Correlating Molecular and Textural Properties of Raw Soy-Based and Beef Burgers Using TD-NMR and TPA
Moshe Hai Azachi1, Zeev Wiesman1
1Phyto-Lipid Biotech Lab (PLBL), Department of Biotechnology Engineering, Faculty, of Engineering Sciences, Ben Gurion University of the Negev, Beer Sheva, Israel.
Abstract:
The accelerating demand for plant-based meat alternatives necessitates advanced analytical approaches capable of characterizing and optimizing texture-a key determinant of consumer acceptance. This study develops an integrated framework combining time-domain nuclear magnetic resonance (TD-NMR) and texture profile analysis (TPA) to elucidate the molecular, structural, and mechanical differences between soy-based plant-based meat analogue (PBMA) burgers and conventional Black Angus beef burgers. Gravimetric and centrifugal assays showed that Angus beef possessed higher total water content and a greater proportion of free and loosely bound water than Soy-PBMA, reflecting stronger water-protein associations in the myofibrillar muscle matrix. TD-NMR relaxation and self-diffusion analyses further demonstrated longer T1/T2 components and higher diffusion coefficients in beef, indicating enhanced water and lipid mobility, improved phase dispersion, and a more cohesive intracellular structure. These molecular features were consistent with TPA results, in which beef exhibited significantly higher hardness, cohesiveness, springiness, and chewiness, reflecting its dense fibrous architecture. Pearson correlation analysis revealed strong associations between NMR parameters and TPA metrics, establishing a mechanistic linkage between water mobility, matrix confinement, and macroscopic texture behavior. Complementary cryo-scanning electron microscopy (Cryo-SEM) imaging visualized these structural contrasts, showing tightly aligned muscle fibers and well-integrated fat globules in beef versus a porous, heterogeneous protein-starch-lipid network in Soy-PBMA. Together, these findings demonstrate that the integrated TD-NMR-TPA approach provides a powerful, non-destructive tool for connecting molecular-scale water dynamics with functional textural properties. This framework offers a predictive foundation for improving formulation, processing, and quality control in next-generation plant-based burgers.
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