Finite element analysis opens new pathways for plant-based meat texture development

The development of plant-based meat analogs (PBMAs) has gained momentum as a more sustainable and ethical alternative to conventional meat. Yet, one of the biggest challenges in the field remains texture replication—achieving the fibrous structure and sensory attributes of animal-derived meat. A new study published in Physics of Fluids by Jingnan Zhang and Heng Zhu explores the role of finite element analysis (FEA) in overcoming this challenge, demonstrating its potential in optimizing the mechanical, thermodynamic, and mass transfer properties of PBMAs during processing.

The study, titled Finite Element Analysis as a Promising Approach for Texture Development of Plant-Based Meat Analogs, highlights how computational modeling can be leveraged to enhance processing techniques such as high-moisture extrusion, shear cell technology, and extrusion-based 3D printing. These methods are instrumental in structuring plant proteins into fibrous networks that mimic the texture of traditional meat, but inconsistencies in protein alignment and textural stability remain key barriers. Zhang and Zhu argue that integrating FEA into PBMA development could provide a roadmap for more consistent, scalable production.

The fundamental structural differences between plant and animal proteins make it difficult to replicate the texture of conventional meat. While muscle fibers in meat align naturally into organized structures, plant proteins are typically globular and do not self-assemble in the same way. This discrepancy means that PBMA manufacturers must rely on precise processing conditions—such as controlled shear forces, pressure, and temperature—to align plant proteins into fibrous formations that resemble muscle tissue.

Various mechanical techniques have been used to enhance PBMA texture, including high-moisture extrusion (HME), which applies heat, pressure, and shear forces to plant proteins to create structured fibers. Shear cell technology offers a lower-energy alternative, using controlled mechanical shear to align proteins into fibrous networks. Meanwhile, 3D printing allows for more intricate control over texture formation, layer by layer. However, despite these advancements, achieving a consistent, meat-like texture remains difficult due to the variability in plant protein sources and their structural behaviors under different processing conditions.

Finite element analysis is widely used in engineering and materials science to simulate and predict mechanical and thermal behaviors in complex systems. In the food industry, it has been employed in areas such as heat transfer modeling in baking and texture optimization in dough extrusion. Zhang and Zhu’s study suggests that applying FEA to PBMA development could help overcome key textural challenges by predicting how plant proteins will behave under different conditions, allowing for real-time optimization of processing techniques.

For example, FEA could assist in refining extrusion processes by simulating how different shear forces and temperatures affect protein alignment. It could also improve textural consistency by predicting how PBMAs will respond to heating, cooling, and storage. Additionally, multi-physics simulations—which integrate thermal, mechanical, and mass transfer properties—could help manufacturers anticipate moisture migration and phase transitions that influence PBMA juiciness and mouthfeel.

One of the major obstacles in PBMA manufacturing is the variability in plant protein sources. Factors such as protein composition, hydration capacity, and structural integrity can differ significantly between soy, pea, and wheat gluten, impacting the final product’s texture and consistency. Zhang and Zhu propose that FEA, combined with advanced rheological models, could help manufacturers predict and adjust for these differences, leading to more uniform production outcomes.

Additionally, scaling up PBMA production while maintaining texture integrity is a major industry concern. Current experimental approaches to optimizing PBMA texture are time-consuming and costly. By integrating FEA simulations into production workflows, companies could significantly reduce trial-and-error testing, accelerating the development of high-quality plant-based products that meet consumer expectations.

The study suggests that future research should focus on refining FEA models to better capture the unique mechanical behaviors of plant proteins. Advanced non-linear rheological models could further improve predictions of shear-thinning properties and viscoelasticity, which are critical for fibrous texture formation. Moreover, hybrid computational approaches—combining FEA with methods like computational fluid dynamics (CFD) and smoothed particle hydrodynamics (SPH)—could provide even more detailed insights into how moisture and proteins interact during processing.

Zhang and Zhu also highlight the potential of machine learning (ML) to enhance FEA applications in PBMA development. By integrating ML algorithms into FEA simulations, researchers could analyze large datasets from experimental trials and computational models to refine predictive accuracy and optimize processing parameters in real-time. This could significantly enhance efficiency and scalability in PBMA production, making plant-based meat more accessible and appealing to a wider audience.