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Lp-norm-residual constrained regularization model for estimation of particle size distribution in dynamic light scattering

Journal Article


Abstract


  • In particle size measurement using dynamic light scattering (DLS), noise makes the estimation of the particle size distribution (PSD) from the autocorrelation function data unreliable, and a regularization technique is usually required to estimate a reasonable PSD. In this paper, we propose an Lp-norm-residual constrained regularization model for the estimation of the PSD from DLS data based on the Lp norm of the fitting residual. Our model is a generalization of the existing, commonly used L2-norm-residual-based regularization methods such as CONTIN and constrained Tikhonov regularization. The estimation of PSDs by the proposed model, using different Lp norms of the fitting residual for p = 1, 2, 10, and ∞, is studied and their performance is determined using simulated and experimental data. Results show that our proposed model with p = 1 is less sensitive to noise and improves stability and accuracy in the estimation of PSDs for unimodal and bimodal systems. The model with p = 1 is particularly applicable to the noisy or bimodal PSD cases.

Publication Date


  • 2017

Citation


  • Zhu, X., Li, J., Thomas, J. C., Song, L., Guo, Q., & Shen, J. (2017). Lp-norm-residual constrained regularization model for estimation of particle size distribution in dynamic light scattering. Applied Optics, 56(19), 5360-5368. doi:10.1364/AO.56.005360

Scopus Eid


  • 2-s2.0-85021647928

Start Page


  • 5360

End Page


  • 5368

Volume


  • 56

Issue


  • 19

Abstract


  • In particle size measurement using dynamic light scattering (DLS), noise makes the estimation of the particle size distribution (PSD) from the autocorrelation function data unreliable, and a regularization technique is usually required to estimate a reasonable PSD. In this paper, we propose an Lp-norm-residual constrained regularization model for the estimation of the PSD from DLS data based on the Lp norm of the fitting residual. Our model is a generalization of the existing, commonly used L2-norm-residual-based regularization methods such as CONTIN and constrained Tikhonov regularization. The estimation of PSDs by the proposed model, using different Lp norms of the fitting residual for p = 1, 2, 10, and ∞, is studied and their performance is determined using simulated and experimental data. Results show that our proposed model with p = 1 is less sensitive to noise and improves stability and accuracy in the estimation of PSDs for unimodal and bimodal systems. The model with p = 1 is particularly applicable to the noisy or bimodal PSD cases.

Publication Date


  • 2017

Citation


  • Zhu, X., Li, J., Thomas, J. C., Song, L., Guo, Q., & Shen, J. (2017). Lp-norm-residual constrained regularization model for estimation of particle size distribution in dynamic light scattering. Applied Optics, 56(19), 5360-5368. doi:10.1364/AO.56.005360

Scopus Eid


  • 2-s2.0-85021647928

Start Page


  • 5360

End Page


  • 5368

Volume


  • 56

Issue


  • 19