion Mobility Spectrometry at Extreme Electric Fields Coupled to Mass Spectrometry. Analytical Chemistry 81(19), 8048–8053 (2009)CrossRefGoogle Scholar
  31. 31.
    Zhao, W., et al.: Two-dimensional wavelet analysis based classification of gas chromatogram differential mobility spectrometry signals. Analytica Chimica Acta 647(1), 46–53 (2009)CrossRefGoogle Scholar
  32. 32.
    Awan, M.A., Fleet, I., Thomas, C.L.P.: Optimising cell temperature and dispersion field strength for the screening for putrescine and cadaverine with thermal desorption-gas chromatography-differential mobility spectrometry. Analytica Chimica Acta 611(2), 226–232 (2008)CrossRefGoogle Scholar
  33. 33.
    Lu, Y., Harrington, P.B.: Forensic application of gas chromatography - Differential mobility spectrometry with two-way classification of ignitable liquids from fire debris. Analytical Chemistry 79(17), 6752–6759 (2007)CrossRefGoogle Scholar
  34. 34.
    Rearden, P., et al.: Fuzzy rule-building expert system classification of fuel using solid-phase microextraction two-way gas chromatography differential mobility spectrometric data. Analytical Chemistry 79(4), 1485–1491 (2007)CrossRefGoogle Scholar
  35. 35.
    Lu, Y., Chen, P., Harrington, P.B.: Comparison of differential mobility spectrometry and mass spectrometry for gas chromatographic detection of ignitable liquids from fire debris using projected difference resolution. Analytical and Bioanalytical Chemistry 394(8), 2061–2067 (2009)CrossRefGoogle Scholar
  36. 36.
    Molina, M.A., et al.: Design-of-experiment optimization of exhaled breath condensate analysis using a miniature differential mobility spectrometer (DMS). Analytica Chimica Acta 628(2), 155–161 (2008)CrossRefGoogle Scholar
  37. 37.
    Davis, C.E., et al.: Volatile and non-volatile analysis of biomarkers in human breath using differential mobility spectrometry. IEEE Sensors Journal 13(1), 1–9 (2010)Google Scholar
  38. 38.
    Sankaran, S., et al.: Microfabricated differential mobility spectrometers for breath analysis. In: IEEE Sensors, Atlanta, GA, pp. 16–19 (2007)Google Scholar
  39. 39.
    Frank, M., et al.: Modular sampling and analysis techniques for real-time analysis of human breath. In: IEEE Sensors, Atlanta, GA, pp. 10–13 (2007)Google Scholar
  40. 40.
    Lewis, N.S.: Comparisons between mammalian and artificial olfaction based on arrays of carbon black-polymer composite vapor detectors. Accounts of Chemical Research 37(9), 663–672 (2004)CrossRefGoogle Scholar
  41. 41.
    Friedrich, M.J.: Scientists Seek to Sniff Out Diseases Electronic “Noses” Someday Be Diagnostic Tools. Jama-Journal of the American Medical Association 301(6), 585–586 (2009)CrossRefGoogle Scholar
  42. 42.
    Fens, N., et al.: Exhaled Breath Profiling Enables Discrimination of Chronic Obstructive Pulmonary Disease and Asthma. American Journal of Respiratory and Critical Care Medicine 180(11), 1076–1082 (2009)CrossRefGoogle Scholar
  43. 43.
    Dragonieri, S., et al.: An electronic nose in the discrimination of patients with non-small cell lung cancer and COPD. Lung Cancer 64(2), 166–170 (2009)CrossRefGoogle Scholar
  44. 44.
    Thaler, E.R., Hanson, C.W.: Use of an electronic nose to diagnose bacterial sinusitis. American Journal of Rhinology 20(2), 170–172 (2006)Google Scholar
  45. 45.
    Thaler, E.R., et al.: Use of an electronic nose for detection of biofilms. American Journal of Rhinology 22(1), 29–33 (2008)CrossRefMathSciNetGoogle Scholar
  46. 46.
    Fend, R., et al.: Prospects for clinical application of electronic-nose technology to early detection of Mycobacterium tuberculosis in culture and sputum. Journal of Clinical Microbiology 44(6), 2039–2045 (2006)CrossRefGoogle Scholar
  47. 47.
    Zhao, W., et al.: Machine learning: a crucial tool for developing sensors. Algorithms 1(2), 130–152 (2008)CrossRefGoogle Scholar
  48. 48.
    Krebs, M.D., et al.: Two-dimensional alignment of differential mobility spectrometer data. Sensors and Actuators B-Chemical 119(2), 475–482 (2006)CrossRefMathSciNetGoogle Scholar
  49. 49.
    Liu, Y.H.: Wavelet feature extraction for high-dimensional microarray data. Neurocomputing 72(4-6), 985–990 (2009)CrossRefGoogle Scholar
  50. 50.
    Akaho, S.: Conditionally independent component analysis for supervised feature extraction. Neurocomputing 49, 139–150 (2002)MATHCrossRefGoogle Scholar
  51. 51.
    Gualdron, O., et al.: Variable selection for support vector machine based multisensor systems. Sensors and Actuators B-Chemical 122(1), 259–268 (2007)CrossRefGoogle Scholar
  52. 52.
    Ochoa, M.L., Harrington, P.B.: Chemometric studies for the characterization and differentiation of microorganisms using in situ derivatization and thermal desorption ion mobility spectrometry. Analytical Chemistry 77(3), 854–863 (2005)CrossRefGoogle Scholar
  53. 53.
    Walczak, B., Wu, W.: Fuzzy warping of chromatograms. Chemometrics and Intelligent Laboratory Systems 77(1-2), 173–180 (2005)Google Scholar
  54. 54.
    Alsberg, B.K., et al.: Classification of pyrolysis mass spectra by fuzzy multivariate rule induction-comparison with regression, K-nearest neighbour, neural and decision-tree methods. Analytica Chimica Acta 348(1-3), 389–407 (1997)CrossRefGoogle Scholar
  55. 55.
    Nazarov, E.G., et al.: Pressure effects in differential mobility spectrometry. Analytical Chemistry 78(22), 7697–7706 (2006)CrossRefGoogle Scholar
  56. 56.
    Borsdorf, H., Nazarov, E.G., Miller, R.A.: Atmospheric-pressure ionization studies and field dependence of ion mobilities of isomeric hydrocarbons using a miniature differential mobility spectrometer. Analytica Chimica Acta 575(1), 76–88 (2006)CrossRefGoogle Scholar
  57. 57.
    Zhao, W., Davis, C.E.: Swarm intelligence based wavelet coefficient feature selection for mass spectral classification: an application to proteomics data. Analytica Chimica Acta 651(1), 15–23 (2009)CrossRefGoogle Scholar
  58. 58.
    Timmis, J., Neal, M., Hunt, J.: An artificial immune system for data analysis. Biosystems 55(1-3), 143–150 (2000)CrossRefGoogle Scholar
  59. 59.
    Watkins, A.B., Boggess, L.C.: A resource limited artificial immune classifier. In: Proceedings of the, Congress on Evolutionary Computation. CEC 2002, vol. 1/2, vol.xxxvi+2034, pp. 926–931 (2002) (Cat. No.02TH8600)Google Scholar
  60. 60.
    Zhao, W., Davis, C.E.: Autoregressive model based feature extraction method for time shifted chromatography data. Chemometrics and Intelligent Laboratory Systems 96(2), 252–257 (2009)CrossRefGoogle Scholar
  61. 61.
    Zhao, W., Morgan, J.T., Davis, C.E.: Gas chromatography data classification based on complex coefficients of an autoregressive model. Journal of Sensors 1(1), 1–8 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Weixiang Zhao
    • 1
    • 3
  • Abhinav Bhushan
    • 1
    • 3
  • Michael Schivo
    • 2
    • 3
  • Nicholas J. Kenyon
    • 2
    • 3
  • Cristina E. Davis
    • 1
    • 3
  1. 1.Mechanical and Aerospace Engineering 
  2. 2.Division of Pulmonary and Critical Care Medicine 
  3. 3.Clinical and Translational Science CenterUniversity of California DavisDavisUSA

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