Examining the safety of duck breast prepared the sous vide method
Bibliographic record
Abstract

 Objectives: There is an increasing desire in the culinary industry to use sous vide to prepare meals at low internal temperatures to enhance flavour, texture, and quality. The sous vide method uses specific time and temperature combinations to allow for sufficient microbial destruction. The BCCDC’s Guidelines for Restaurant Sous Vide Cooking Safety in British Columbia suggests time and temperature combinations to help ensure that the required log10 reductions of pathogens are achieved. Concerns for public safety arise when chefs deviate from the guidelines, and therefore may not achieve the appropriate log10 reductions. This study looked at a commonly used sous vide duck breast recipe and determine whether appropriate the appropriate log10 reductions were met. It also examine the efficacy of the sear step and resting period in achieving the log10 reductions. Methods: After calibration, two batches of 15 duck breasts were prepared using the sous vide method for 80 minutes at 58ºC, the breasts were then seared on a 200°C frying pan for 2 minutes each side, and then subjected to a 4 minute rest period at room temperature. The internal temperature of the breasts was continuously measured using SmartButton thermometers. This data was entered into the AMI Process Lethality Determination Spreadsheet to calculate the log10 reductions. The log10 reductions were analyzed using a one-sample t-test to assess whether the recipe achieved the required 7.0 log10 reductions. Results: The results showed 14% of the 29 duck breasts achieved a 7.0 log10 reduction after the sous vide step of 80 minutes at 58 ºC. The null hypothesis (Ho: measured log10 reductions of duck breasts = 7.0 log10 reductions) was rejected with 100% power and a p-value of 0.00. The mean was 5.13, therefore it seems as though the log10 reductions were significantly lower than 7.0 log10 reductions. After the sear and the resting period, 52% of 27 duck breasts achieved a 7.0 log10 reduction. Statistical analyses showed that the null hypothesis could not be rejected. The p-value was 0.97 and the power was 0.413. Disregarding cumulative effects, the median log10 reductions achieved only by the sear step was 0.43, and the median log10 reductions achieved solely by the resting period was 0.35. Conclusion: Due to lack of normality one cannot confidently say this recipe will achieve 7.0 log10 reductions. However, due to the high log10 reductions achieved, it seems plausible for another recipe to provide adequate log10 reductions while maintaining acceptable quality. The sous vide step should be used for the majority of the log10 reductions. Due to a wide variability in the results, the sear and resting period should only be used for small increases in log10 reductions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".