Lasers in Medical Science

, Volume 13, Issue 1, pp 32–41

Quantification of polydimethylsiloxane concentration in turbid samples using raman spectroscopy and the method of partial least squares

  • A. J. Durkin
  • M. N. Ediger
  • G. H. Pettit
Article

Abstract

This paper presents a preliminary application of Raman spectroscopy in conjunction with the chemometric method of partial least squares to predict silicone concentrations in homogenous turbid samples. The chemometric technique is applied to Raman spectra to develop an empirical, linear model relating sample spectra to polydimethylsiloxane (silicone) concentration. This is done using a training set of samples having optical properties and known concentrations representative of those unknown samples to be predicted. Partial least squares, performed via cross-validation, was able to predict silicone concentrations in good agreement with true values. The detection limit obtained for this preliminary investigation is similar to that reported in the magnetic resonance spectroscopy literature. The data acquisition time for this Raman-based method is 200 s which compares favourably with the 17 h acquisition required for magnetic resonance spectroscopy to obtain a similar sensitivity. The combination of Raman spectroscopy and chemometrics shows promise as a tool for quantification of silicone concentrations from turbid samples.

Keywords

Biomedical diagnostics Breast implant leakage Optical spectroscopy Partial least squares Polydimethylsiloxane (PDMS) Raman spectroscopy Silicone 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Franck CJ, McCreery RL, Redd DCB, Gansler TS. Detection of silicone in lymph node biopsy specimens by near-infrared Raman spectroscopy. Appl Spectrosc 1993; 47:387–90Google Scholar
  2. 2.
    Kessler DA. The basis of the FDA's decision on breast implants. N Engl J Med 1992; 326:1713–5Google Scholar
  3. 3.
    Gylbert L, Asplund O, Jurell G. Capsular contracture after breast reconstruction with silicone-gel and saline-filled implants: a 6-year follow-up. Plast Reconstr Surg 1990; 85:373–7Google Scholar
  4. 4.
    Brandt B, Breiting V, Christensen L, Nielsen M, Thomsen JL. Five years experience of breast augmentation using silicone gel prostheses with emphasis on capsule shrinkage. Scand J Plast Reconstr Surg 1984; 18:311–6Google Scholar
  5. 5.
    Asplund O. Capsular contracture in silicone gel and saline-filled breast implants after reconstruction. Plast Reconstr Surg 1984; 73:270–5Google Scholar
  6. 6.
    Hausner RJ, Schoen FJ, Mendez-Fernandez MA, Henly WS, Geis RC. Migration of silicone gel to axillary lymph nodes after prosthetic mammoplasty. Arch Pathol Lab Med 1981; 105:371–2Google Scholar
  7. 7.
    Truong LD, Cartwright Jr J, Goodman MD, Woznicki D. Silicone lymphadenopathy associated with augmentation mammoplasty — morphologic features of nine cases. Am J Surg Pathol 1988; 12:484Google Scholar
  8. 8.
    Garrido L, Pfleiderer B, Jenkins BC, Hulka CA, Kopans DB. Migration and chemical modification of silicone in women with breast prostheses. Magn Reson Med 1994; 31:328–30Google Scholar
  9. 9.
    Barker DE, Retsky MI, Schultz S. Bleeding of silicone from bag-gel breast implants, and its clinical relation to fibrous capsule reaction. Plast Reconstr Surg 1978; 61:836–41Google Scholar
  10. 10.
    Teuber SS, Yoshida SH, Gershwin ME. Immunopathologic effects of silicone breast implants. West J Med 1995; 162:418–25Google Scholar
  11. 11.
    Bridges A, Conley C, Burns D, Vasey F. A clinical and immunological evaluation of women with silicone breast implants and symptoms of rheumatic disease. Ann Intern Med 1993; 118:929Google Scholar
  12. 12.
    Pfleiderer B, Ackerman JL, Garrido L. Migration and biodegradation of free silicone from silicone gel-filled implants after long-term implantation. Magn Reson Med 1993; 30:534–43Google Scholar
  13. 13.
    Sanchez-Guerrero J, Colditz GA, Karlson EW, Hunter DJ, Speizer FE, Liang MH. Silicone breast implants and the risk of connective-tissue diseases and symptoms. N Engl J Med 1995; 332:1666–70Google Scholar
  14. 14.
    Centeno JA, Johnson FB. Microscopic identification of silicone in human breast tissues by infrared microscopy and X-ray microanalysis. Appl Spectrosc 1993; 47:341–5Google Scholar
  15. 15.
    Ahn CY, DeBruhl ND, Gorczyca DP, Basset LW, Shaw WW. Silicone implant rupture diagnosis using computed tomography: a case report and experience with 22 surgically removed implants. Ann Plast Surg 1994; 33:624Google Scholar
  16. 16.
    Rowley MJ, Cook AD, Teuber SS. Antibodies to collagen: comparative epitope mapping in women with silicone breast implants, systemic lupus erythematosus, and rheumatoid arthritis. J Autoimmun 1994; 7:775–89Google Scholar
  17. 17.
    Kossovsky N, Zeidler M, Chun G. Surface dependent antigens identified by high binding avidity of serum antibodies in a sub-population of patients with breast prostheses. J Appl Biomater 1993; 4:281–8Google Scholar
  18. 18.
    Evans GRD, Slezak S, Rieters M, Bercowy GM. Silicon tissue assays in nonaugmented cadaveric patients: is there a baseline level? Plast Reconstr Surg 1994; May 22:1117–20Google Scholar
  19. 19.
    Raso DS, Greene WB, Vesely JJ, Willingham MC. Light microscopy techniques for the demonstration of silicone gel. Arch Pathol Lab Med 1994; 118:984Google Scholar
  20. 20.
    Levine RA, Collins T. Definitive diagnosis of breast implant rupture by ultrasonography. Plast Reconstr Surg 1990; 86:803Google Scholar
  21. 21.
    Manoharan R, Baraga JJ, Feld MS, Rava RP. Quantitative histochemical analysis of human artery using Raman spectroscopy. J Photochem Photobiol B: Biol 1992; 16:211Google Scholar
  22. 22.
    Durkin AJ, Richards-Kortum R. A comparison of methods to determine chromophore concentrations from fluorescence spectra of turbid samples. Lasers Surg Med 1996; 19:75–89Google Scholar
  23. 23.
    Robinson MR, Eaton RP, Haaland DM et al. Noninvasive glucose monitoring in diabetic patients: a preliminary evaluation. Clin Chem 1992; 38:1618–22Google Scholar
  24. 24.
    Haaland DM. Multivariate calibration methods applied to quantitative FT-IR analyses. In: Ferraro JR, Krishnan K (eds) Practical Fourier Infrared Spectroscopy. New York: Academic Press, 1990:395–468Google Scholar
  25. 25.
    Bigio IJ, Mourant JR, Boyer JD. Non-invasive identification of bladder cancer with subsurface backscattered light. In: Alfano RR, Katzir A (eds) Advances in Laser and Light Spectroscopy to Diagnose Cancer and other Diseases. Bellingham, Washington: SPIE Press, 1994:2135Google Scholar
  26. 26.
    Mahadevan-Jansen A, Richards-Kortum R. Raman spectroscopy for the detection of cancers and pre-cancers. J Biomed Optics 1996; 1:31–70Google Scholar
  27. 27.
    Wicksted JP, Erckens RJ, Motamedi M. Monitoring of aqueous humor metabolites using Raman spectroscopy. In: Alfano RR, Katzir A (eds) Advances in Laser and Light Spectroscopy to Diagnose Cancer and other Diseases. Bellingham, Washington: SPIE Press, 1994; 2135:264–74Google Scholar
  28. 28.
    Cooper JB, Flecher PE, Vess TM, Welch WT. Remote fiber optic Raman analysis of xylene isomers in mock petroleum fuels using a low-cost dispersive instrument and partial least-squares regression analysis. Appl Spectrosc 1995; 49:586–92Google Scholar
  29. 29.
    Morris AR, Hoyt CC, Miller P, Treado PJ. Liquid crystal tunable filter Raman chemical imaging. Appl Spectrosc 1997; 50:805–11Google Scholar
  30. 30.
    Schaeberle MD, Karakatsanis CG, Lau CL, Treado PJ. Raman chemical imaging: noninvasive visualization of polymer blend architecture. Anal Chem 1995; 67:4316–21Google Scholar
  31. 31.
    Kidder L, Kalasinsky VF, Luke JL, Levin IW, Lewis EN. Visualization of silicone gel in human breast tissue using new infrared imaging spectroscopy. Nature Med 1997; 3:235–7Google Scholar
  32. 32.
    Malinowski ER. Factor Analysis in Chemistry. New York: John Wiley, 1991:169–72Google Scholar
  33. 33.
    Durkin AJ, Gardner CM, Richards-Kortum R. Comparison of methods for determining fluorophore concentrations from turbid samples, Proceedings from the Conference on Lasers and Electro-optics (CLEO), 1994Google Scholar
  34. 34.
    Thomas EV, Haaland DM. Comparison of multivariate calibration methods for quantitative spectral analysis. Anal Chem 1990; 62:1091–9Google Scholar
  35. 35.
    Durkin AJ, Jaikumar S, Richards-Kortum R. Optically dilute, absorbing and turbid phantoms for fluorescence spectroscopy of homogeneous and inhomogeneous samples. Appl Spectrosc 1993; 47:2114–21Google Scholar
  36. 36.
    Centeno JA, Kalasinsky VF, Johnson FB, Vihn TN, O'Leary TJ. Fourier transform infrared microscopic identification of foreign materials in tissue sections. Lab Invest 1992; 66:123–31Google Scholar
  37. 37.
    Centeno JA, Johnson FB, Kalasinsky VF, Mullick FG, Pestaner JP. Microspectroscopic evaluation of foreign inclusions in human pathology: the laser Raman microprobe technique, Poster presented at United States and Canadian Academy of Pathology, Annual Meeting, Toronto, Canada, 1995Google Scholar
  38. 38.
    Myrick ML, Angel SM. Elimination of background in fiber optic Raman measurements. Appl Spectrosc 1990; 44:565–70Google Scholar
  39. 39.
    Centeno JA, Luke JL, Kalasinsky VF, Mullick FG. Biophysical characterization of silicone breast implants by laser Raman microprobe and infrared microspectroscopy, Preprints of Papers Presented at the 208th ACS National Meeting, 1994; 34:132Google Scholar
  40. 40.
    Berger AJ, Wang Y, Sammeth DM, Itzkan I, Kneipp K, Feld MS. Aqueous dissolved gas measurements using near-infrared Raman spectroscopy. Appl Spectrosc 1995; 49:1164–6Google Scholar
  41. 41.
    Berger AJ, Wang Y, Feld MS. Rapid noninvasive concentration measurements of aqueous biological analytes of near-infrared Raman spectroscopy. Appl Optics 1996; 35:209–12Google Scholar
  42. 42.
    Tanaka K, Pacheco M, Brennan JF et al. Compound parabolic concentrator probe for efficient light collection in spectroscopy of biological tissue. Appl Optics 1996; 35:758–63Google Scholar
  43. 43.
    Small GW, Arnold MA, Marquardt LA. Strategies for coupling digital filtering with partial least squares regression: application to the determination of glucose in plasma by Fourier transform near-infrared spectroscopy. Anal Chem 1993; 65:3279–89Google Scholar

Copyright information

© Springer-Verlag 1998

Authors and Affiliations

  • A. J. Durkin
    • 1
  • M. N. Ediger
    • 1
  • G. H. Pettit
    • 2
  1. 1.Electro-optics branch of the FDA Center for Devices and Radiological HealthRockville
  2. 2.Autonomous Technologies CorporationOrlandoUSA
  3. 3.FDA/CDRH/HFZ-134National Research Council Research AssociateRockvilleUSA

Personalised recommendations