Literature DB >> 30256358

Correlation of instrumental texture properties from textural profile analysis (TPA) with eating behaviours and macronutrient composition for a wide range of solid foods.

May Sui Mei Wee1, Ai Ting Goh, Markus Stieger, Ciarán G Forde.   

Abstract

Faster eating rates have previously been associated with higher ad libitum energy intakes, and several studies have manipulated eating rates and intake by changing food textures. Food texture based changes to slow eating rates can produce reductions in energy intake without affecting post-meal satisfaction or re-bound hunger. However, an understanding of how specific food textures and instrumental texture properties influence oral processing behaviour remains limited. The current study sought to establish relationships between objective measures of oral processing behaviour (i.e. number of bites, average bite size, total chews, chews per bite, oro-sensory exposure time and eating rate) and instrumental measures of a food texture including hardness, adhesiveness, springiness, cohesiveness, chewiness, resilience and modulus. Across two studies, behavioural coding analysis was completed on video-recordings of participants consuming fixed portions of a wide range of different solid foods (n = 59) to derive objective measures of oral processing behaviours. These measures were correlated with instrumental Textural Profile Analysis (TPA) for the same set of foods. Significant correlations (p < 0.05) were found between oral processing parameters and texture properties (i.e. springiness, cohesiveness, chewiness and resilience). No significant correlations were found between hardness and modulus and oral processing parameters. Protein content of the food was associated with springiness and chewiness, which may help to further reduce eating rates. In terms of the 'breakdown path model', hardness and modulus might represent degree of initial food structure while springiness, cohesiveness, chewiness and resilience seem to determine how fast the degree of structure is reduced to the swallowing plane. Water content and adhesiveness were associated with level of lubrication that is required before reaching the swallowing plane. The current study highlights opportunities to understand eating rate (g min-1) through the breakdown path model and the potential for specific features of a foods texture to influence rate and extent of energy intake. The correlation between instrumental texture properties and oral processing patterns provides guidance on the parameters that are likely to produce 'faster' and 'slower' versions of foods, and suggests how texture modifications could be applied to moderate eating rate and energy intake within meals.

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Year:  2018        PMID: 30256358     DOI: 10.1039/c8fo00791h

Source DB:  PubMed          Journal:  Food Funct        ISSN: 2042-6496            Impact factor:   5.396


  16 in total

1.  Increased oral processing and a slower eating rate increase glycaemic, insulin and satiety responses to a mixed meal tolerance test.

Authors:  Ai Ting Goh; Jie Ying Michelle Choy; Xin Hui Chua; Shalini Ponnalagu; Chin Meng Khoo; Clare Whitton; Rob Martinus van Dam; Ciarán Gerard Forde
Journal:  Eur J Nutr       Date:  2021-01-02       Impact factor: 5.614

2.  The Comprehensive Utilization of Bean Dregs in High-Fiber Tofu.

Authors:  Wenjing Lu; Yue Zhang; Chaogeng Xiao; Di Chen; Qin Ye; Cen Zhang; Xianghe Meng; Shengjian Wang
Journal:  Foods       Date:  2022-05-19

3.  Texture-based differences in eating rate influence energy intake for minimally processed and ultra-processed meals.

Authors:  Pey Sze Teo; Amanda JiaYing Lim; Ai Ting Goh; Janani R; Jie Ying Michelle Choy; Keri McCrickerd; Ciarán G Forde
Journal:  Am J Clin Nutr       Date:  2022-07-06       Impact factor: 8.472

4.  Assessment of the Miniature Kramer Shear Cell to Measure Both Solid Food and Bolus Mechanical Properties and Their Interplay with Oral Processing Behavior.

Authors:  María Dolores Álvarez; Jaime Paniagua; Beatriz Herranz
Journal:  Foods       Date:  2020-05-11

5.  Influence of almond and coconut flours on Ketogenic, Gluten-Free cupcakes.

Authors:  Lauren Hopkin; Hannah Broadbent; Gene J Ahlborn
Journal:  Food Chem X       Date:  2021-12-06

6.  Influence of Muscle Type on Physicochemical Parameters, Lipolysis, Proteolysis, and Volatile Compounds throughout the Processing of Smoked Dry-Cured Ham.

Authors:  Nives Marušić Radovčić; Ivna Poljanec; Sandra Petričević; Leticia Mora; Helga Medić
Journal:  Foods       Date:  2021-05-28

7.  Association Between Self-Reported Eating Rate, Energy Intake, and Cardiovascular Risk Factors in a Multi-Ethnic Asian Population.

Authors:  Pey Sze Teo; Rob M van Dam; Clare Whitton; Linda Wei Lin Tan; Ciarán G Forde
Journal:  Nutrients       Date:  2020-04-13       Impact factor: 5.717

8.  Influence of the Use of Milk Replacers and pH on the Texture Profiles of Raw and Cooked Meat of Suckling Kids.

Authors:  Guillermo Ripoll; María J Alcalde; María G Córdoba; Rocío Casquete; Anastasio Argüello; Santiago Ruiz-Moyano; Begoña Panea
Journal:  Foods       Date:  2019-11-19

9.  Ultra-Processing or Oral Processing? A Role for Energy Density and Eating Rate in Moderating Energy Intake from Processed Foods.

Authors:  Ciarán G Forde; Monica Mars; Kees de Graaf
Journal:  Curr Dev Nutr       Date:  2020-02-10

10.  Combined Impact of a Faster Self-Reported Eating Rate and Higher Dietary Energy Intake Rate on Energy Intake and Adiposity.

Authors:  Pey Sze Teo; Rob M van Dam; Ciarán G Forde
Journal:  Nutrients       Date:  2020-10-25       Impact factor: 5.717

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