The article titled “Ultrasound Imaging of Artificial Tongues During Compression and Shearing of Food Gels on a Biomimetic Testing Bench” by Glumac et al. [1] introduces a novel method to study tongue–food interactions using ultrasound (US) imaging. The study primarily aims to improve understanding of mechanical processes during oral food processing. In particular, it focuses on how deformation at the tongue–food interface influences texture perception.

To simulate oral conditions, the researchers created four artificial tongue models from polyvinyl alcohol (PVA) cryogels. These phantoms varied in surface roughness and stiffness to mimic different human tongue properties. They also prepared model food gels from agar, each with different concentrations to represent various textures. In the experiments, the gels were placed between the tongue phantom and a simulated hard palate on a biomimetic testing bench. A multi-axis force sensor measured the mechanical loads, while an ultrasound transducer array captured real-time images of the tongue surface during both compression and shear tests.

Using ultrasound contour tracking, the team precisely monitored deformation along the contact surface. During shear tests, particle tracking methods, including Particle Image Velocimetry (PIV), visualized horizontal velocity gradients within the tongue model. These results showed that deformation was unevenly distributed across the contact region. Consequently, the study revealed how tactile stimuli arise during oral food manipulation.

A key finding was the ability to distinguish between static and dynamic friction phases during shearing. This distinction significantly affects how textures are perceived in the mouth. Moreover, the technique demonstrated how tongue stiffness influenced force transmission and deformation patterns. These results underscore the crucial role of oral biomechanics in sensory evaluation.

Importantly, the study combines high-resolution US imaging with a biomimetic mechanical platform. This approach offers spatial and temporal resolution of oral interactions that were previously inaccessible. Therefore, the findings have broad implications for sensory science, food texture engineering, and oral drug delivery.

MCC Spheres enhancing the reproducibility and standardization

In the context of ultrasound imaging of artificial tongues, CELLETS® 90 (60 – 100 µm) provide a promising way to improve reproducibility and standardization. These highly uniform microcrystalline cellulose spheres have consistent mechanical properties and geometric features. Therefore, they are ideal as model substrates in oral-processing research. Their controlled size and mechanical resilience can benchmark system sensitivity. Additionally, they can serve as reference particles within gel matrices to help interpret deformation dynamics more clearly. Moreover, using CELLETS® supports pharmaceutical studies by simulating oral disintegration of solid dosage forms. By integrating them into the US-based methodology, researchers can expand the translational relevance of this platform for both food and pharmaceutical applications.

Scientific Significance

This work pioneers the use of biomimetic tongue models combined with advanced ultrasound imaging. It allows researchers to quantitatively analyze oral texture mechanics. Importantly, the method resolves friction phases, spatial deformation patterns, and velocity gradients during tongue–food interactions. As a result, it enhances our understanding of mechanosensory stimulation pathways. These insights are invaluable for designing food products, especially for populations with altered oral processing, such as the elderly or people with dysphagia. They also guide the development of orally disintegrating drug formulations. Furthermore, integrating CELLETS® strengthens the methodology’s robustness. This addition bridges food science and pharmaceutical applications while encouraging cross-disciplinary collaboration.

References

[1] M. Glumac, J.-L. Gennisson, V. Mathieu, Journal of Texture Studies, 2025; 56:e70030; doi:10.1111/jtxs.70030

Disclaimer: this text was partly composed with ChatGPT-4.