Comparation of micro-viscosity of liquid oil in different colloidal fat crystal networks using molecular rotors. (1st July 2020)

• Record Type: Journal Article
• Authors: Chai, Xiuhang; Meng, Zong; Liu, Yuanfa
• Is Part Of: Food chemistry. Volume 317(2020)
• Publication Date: 2020-07-01
• Subjects: PKS palm kernel stearin -- FHRSO fully hydrogenated rapeseed oil -- SO soybean oil -- CR 1-(2, 5-dimethoxy-phenylazo)-naphthalen-2-ol -- XRD X-ray diffraction -- PLM polarized light microscopy -- ANOVA analysis of variance; Micro-viscosity -- Molecular rotor -- Fluorescence emission -- Microstructure -- Fat crystal network -- Fractal dimension; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2020.126382
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: Micro-viscosity of lipid oil in fat matrices was evaluated using molecular rotor. Higher solid fat content resulted in larger micro-viscosity. Smaller thickness of nanocrystal and larger Db led to higher micro-viscosity. Viscosity of bulk oil was not the actual viscosity of liquid oil in fat matrices. Abstract: Micro-viscosity is an important parameter to describe the microenvironment of the fat crystal network. In this study, we evaluated the micro-viscosity of the liquid oil confined in mixtures of palm kernel stearin (PKS)/soybean oil (SO) and fully hydrogenated rapeseed oil (FHRSO)/SO using molecular rotors. The micro-viscosity was shown to increase with solid fat content (SFC), as well as with high proportion of triglycerides that crystallized and formed stronger linked networks. In addition, the thickness of nanocrystals decreased with the increase of solid fat and denser fat crystal network appeared with larger box-counting fractal dimension. Mathematic fit analysis further indicated that molecular confinement of the oil was strongly dependent on the microstructure with high-space filling colloidal fat crystal networks. Larger box-counting fractal dimension led to higher micro-viscosity. However, the critical box-counting fractal dimension was found to be 1.86 irrespective of the nature of the network.