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The texture of meat is one of the most important features to mimic when developing meat analogs. Both protein source and processing method impact the texture of the final product. We can distinguish three types of mechanical tests to quantify the textural differences between meat and meat analogs: puncture-type, rheological torsion tests, and classical mechanical tests of tension, compression, and bending. Here we compile the shear force and stiffness values of whole and comminuted meats and meat analogs from the two most popular tests for meat, the Warner-Bratzler shear test and the double-compression texture profile analysis. Our results suggest that, with the right fine-tuning, today’s meat analogs are well capable of mimicking the mechanics of real meat. While Warner-Bratzler shear tests and texture profile analysis provide valuable information about the tenderness and sensory perception of meat, both tests suffer from a lack of standardization, which limits cross-study comparisons. Here we provide guidelines to standardize meat testing and report the meat stiffness as the single most informative mechanical parameter. Collecting big standardized data and sharing them with the community at large could empower researchers to harness the power of generative artificial intelligence to inform the systematic development of meat analogs with desired mechanical properties and functions, taste and sensory perception.
The texture of meat is one of the most important features to mimic when developing meat analogs. Both protein source and processing method impact the texture of the final product. We can distinguish three types of mechanical tests to quantify the textural differences between meat and meat analogs: puncture-type, rheological torsion tests, and classical mechanical tests of tension, compression, and bending. Here we compile the shear force and stiffness values of whole and comminuted meats and meat analogs from the two most popular tests for meat, the Warner-Bratzler shear test and the double-compression texture profile analysis. Our results suggest that, with the right fine-tuning, today’s meat analogs are well capable of mimicking the mechanics of real meat. While Warner-Bratzler shear tests and texture profile analysis provide valuable information about the tenderness and sensory perception of meat, both tests suffer from a lack of standardization, which limits cross-study comparisons. Here we provide guidelines to standardize meat testing and report the meat stiffness as the single most informative mechanical parameter. Collecting big standardized data and sharing them with the community at large could empower researchers to harness the power of generative artificial intelligence to inform the systematic development of meat analogs with desired mechanical properties and functions, taste and sensory perception.
The texture of meat is one of the most important features to mimic when developing meat analogs. Both protein source and processing method impact the texture of the final product. We can distinguish three types of mechanical tests to quantify the textural differences between meat and meat analogs: puncture type, rheological torsion tests, and classical mechanical tests of tension, compression, and bending. Here, we compile the shear force and stiffness values of whole and comminuted meats and meat analogs from the two most popular tests for meat, the Warner–Bratzler shear test and the double-compression texture profile analysis. Our results suggest that, with the right fine-tuning, today’s meat analogs are well capable of mimicking the mechanics of real meat. While Warner–Bratzler shear tests and texture profile analysis provide valuable information about the tenderness and sensory perception of meat, both tests suffer from a lack of standardization, which limits cross-study comparisons. Here, we provide guidelines to standardize meat testing and report meat stiffness as the single most informative mechanical parameter. Collecting big standardized data and sharing them with the community at large could empower researchers to harness the power of generative artificial intelligence to inform the systematic development of meat analogs with desired mechanical properties and functions, taste, and sensory perception.
Calcium alginate hydrogel is one of the most widely used materials for drug-carrier beads used in drug-delivery systems. In this study, we developed a new method to improve the encapsulation efficiency of ingredients, such as medicines, in calcium alginate hydrogel beads. In the gold standard method, the hydrogel beads are prepared in the liquid phase. In contrast, in the new method, to enhance the encapsulation efficiency, the hydrogel beads are prepared in the gas phase using a water-repellent surface. In brief, a droplet of sodium alginate aqueous solution is rolled on a water-repellent surface with CaCl2 powder, a cross-linking agent. This process leads to the direct attachment of CaCl2 powder to the droplet, resulting in the formation of spherical hydrogel beads with high mechanical strength and higher encapsulation efficiency than beads prepared by previous methods. The hydrogel beads exhibit similar permeability for glucose, a model for low-molecular-weight medicines, to those prepared by previous methods. These results show that the new method is promising for the preparation of calcium alginate hydrogel beads for drug-delivery systems.
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