Fish gelatin is increasingly recognized as an alternative source of gelatin, but its use has been limited due to weak gelling properties. To address these issues, the effect of furcellaran, a gelling agent, was examined at various levels (25-100% FG substitution) on the structural and physicochemical properties of FG gels. As the amount of FUR increased to 25%, the FG/FUR gel showed improved hardness and gel strength (P<0.05). Additionally, increasing FUR levels led to higher gelling and melting points, showing a dose-dependent relationship. Microstructural analysis revealed that adding FUR created a denser gel network with smaller gaps. SAXS scattering intensities also increased as FUR concentration rose. Overall, adding FUR improved the gelling properties of FG without negatively affecting springiness and syneresis, enhancing gel strength and gelling temperature.
The limitations of fish gelatin (FG) in terms of weak gelling properties, low gel strength, and inability to set at room temperature. By investigating the impact of furcellaran (FUR), a gelling agent, the study offers a solution to enhance FG’s functional properties, making it a more viable alternative to traditional animal-based gelatin. The findings suggest that FUR improves the texture, gel strength, and thermal stability of FG, which is crucial for a wide range of applications in the food and pharmaceutical industries. This could lead to the development of more sustainable, plant-based gelling agents, offering ethical and environmental benefits. Additionally, the study enhances the understanding of the molecular interactions between FG and FUR, providing a foundation for further innovations in gelation technology and the creation of improved, multifunctional gel-based products.
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This innovation reduces costs and enhances queue management efficiency in restaurants, ensuring an organized system, minimizing wait times, and improving customer handling.
คณะวิศวกรรมศาสตร์
This project focuses on the development of an automatic license plate recognition system that supports both standard and special license plates in Thailand. By utilizing Machine Learning technology, the system enhances the efficiency of license plate reading. It can process data from both images and videos. Users can register and subscribe to the service, allowing them to send data for processing through RESTful API, WebSocket, and registered IP cameras.
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