Microbiological Stability of Rice Tart Stored at Ambient Temperature after Baking
Bibliographic record
Abstract
Very popular in Belgium, rice tart is a hot pastry sold in bakeries. It is then consumed at home, for dessert or snack. This study is conducted to investigate the microbiological stability of this foodstuff, from the end of baking to end user by consumers. In this purpose, 108 rice tart samples were collected from each of seven bakeries in five Belgium provinces. Physico-chimical analysis in addition to microbiological analysis were carried out in accordance with the European Regulation EC 2073/2005 and with references methods, to enumerate the total microorganisms count, Staphylococcus aureus, Bacillus cereus as well as Enterobacteria, susceptible likely to contaminate the tarts during the production or after baking. Even when the results meet the microbiological safety standards immediately after baking in all bakeries implicated, a significant (p <0,05) increase of mesophilic aerobic bacteria (ranging from <1 to 7 log cfu/g) and B. cereus (> 3,7 log cfu/g) was observed during the storage at nonrefrigerated temperature (28 to 30°C). A post-baking recontamination and other parameters like an insufficient baking time or a non uniform distribution of the oven heat could explain the observed bacteria growth. The present study shows that most rice tarts investigated are microbiologically safe. However, the possible increase of bacteria load in this foodstuff attributable to the favorable aw, pH and temperature conditions, highlight the importance of applying good hygienic practices and compliance with storage conditions after baking to ensure consumer safety.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".