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The Regularization algorithm utilizes a small a parameter, thereby assuming a low level of noise in the measured correlogram.
#MALVERN ZETASIZER VS DLS FREE#
The Regularization algorithm, written by Maria Ivanova, is a more aggr essive algorithm which has been optimized for dust free small particle samples, such as pure proteins and micelles. As a consequence, near in size particle distribution peaks tend to be blended together in a CONTIN derived size distribution. CONTIN is considered to be a conservative algorithm, in that the choice of the alpha ( a ) parameter, which controls the "smoothness" of the resultant distribution, assumes a moderate level of noise in the measured correlogram.
#MALVERN ZETASIZER VS DLS PRO#
The CONTIN algorithm was originally written by Steven Pro vencher and is has become the industry standard for general DLS analysis.
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the "regularizer" or alpha parameter) within the NNLS algorithm, in order to optimize it for a given set of instrument and sample conditions. What generally makes these named algorithms u nique is the locking of variables (e.g. ALL of these algorithms are Non-Negative Least Squares (NNLS) based algorithm s. There are a variety of "named" algorithms available to light scattering r esearchers, either through the web or through the collection of DLS instrument vendors. The problem with the what’s the best algorithm question is that the answer is not a simple one, in that it depends very much on the typ e of samples being analyzed, the working size range of the instrument being used, and most importantly, the level of noise in the measured correlogram. So th e question, "what’s the best DLS algorithm", is a good question. But in practice there is no such thing as a perfect noise free correlogram, and minimizing the sum of squares error in the presence of noise can lead to erroneous results, with no reproducibility and absolutely zero validity. For a perfect noise free correlation function, this approach would in fact be ideal. One might initially feel that the question is ungrounded, in that the obvious best method for fittin g the correlogram would be to use an iterative approach until the sum of squares error is minimized. A common question from users o f dynamic light scattering instrumentation is what is the best multi-modal algorithm. In the dynamic light scattering (DLS) technique, the distribution of diffusion coefficient s for a collection of particles is calculated by application of a multi-exponential fitting algorithm to the measured correlation curve. What_s_the_best_DLS_algorithm_ What's the best DLS algorithm?