Physiologically relevant concentrations of blueberry-derived aglycone phenolic acids can induce Nrf2-regulated antioxidant response proteins in vascular endothelial cells in response to low μm concentrations of H O . Our results represent an advance over previous studies that have used single compounds or high concentrations in cell-based investigations.

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The term textural complexity is associated with a range of different perceivable textures and sensations occurring from first bite through to swallow. The aim of this study was to develop gel‐based model foods with “built‐in” levels of textural complexity for use in a wider study of the impact of texture on satiation and satiety. The model foods needed to be quantified for textural complexity and that assessment was conducted using instrumental and sensory measurements model foods of different structural complexity (based on inclusions and layers with differing mechanical properties) resulted in puncture curves with differing numbers of peaks caused by the sequential puncture of structural features, and in differing lengths; in general higher complexity led to a greater number of peaks and greater length. Consistent with the definition of textural complexity, more texturally complex samples generated a greater number of descriptors during the “chewdown” phase in descriptive sensory analysis. Temporal dominance of sensation (TDS) analysis gave different information on the dynamics of texture perception during chewing sequence compared to descriptive analysis but confirmed the levels of textural complexity of the model foods. In particular TDS indicates the evolution of the number of texture attributes perceived, and the number of times the dominant texture changed. Practical Applications These gel‐based model foods with different textural complexity, similar nutritional densities and comparable chewing time can be used for investigating the hypothesized relationship between textural complexity and satiation. The instrumental and sensory tests used in this study proved capable of differentiating levels of textural complexity. The findings of Quantitative Descriptive Analysis and Temporal Dominance of Sensation, and using the number of peaks and length of a puncture curve to quantify textural complexity could be expanded on in the future to create standardised tests.

The aim of this study was to examine textural complexity by developing a model food with varying levels of textural complexity but comparable nutritional density. Embedding inclusions in a gel matrix and layering the samples created different levels of structural and textural complexity: low, medium, and high. Texture properties and textural complexity were analyzed by generic quantitative descriptive analysis and modified texture profiling. The total number of unique textural descriptors observed by the panellists was used as a rudimentary measure of textural complexity and an increasing trend of textural complexity, low complexity < medium complexity < high complexity was observed. Ten unique descriptors showed significantly greater intensity ratings (p < 0.05) for the high complexity sample. This suggested that these textural attributes may be more likely to be distinguished during consumption of a high complexity sample compared to the low complexity or medium complexity samples, leading to greater perceived textural complexity in the high complexity sample.