On‐Line Sterilization of Cheese Whey Using Ultraviolet Radiation
Bibliographic record
Abstract
The effectiveness of ultraviolet radiation for on-line sterilization of cheese whey was investigated. The effects of flow rate and residence time on the performance of three UV reactors having different gap sizes (18, 13, and 6 mm) were studied. Six flow rates and six residence times were tested with the three UV reactors. The cheese whey used in this study had a very high turbidity (4317 NTU), very poor transmittance in the UV radiation germicidal range ( approximately 0%), and high percentage of large solid particles ( approximately 20% > 100 microm). Although the cheese whey physical characteristics showed low probability of sterilization using UV radiation, the study showed that UV radiation can be used on-line to sterilize cheese whey if the proper reactor gap size and the appropriate residence time are used. There were combined effects of the flow rate and gap size. The cell removal efficiency increased with increases in residence time and decreases in the UV reactor gap size. Removal efficiency of 100% was not achieved in this study with the first UV reactor (18-mm gap size), whereas 100% removal efficiency was achieved with the second (13-mm gap size) and third (6-mm gap size) UV reactors at residence times of 2.0 and 0.5 h, respectively. The microbial decay rates achieved in this study were 4.94, 7.62, and 20.9 h(-)(1) using the first, second, and third UV reactor, respectively. Residence times of 3.3, 2.1, and 0.8 h would be required to completely destruct a microbial population of 5.95 x 10(6) cells/mL using the first, second, and third UV reactors, respectively. Although cheese whey sterilization using UV radiation seems to be a good alternative to pasteurization, increases in cheese whey temperature resulted in lamp fouling. If online sterilization is to be used, the fouling problem should be investigated and a maintenance scheme for the UV reactor should be developed.
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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) | 0.001 | 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".