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Experimental and analytical temperature distributions during oven-based convection heating
Kathryn L McCarthy1, Michael J McCarthy, Vineet Rakesh
1Dept. of Food Science and Technology, Univ. of California, Davis, Davis, CA 95616, USA. klmccarthy@ucdavis.edu
Journal of Food Science
|May 25, 2010
Summary
This study used magnetic resonance (MR) to validate a mathematical model for predicting food heating. Results show the model is accurate axially but needs coupled heat and mass transfer for radial accuracy, especially with moisture loss.
Area of Science:
- Food Engineering
- Transport Phenomena
- Mathematical Modeling
Background:
- Mathematical models and experimental evaluation are crucial for optimizing food processing.
- Magnetic resonance imaging (MRI) is a powerful tool for quantifying transport processes in various applications.
- Understanding heat and mass transfer is essential for predicting internal temperature distributions during food heating.
Purpose of the Study:
- To assess a mathematical model based on Fourier's second law using magnetic resonance (MR) for food heating.
- To compare analytical predictions of internal temperature distributions with experimental MR measurements.
- To determine when coupled heat and mass transport models are necessary during convective heating.
Main Methods:
- Utilized magnetic resonance (MR) to measure internal temperatures in cylindrical food samples (gel, potato).
- Heated samples in a convection oven and compared experimental MR temperatures to analytical model predictions.
- Analyzed temperature distributions in both axial and radial directions.
Main Results:
- Axial temperature distributions showed favorable agreement for the food gel and acceptable agreement for potato samples.
- Radial temperature gradients in the gel followed predicted trends but with a shallower gradient.
- Radial temperature predictions for potato samples deviated significantly due to moisture loss, indicating limitations of the basic model.
Conclusions:
- The analytical model provides a reasonable prediction of axial temperature profiles during convective heating.
- Moisture loss significantly impacts radial temperature predictions, necessitating more complex models.
- Coupled heat and mass transfer models are required for accurate radial temperature resolution when significant moisture loss occurs.
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