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April 16, 2025 - GTU Office of Press and Public Relations
The project titled “Development of CNC-Based Hybrid Stabilized Pickering HIPE Systems for Functional Fat Substitution and Modulation of Lipid Digestion and Their Applicability in Bakery Products”, led by Asst. Prof. Emrah Kırtıl, a faculty member at the Department of Chemical Engineering, Faculty of Engineering at Gebze Technical University (GTU), is awarded support under TÜBİTAK’s 3501 Career Development Program.
The project, coordinated by Kırtıl, aims to control lipid digestion and develop functional fat substitutes in bakery products through the use of CNC-based hybrid Pickering HIPE systems. Carried out in collaboration with Pakmaya A.Ş., this interdisciplinary initiative integrates surface chemistry, colloid science, and artificial intelligence-based quality analyses, opening the door to innovative applications in food systems.
The project targets the design of innovative Pickering-type High Internal Phase Emulsions (HIPEs) to enable functional fat replacement and modulation of lipid digestion. The main strategy is to transform cellulose nanocrystals (CNC) into hybrid or multilayer stabilizers by combining them with natural components such as phosphatidylethanolamine (PE) and lauric arginate (LAE). In doing so, a stable barrier is formed at the oil–water interface that restricts the access of pancreatic lipase to the fat droplets, thus partially limiting lipid digestion. The project aims to comprehensively examine the physicochemical properties, stability, rheological performance, and oxidative resistance of CNC-PE and CNC-LAE-CNC stabilizers.
By using HIPEs with high oil content as full or partial fat substitutes in bakery products, the project seeks to reduce the calorie content of these products while preserving their sensory and structural qualities. Machine learning-based approaches form a key component of the project. In this context, large-scale datasets obtained from photographs, porosity measurements, and textural tests of cake samples—routinely analyzed in the lab or collected from production lines—will be used to train a machine learning model. This will enable objective, rapid, and reproducible quality assessments regarding the performance of various cake formulations under different production conditions, offering valuable insights for both product development and food standardization.
Designed within an interdisciplinary framework, the project brings together surface/interfacial chemistry, colloid science, and food technology with AI-driven data analysis. The results are expected to contribute to the development of low-energy but sensorially rich food products in the fight against obesity. Additionally, the project is anticipated to play a significant role in reducing food waste and increasing production efficiency by offering an innovative alternative to existing quality control methods in the food industry.
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