Composition and microbiological quality of raw milk refrigerated in community tanks
Bibliographic record
Abstract
Milk is rich in nutrients, but several factors in the production system interfere with its quality. The objective of this study was to analyze the composition and microbiological quality of raw milk stored in community tanks from the municipality of Aricanduva – Minas Gerais. Samples were collected from eight community tanks at different properties. The milk composition and temperature were analyzed using the digital milk analyzer. The total count of aerobic mesophilic microorganisms was performed at the Laboratory of Science and Technology of Products of Animal Origin, Department of Animal Science, Federal University of Vales do Jequitinhonha and Mucuri, Diamantina – Minas Gerais. The data obtained were compared to the values established by Normative Instruction (IN) n° 76, by means of Student's t-test and Wilcoxon's non-parametric test at the level of 5% of significance. The average levels of fat (3.55 g/100g), protein (3.00 g/100g), lactose (4.45 g/100g) and total solids (11.76 g/100g) were within the standards minimum required. The content of defatted solids (8.21 g/100g) was the limiting factor to the compliance with IN76, with an average value lower than that established (8.40 g/100g). The temperature of the milk was higher than the standard required by the legislation and the count of mesophilic microorganisms did not differ from the legislation. It is concluded that the composition of raw milk stored in community tanks in the municipality of Aricanduva – Minas Gerais, compared to IN 76, did not fully met the legislation in all evaluated parameters.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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".