JOURNAL ARTICLE

A general viscosity model for high moisture extrudates of pea protein isolates/gluten blend.

  • Published In: Physics of Fluids, 2025, v. 37, n. 3. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Purcell, Tom; Delaplace, Guillaume; Riaublanc, Alain; Demême, Maxime; Derensy, Antoine; Della Valle, Guy 3 of 3

Abstract

This article focuses on the rheological characterization of extruded blends of 60% pea protein isolate and 40% gluten to better understand their structuring mechanisms during high moisture extrusion (HME). Using a capillary pre-shearing rheometer, Rheoplast®, the study measured shear and extensional viscosities of samples extruded at varying temperatures (130–150 °C) and moisture contents (50–60% w/w), finding that viscosity followed a power-law behavior without wall slip. The time-temperature superposition principle was extended to incorporate water content effects via the glass transition temperature (Tg), enabling master flow curves to be modeled by the Carreau equation across a wide shear rate range. Morphological analysis by electron microscopy revealed that extrusion temperature influenced fibrous structure, correlating with rheological parameters, and extensional viscosity measurements indicated strong resistance to stretching, which may contribute to fiber formation. These findings provide a rheological model useful for simulating protein blend flow in extrusion dies and offer insights for optimizing plant-based meat analog production.

Additional Information

  • Source:Physics of Fluids. 2025/03, Vol. 37, Issue 3, p1
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2025
  • ISSN:1070-6631
  • DOI:10.1063/5.0256835
  • Accession Number:184176225
  • Copyright Statement:Copyright of Physics of Fluids is the property of American Institute of Physics 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.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.