Effect of saliva on tribo-rheological characteristics of concentrated guar gum in both aqueous and milk protein-based emulsion systems

초록

In this study, the rheological and lubricating characteristics of guar gum (GG) at a relatively high concentration (1.0%) were compared between aqueous and milk protein isolate (MPI)-based emulsion systems, and the influence of saliva on these characteristics was examined. All original sample solutions exhibited pseudoplastic flow behavior. The thickened emulsion systems showed higher apparent viscosity and viscoelastic moduli, along with lower flow behavior indices, than the aqueous system. These results indicate the formation of stronger viscoelastic networks in the emulsion systems due to intermolecular interactions between GG and emulsion particles, as supported by FTIR analysis. Following treatment with artificial saliva, the apparent viscosity and viscoelastic moduli of both the aqueous and emulsion systems decreased markedly because of dilution effects. Similarly, water-diluted samples showed lower rheological properties than their original counterparts. However, the saliva-treated emulsion systems exhibited lower apparent viscosity and tan δ values than the corresponding water-diluted systems, suggesting that salivary components influenced the interactions between GG and emulsion particles beyond simple dilution effects. All samples exhibited typical Stribeck curves. Only slight differences in lubrication behavior were observed among the original, saliva-treated, and water-diluted aqueous systems. In contrast, the saliva-treated emulsion systems exhibited lower maximum friction coefficients and a more gradual decrease in friction coefficient throughout the mixed lubrication regime than both the original and water-diluted counterparts. Collectively, these results demonstrate that GG at a relatively high concentration exhibits distinct rheological properties in aqueous and emulsion systems, and that saliva significantly modifies their viscoelastic and lubricating characteristics. © 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

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