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Analysis of noisy dynamic light scattering data using constrained regularization techniques

机译:使用约束正则化技术分析嘈杂的动态光散射数据

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摘要

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.
机译:来自胶体颗粒的动态光散射(DLS)通常包含噪声,这使得相关函数的求逆无法获得可靠的粒径分布(PSD)。在这项工作中,使用约束正则化技术分析了具有基线误差的劣质相关函数数据。研究了基线误差的影响,并提出了两种策略来补偿基线误差。一种策略是基于PSD中大尺寸伪峰的边缘比例检测,另一种策略是基于解范数。来自模拟和实验数据的结果证明了我们提出的策略的有效性。研究了标准Tikhonov和约束正则化的L曲线规则,广义交叉验证(GCV)规则和鲁棒GCV规则,以确定正则化参数。使用模拟和实验数据对这些规则进行了比较。结果表明,通过基线补偿对基线误差进行校正以及合理的正则化参数选择,可以提高劣质DLS数据分析中PSD恢复的准确性。

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