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Virtual Journal for Biomedical Optics

Virtual Journal for Biomedical Optics

| EXPLORING THE INTERFACE OF LIGHT AND BIOMEDICINE

  • Editors: Andrew Dunn and Anthony Durkin
  • Vol. 7, Iss. 12 — Dec. 19, 2012

Analysis of noisy dynamic light scattering data using constrained regularization techniques

Xinjun Zhu, Jin Shen, and John C. Thomas  »View Author Affiliations


Applied Optics, Vol. 51, Issue 31, pp. 7537-7548 (2012)
http://dx.doi.org/10.1364/AO.51.007537


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Abstract

Dynamic light scattering (DLS) from colloidal particles often contains noise, which makes inversion of the correlation function to obtain the particle size distribution (PSD) unreliable. In this work, poor-quality correlation function data with baseline error were analyzed using constrained regularization techniques. The effect of baseline error was investigated, and two strategies were proposed to compensate for baseline error. One strategy is based on edge proportion detection of spurious peaks at large size in the PSD, and the other is based on the solution norm. Results from simulated and experimental data demonstrate the effectiveness of our proposed strategies. The L-curve rules for standard Tikhonov and for constrained regularization, the generalized cross-validation (GCV) rule, and the robust GCV rule were investigated for determination of the regularization parameter. A comparison of these rules was done using both simulated and experimental data. It is shown that correction of baseline error with baseline compensation as well as a reasonable regularization parameter choice improves the accuracy of PSD recovery in poor-quality DLS data analysis.

© 2012 Optical Society of America

OCIS Codes
(290.3200) Scattering : Inverse scattering
(290.3700) Scattering : Linewidth
(290.5820) Scattering : Scattering measurements
(290.5850) Scattering : Scattering, particles
(300.6170) Spectroscopy : Spectra

ToC Category:
Instrumentation, Measurement, and Metrology

History
Original Manuscript: May 7, 2012
Revised Manuscript: August 18, 2012
Manuscript Accepted: September 10, 2012
Published: October 24, 2012

Virtual Issues
Vol. 7, Iss. 12 Virtual Journal for Biomedical Optics

Citation
Xinjun Zhu, Jin Shen, and John C. Thomas, "Analysis of noisy dynamic light scattering data using constrained regularization techniques," Appl. Opt. 51, 7537-7548 (2012)
http://www.opticsinfobase.org/vjbo/abstract.cfm?URI=ao-51-31-7537


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References

  1. R. Pecora, Dynamic Light Scattering: Application of Photon Correlation Spectroscopy (Plenum, 1985).
  2. F. Scheffold, A. Shalkevich, R. Vavrin, J. Crassous, and P. Schurtenberger, PCS Particle Sizing in Turbid Suspensions: Scope and Limitations (ACS, 2004).
  3. J. C. Thomas, “Photon correlation spectroscopy: technique and instrumentation,” Proc. SPIE 1430, 2–18 (1991). [CrossRef]
  4. Y. Li, V. Lubchenko, and P. G. Vekilov, “The use of dynamic light scattering and Brownian microscopy to characterize protein aggregation,” Rev. Sci. Instrum. 82, 053106(2011). [CrossRef]
  5. S. W. Provencher, “A constrained regularization method for inverting data represented by linear algebraic or integral equations,” Comput. Phys. Commun. 27, 213–227 (1982). [CrossRef]
  6. D. A. Ross and H. S. Dhadwal, “Regularized inversion of the Laplace transform: accuracy of analytical and discrete inversion,” Part. Part. Syst. Charact. 8, 282–286 (1991). [CrossRef]
  7. H. Schnablegger and O. Glatter, “Optical sizing of small colloidal particles: an optimized regularization technique,” Appl. Opt. 30, 4889–4896 (1991). [CrossRef]
  8. S. W. Provencher and P. Stepanek, “Global analysis of dynamic light scattering autocorrelation functions,” Part. Part. Syst. Charact. 13, 291–294 (1996). [CrossRef]
  9. M. J. Fernandes, N. C. Santos, and M. Castanho, “Continuous particle size distribution analysis with dynamic light scattering: MAXAMPER: a regularization method using the maximum amplitude for the average error and the Lagrange’s multipliers method,” J. Biochem. Biophys. Methods 36, 101–117 (1998). [CrossRef]
  10. V. M. Gun’ko, A. V. Klyueva, Y. N. Levchuk, and R. Leboda, “Photon correlation spectroscopy investigations of proteins,” Adv. Colloid Interface Sci. 105, 201–328 (2003). [CrossRef]
  11. X. Zhu, J. Shen, Y. Wang, J. Guan, X. Sun, and X. Wang, “The reconstruction of particle size distributions from dynamic light scattering data using particle swarm optimization techniques with different objective functions,” Opt. Laser Technol. 43, 1128–1137 (2011). [CrossRef]
  12. X. Zhu, J. Shen, W. Liu, X. Sun, and Y. Wang, “Nonnegative least-squares truncated singular value decomposition to particle size distribution inversion from dynamic light scattering data,” Appl. Opt. 49, 6591–6596 (2010). [CrossRef]
  13. A. E. Smart, R. V. Edwards, and W. V. Meyer, “Quantitative simulation of errors in correlation analysis,” Appl. Opt. 40, 4064–4078 (2001). [CrossRef]
  14. H. Ruf, “Effects of normalization errors on size distributions obtained from dynamic light scattering data,” Biophys. J. 56, 67–78 (1989). [CrossRef]
  15. B. B. Weiner and W. W. Tscharnuter, Uses and Abuses of Photon Correlation Spectroscopy in Particle Sizing (ACS, 1987).
  16. H. Ruf, B. Gould, and W. Haase, “The effect of nonrandom errors on the results from regularized inversions of dynamic light scattering data,” Langmuir 16, 471–480(2000). [CrossRef]
  17. H. Ruf, “Treatment of contributions of dust to dynamic light scattering data,” Langmuir 18, 3804–3814 (2002). [CrossRef]
  18. D. A. Lowther, R. D. Throne, L. G. Olson, and J. R. Windle, “A comparison of two methods for choosing the regularization parameter for the inverse problem of electrocardiography,” Biomed. Sci. Instrum. 38, 257–261 (2002).
  19. C. G. Farquharson and D. W. Oldenburg, “A comparison of automatic techniques for estimating the regularization parameter in non-linear inverse problems,” Geophys. J. Int. 156, 411–425 (2004). [CrossRef]
  20. H. G. Choi, A. N. Thite, and D. J. Thompson, “Comparison of methods for parameter selection in Tikhonov regularization with application to inverse force determination,” J. Sound Vib. 304, 894–917 (2007). [CrossRef]
  21. F. Bauer and M. A. Lukas, “Comparing parameter choice methods for regularization of ill-posed problems,” Math. Comput. Simul. 81, 1795–1841 (2011). [CrossRef]
  22. K. Schatzel, M. Drewel, and S. Stimac, “Photon correlation measurements at large lag times: improving statistical accuracy,” J. Mod. Opt. 35, 711–718 (1988). [CrossRef]
  23. P. Stepanek, Z. Tuzar, P. Kadlec, and J. Kriz, “A dynamic light scattering study of fast relaxations in polymer solutions,” Macromolecules 40, 2165–2171 (2007). [CrossRef]
  24. K. Sumitomo, K. Mayumi, and H. Yokoyama, “Dynamic light scattering measurement of sieving polymer solutions for protein separation on SDS CE,” Electrophoresis 30, 3607–3612 (2009). [CrossRef]
  25. T. Roths and J. Honerkamp, “Simultaneous regularization method for the determination of radius distributions from experimental multiangle correlation functions,” Phys. Rev. E 64, 041404 (2001). [CrossRef]
  26. P. C. Hansen and D. P. O’Leary, “The use of L-curve in the regularization of discrete ill-posed problems,” SIAM J. Sci. Comput. 14, 1487–1503 (1993). [CrossRef]
  27. G. H. Golub, M. Heath, and G. Wahba, “Generalized cross-validation as a method for choosing a good ridge parameter,” Technometrics 21, 215–223 (1979). [CrossRef]
  28. M. A. Lukas, “Robust generalized cross-validation for choosing the regularization parameter,” Inverse Probl. 22, 1883–1902 (2006). [CrossRef]
  29. P. C. Hansen, “Regularization Tools Version 4.0 for Matlab 7.3,” Numer. Algorithms 46, 189–194 (2007). [CrossRef]
  30. A. B. Yu and N. Standish, “A study of particle size distribution,” Powder Technol. 62, 101–118 (1990). [CrossRef]
  31. Y. Sun and J. Walker, “Maximum likelihood data inversion for photon correlation spectroscopy,” Meas. Sci. Technol. 19, 115302 (2008). [CrossRef]
  32. L. A. Clementi, J. R. Vega, L. M. Gugliotta, and H. Orlande, “A Bayesian inversion method for estimating the particle size distribution of latexes from multiangle dynamic light scattering measurements,” Chemom. Intell. Lab. Syst. 107, 165–173 (2011). [CrossRef]

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