JOURNAL ARTICLE
Beef and plant‐based burgers' mastication parameters depend on texture rather than on serving conditions.
Published In: Journal of Texture Studies, 2023, v. 54, n. 3. P. 440 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Ilic, Jovan; Djekic, Ilija; Tomasevic, Igor; van den Berg, Marco; Oosterlinck, Filip 3 of 3
Abstract
Previous studies dealing with plant‐based meat analogs confirmed the potential of oral processing methods to identify options for improving those products. Knowing that sensory perception can be influenced by adding condiments, this short communication aimed to investigate the texture and oral processing of four plant‐based burger analogs and a beef burger when consumed in portions or as part of model meals with buns and sides. Texture profile analysis indicated that beef burgers and analog E were the toughest. Two analogs (B and S) showed textures close to beef, while one (analog D) displayed significantly lower values for hardness, toughness, cohesiveness, and springiness. The instrumental data was only partly reflected in the mastication parameters. Adaptations in mastication behavior were expected, but differences between the plant‐based analogs were smaller than anticipated, although clear differences were observed for consumption time, number of chews and number of swallows. On the whole, mastication patterns concurred within different consumption scenarios (portions, model burgers), and significant correlations with instrumental texture were obtained. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of Texture Studies. 2023/06, Vol. 54, Issue 3, p440
- Document Type:Article
- Subject Area:Nutrition and Dietetics
- Publication Date:2023
- ISSN:0022-4901
- DOI:10.1111/jtxs.12763
- Accession Number:164352286
- Copyright Statement:Copyright of Journal of Texture Studies is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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