Help Privacy Policy Disclaimer
  Advanced SearchBrowse




Journal Article

The Effect of Nonrandom Errors on the Results from Regularized Inversions of Dynamic Light Scattering Data


Ruf,  Horst
Department of Biophysical Chemistry, Max Planck Institute of Biophysics, Max Planck Society;


Haase,  Winfried
Department of Structural Biology, Max Planck Institute of Biophysics, Max Planck Society;

External Resource
No external resources are shared
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available

Ruf, H., Gould, B. J., & Haase, W. (2000). The Effect of Nonrandom Errors on the Results from Regularized Inversions of Dynamic Light Scattering Data. Langmuir, 16(2), 471-480. doi:10.1021/la990630i.

Cite as: http://hdl.handle.net/21.11116/0000-0007-B298-4
Dynamic light scattering data from measurements of polystyrene latex beads and lipoprotein particles were analyzed with the regularization algorithms CONTIN and ORT. In addition to the methods for the selection of appropriately regularized solutions of these programs, we applied the method of L-curves. We have studied the effect of systematic errors in the data due to an error in the experimental baseline and of partly correlated errors of the intensity fluctuation noise on the results obtained with these selection methods. The solutions determined with the F-test were most sensitive to the presence of nonrandom errors. Then, the F-test yielded too weakly regularized solutions associated with complex and highly variable size distributions. In this situation, the other two methods (the stability plot and the L-curve method) provided too strongly regularized solutions but with less variable and more reliable size distributions. When data contained only randomly distributed errors, all three selection methods yielded practically the same result. This study confirmed the importance of the accuracy of the baseline. Normalization with the correct value improved the reliability of the size distribution and the quality of the fit by up to 100-fold. Baseline errors were determined with the baseline variation option of the program ORT, where the optimal value is found from the minimum of the mean deviation curve with highest sensitivity for variations of the baseline, and a similar variation method used with CONTIN. The sensitivity parameter, with which the optimal regularization strength is determined, proved to be useful when data contained only random noise but failed in the presence of nonrandom statistical errors. The new method used with CONTIN, where the optimal regularization strength and the solution associated with the optimal baseline were determined with L-curves, was successful in all cases.