A basic experiment to record dynamic light scattering (DLS) time series was assembled using basic components. The DLS time series processing using the Lorentzian function fit was considered as reference. A Neural Network was designed and trained using simulated frequency spectra for spherical particles in the range 0–350 nm, assumed to be scattering centers, and the neural network design and training procedure are described in detail. The neural network output accuracy was tested both on simulated and on experimental time series. The match with the DLS results, considered as reference, was good serving as a proof of concept for using neural networks in fast DLS time series processing.

This paper presents a simple alternative to dynamic light scattering (DLS) time series processing by using an artificial neural network. A simple experiment for recording a DLS time series is presented. The reference method for DLS time series processing consisted of fitting the analytical form of the Lorentzian line to the frequency spectrum of the recorded scattered light intensity. An artificial neural network with one hidden layer was designed and trained. The training data consisted of a big set of autocorrelations of simulated time series for monodispersed spherical particles with diameters in the range 10–1200 µm. The neural network output precision was tested both on simulated and on experimental time series recorded on fluids containing nanoparticles and microparticles. The errors of the artificial neural network output relative to the reference diameters were small enough and the data processing procedure was three orders of magnitude faster, proving that, in spite of the simplicity, the artificial neural networks approach can be a faster alternative for DLS time series processing.

If coherent light is incident on a suspension containing nanoparticles, they act as scattering centers and the result of the far-field interference is a "speckled" image. The scattering centers have a complex movement of both sedimentation and Brownian motion. Consequently the speckle image is not static but presents time fluctuations. A computer code to simulate the dynamics of the coherent light scattering on nanofluids was written, tested, and used to calculate the far-field intensity variation for nanofluids having different particle size. The results are discussed and an alternative experimental method for fast nanoparticle size assessing is suggested as a possible application.

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Dynamic light scattering (DLS) is an essential technique used for assessing the size of the particles in suspension, covering the range from nanometers to microns. Although it has been very well established for quite some time, improvement can still be brought in simplifying the experimental setup and in employing an easier to use data processing procedure for the acquired time-series. A DLS time series processing procedure based on an artificial neural network is presented with details regarding the design, training procedure and error analysis, working over an extended particle size range. The procedure proved to be much faster regarding time-series processing and easier to use than fitting a function to the experimental data using a minimization algorithm. Results of monitoring the long-time variation of the size of the Saccharomyces cerevisiae during fermentation are presented, including the 10 h between dissolving from the solid form and the start of multiplication, as an application of the proposed procedure. The results indicate that the procedure can be used to identify the presence of bigger particles and to assess their size, in aqueous suspensions used in the food industry.