Retort process modelling for Indian traditional foods

Area of Science:

• Food Science and Technology
• Chemical Engineering
• Process Modeling

Background:

• Indian traditional foods are complex, heterogeneous recipes often packed in multilayer pouches.
• Optimizing the retorting process (a thermal sterilization method) is crucial for ensuring food safety and quality.
• Accurate temperature prediction is essential for effective retorting, especially for multi-component food systems.

Purpose of the Study:

• To develop a unified lumped-parameter model for estimating the cold point temperature during the retorting of multilayer pouch-packed Indian foods.
• To identify key independent variables influencing the retorting process and temperature profiles.
• To validate the model's efficacy using diverse traditional Indian vegetarian products.

Main Methods:

• Utilized a lumped-parameter approach to model the retorting process.
• Developed a predictive model based on initial process conditions, retort temperature, and % solid content.
• Validated the model against experimental data for four Indian vegetarian products: Pulav, Sambar, Gajar Halawa, and Upama.

Main Results:

• The developed model accurately predicted temperature profiles, with a maximum error of ±10% compared to experimental values.
• Initial process conditions, retort temperature, and % solid content were identified as significant influencing variables.
• The model demonstrated good predictive capability for complex, multi-component food systems.

Conclusions:

• The proposed unified model is a valuable tool for optimizing the retorting process of various Indian traditional vegetarian foods.
• The model can significantly reduce the number of experimental trials required for process development and validation.
• This approach contributes to efficient and safe thermal processing of ethnic convenience foods.