Multivariate optimization of field-flow fractionation of nanoscale synthetic amorphous silica in processed foods supported by computational modelling

Abstract

Synthetic amorphous silica (SiO₂) is a highly attractive biomaterial that has been widely used in the food industry to improve the flow properties of powdered ingredients. However, the interactions of food-grade nanoparticulate additives with biomolecules are not fully understood. In this work, a multivariate method was developed for the detection and characterization of SiO₂ particles based on Asymmetric Flow Field-Flow Fractionation (AF4) coupled online to Multi-Angle Light Scattering (MALS) and Dynamic Light Scattering (DLS) detectors. This analytical approach attempts to address the fate and presence of SiO₂ nanoparticulate (NP) additives (E551) in food products. The experimental design using SiO₂-NP standards resulted in the following optimum conditions of the system: crossflow, 0.8 ml min⁻¹; injection time, 5 min; and sonication time, 60 min. The average geometric diameters (D_geo) for SiO₂-NPs in the three selected coffee creamers detected by AF4-MALS were 286.7 nm, 129.8 nm and 190.7 nm, and the hydrodynamic diameters (D_h) detected by AF4-DLS were 301.5 nm, 141.3 nm and 197.8 nm respectively. The calculated shape factor (r_g/r_h) values ranged from 0.7 to 0.8, indicative of the spherical particle geometry. In addition, the electrostatic interaction between SiO₂-NPs and glucose/water mixtures by Monte Carlo simulations revealed the O-interactions dominating over the flat amorphous SiO₂ surface. The strongest interaction observed (around −239 kcal mol⁻¹) for the SiO₂–water/glucose mixture demonstrates the hydrophilic nature of SiO₂-NPs. These findings provide fundamental information to improve the understanding of nanoparticulate interactions of food additives and pave the way for nano-labelling of nanomaterials in complex matrices.