Mathematical modeling for thermally treated vacuum-packaged foods: A review on sous vide processing
Bibliographic record
Abstract
Consumer dietary awareness drives a need for minimally processed foods with quality sensory and nutritional attributes and extended shelf life. Sous vide cooking techniques are a viable technology for meeting these consumer demands. Sous vide is the process of cooking vacuum-sealed foods in plastic pouches at low temperatures, generally 55–60 °C, for an extended period under strictly controlled conditions. Despite the high-quality, nutritional, and sensory benefits of sous vide cooking, the use of temperatures significantly lower than typical cooking raises microbiological/safety issues for customers. This review aims to highlight the numerous mathematical approaches used in modeling the quality and microbial safety of sous vide processed foods, as well as the effects of sous vide processing on texture, physiochemical, and nutritional quality. Sous vide processing has been mathematically modeled in a variety of ways, ranging from totally kinetic or empirical to completely physics-based approaches. The emerging picture from this review suggests that mathematical modeling of SV processing has been approached in several ways, from completely kinetic or empirical to completely physics-based approaches to improve sous vide processing technologies in the future. A more general modeling approach, real-time quality evaluation during sous vide processing, and hurdle technology in sous vide are all future areas to investigate in the application of mathematical modeling to improve sous vide processing. There is potential for future applications of mathematical modeling in SV processing to optimize the overall process conditions and the cooking methods for different types of foods and sizes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".