On the Possibility of Analyzing Dynamic Light Scattering Time Series Using the Co-Occurrence Matrix and Artificial Neural Networks

A Dynamic Light Scattering time series can be analyzed using several well-established procedures. An alternative is to compute a co-occurrence matrix of the time series and to establish a correlation between the moments of the intensity level distribution and the physical properties of the particles in suspension using an Artificial Neural Network. We performed a comprehensive error analysis of the proposed method for estimating particle diameter from the simulated Dynamic Light Scattering (DLS) time series, both on simulated and on experimental data. The results demonstrate the potential of utilizing moments of the co-occurrence matrix as an alternative approach for processing DLS time series data to assess the size of suspended particles.