Effect of peracetic acid–ultraviolet combination treatment on microbial and endotoxin levels in a pharmaceutical water system
Bibliographic record
Abstract
Abstract In the pharmaceutical industry, the microbiological quality of water is vital. This research investigated how combining peracetic acid (0.1% V/V) and UV light (>150 mJ/cm2) as wide-spectrum disinfectants affect microbial and endotoxin levels in a pharmaceutical water system. Water samples were taken aseptically from 12 points across the system. The pour plate technique and membrane filtration were used for microbial counts. The presence of endotoxin in distilled water samples was investigated by the Limulus amebocyte lysate (LAL) test gel-clot method. After peracetic acid–UV combination treatment, microbial counts of samples significantly decreased (P < 0.05) compared with UV treatment alone, and they were lower than the action limits specified by the European Pharmacopeia (100 CFU/ml for purified water and 10 CFU/100 ml for water for injection). In addition, water samples were mainly LAL-negative (10 negative weekly reports out of 12 total reports). It is concluded that disinfection of all stages of the water system with peracetic acid–UV combination remarkably improved the microbial quality of the water system. Therefore, rotation between more than one disinfectant policy and periodic disinfection of the water system by peracetic acid–UV combination is recommended to minimize contamination of the water system and pharmaceutical products as well as bacterial infections in product consumers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.000 | 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 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".