Inactivation Kinetics of Yeast Cells during Infrared Drying
Bibliographic record
Abstract
In this study, aqueous yeast suspensions were used to investigate the effects of drying (in an infrared heating environment) on the survival of yeast. The processes were modeled mathematically using a range of kinetics rate equations. The model parameters for each kinetic rate expression were obtained using a Matlab optimization procedure and the more suitable models describing the inactivation processes were identified. In order to provide the data for model validation, experiments were conducted using freshly prepared yeast suspensions. Additional experiments were also performed that further demonstrate the protective effects of sucrose and skim milk solids on yeast survival during drying. The simple Arrhenius equation was found to be a good model for predicting yeast survival during the control experiments, when heat was applied without dehydration occurring. Models incorporating both temperature and moisture content were more effective in describing yeast inactivation during drying. The model that gave the best predictions included the drying rate and the rate of temperature change as variables; the predicted activation energy for yeast deactivation was closest to that obtained from heating-only experiments in comparison with the other models examined. The results from this work are discussed and future prospects are suggested.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 source (direct Gemma or distilled Codex), 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".