Effect of Ozonation on Microbiological Quality, Nutritional Value, and Shelf Life of Fresh Cow’s Milk
Bibliographic record
Abstract
The purpose of this study is to evaluate how effectively ozonation influences the quality of fresh cow's milk, particularly in terms of reducing microbiological contamination levels in accordance with national guidelines.This research was conducted experimentally using the ozonation method and included laboratory testing with duplicate repetitions.A total of 70 fresh cow's milk samples, each representing one dairy cow, were collected from three cities in West Java, Indonesia.The sampling method used was convenience sampling, which involves selecting samples from the target population based on ease of access.West Java is one of the largest milk-producing provinces in Indonesia.The research showed that with ozonation treatment, the inactivation rate of microorganisms reached 94.50%.E. coli and Staphylococcus aureus were the two pathogenic bacteria that were inactivated at rates of 97.19% and 95.97%, respectively.The decrease in standard deviation from 2.4E+08 to 1.7E+08 suggests the treatment significantly improved the uniformity of microbial content in the milk samples.The initial temperature and exposure time were 1℃ for 10 minutes.The ozonation process had no effect on the milk's color, taste, or consistency, except for a slight odor alteration noted post-treatment.In addition, ozonation increased protein and fat levels proportionally.Ozonized milk products had a longer shelf life, exceeding 19 hours at room temperature.This study showed that ozonation effectively reduces microbiological contamination in fresh cow's milk and fulfills national standards.These findings are valuable for the dairy industry in improving product quality and 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 | 0.000 |
| 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 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".