Kinetics of ultraviolet light inactivation of <i>Escherichia coli</i> O157:H7 in liquid foods
Bibliographic record
Abstract
Abstract The effects of pH, depth of food medium and ultraviolet (UV) light dose on the inactivation of Escherichia coli O157:H7 in UV‐opaque products such as apple juice (pH 3.5) and egg white (pH 9.1) were investigated. The applied UV dose ranged from 0 to 6.5 mW min cm−2, while the depths of the medium were 1, 3.5, 5 and 10 mm. The pH of the medium did not affect the inactivation of E coli O157:H7, since similar inactivation characteristics were obtained for both apple juice and liquid egg white. As expected, decreasing the depth of the medium increased the inactivation of E coli O157:H7. More than a 5‐log reduction was obtained when the fluid depth and UV dose were 1 mm and 6.5 mW min cm−2 respectively. However, less than a 1‐log reduction was obtained when the fluid depth was 10 mm. A two‐phase kinetic model was used to model the inactivation of E coli O157:H7. This model indicated that at higher fluid depths the inactivation rate was controlled by the second, slower inactivation phase, resulting in a lower overall inactivation. The visual appearance of the treated apple juice and egg white did not show any discolouration changes during 4 weeks of storage at ambient temperature (25 °C). Copyright © 2003 Society of Chemical Industry
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".