Research Projects

High moisture meat analogue (HMMA) is a textured protein product mimicking animal meat, prepared using plant-based proteins (pea, soy or wheat). Most studies have shown an effect of the formulations and twin-screw extruder (TSE) process parameters on HMMA properties. However, the effects of TSE design, Multiphysics simulations for material behaviour in TSE, predictive modelling for improved HMMA properties and dynamic adjustment of TSE process parameters are not well documented. HMMA manufacturing is based on formulation, heating and shearing processes in TSE and cooling for fibrous structure formation in the cooling die. Heating and shearing of material depend on screw element and barrel design, heating profile, and formulation composition. The screw elements (conveying, mixing, kneading, and shear) impact need to be assessed using Multiphysics simulation before fabricating the TSE to save money and time. An e-nose system and digital camera can be installed to measure flavor and color. Moreover, artificial intelligence (AI) can be used to predict the HMMA properties (texture, structure, color, and flavor). Therefore, this research proposal envisaged the design and fabrication of AI-powered Indigenous lab scale smart TSE, which will have features to predict a material's flow pattern and behaviour, properties of HMMA, and dynamic process adjustment. Cooking and shelf-life evaluation of HMMA will also be conducted to check the feasibility of TSE and developed process. The hypotheses to be tested include (a) Multiphysics simulation can provide the material behaviour under thermal and shear environments, (b) Viscosity and flow pattern estimation in cooling die can be correlated to fibrous structure formation and texture of HMMA, (c) AI integration can predict the fibrous structure using formulation and process parameters (d) Predictive models can dynamically adjust the TSE operating process parameters, (e) Digital camera and e-nose system can provide color and flavor profiles to improve the predictive models and formulations. The experimental plan will include designing screw elements, barrel, feeder, cooling die, sensor and control assembly; simulation studies for analysing failures and material behaviour using Multiphysics; finalizing the design and fabrication of TSE; optimization of process parameters to generate the scientific data for AI modelling including dynamic control and cooking and storage studies to evaluate shelf life of HMMA. The significance of this project will be the unique smart TSE used to predict HMMA properties using formulation and dynamically adjusting process parameters. It will provide information on screw elements, temperature profiles, process parameters, etc., for achieving desired HMMA properties. It will not only save time, money and materials in standardizing commercial HMMA and other extruded products but also upgrade the traditional TSE with AI and promote the use of digital technology in sustainable innovation.