Monitoring colostrum harvesting and distribution equipment cleanliness with adenosine triphosphate luminometry before and after recommendations to improve hygiene practices in French dairy farms
Bibliographic record
Abstract
This observational study evaluated the effects of practical hygiene recommendations on cleanliness and bacterial contamination of equipment used to harvest and feed colostrum on 8 French dairy farms. The study was conducted in 2 phases: before (phase 1) and after (phase 2) the implementation of farm-specific recommendations. During each phase, equipment cleanliness (e.g., robot-compatible buckets, milking buckets, transfer buckets, bottles, nipples, nipple buckets, drenchers, and esophageal tube feeders [ET]) was assessed using ATP luminometry (through direct surface [ATP-S] and rinsing liquid [ATP-L] swabbing, expressed in relative light units [RLU]), visual scoring, and culture-based bacteriological analysis. Fresh colostrum samples were also collected and analyzed during each phase. A self-reported questionnaire administered during phase 1 identified critical control points for colostrum contamination, which informed the formulation of practical hygiene recommendations. Contamination thresholds were defined as ≥100,000 cfu/mL for total bacterial count (TBC) and ≥10,000 cfu/mL for total coliform count (TCC). Hygiene practices related to the operator, cow teats, and equipment varied considerably among farms, as did adherence to the recommended practices. Overall, lower RLU values were recorded on equipment surfaces during phase 2 compared with phase 1 (ATP-L: −1.51 log [±0.24], P < 0.001; ATP-S: −1.15 log [±0.24], P < 0.001). These reductions were associated with good operator hygiene practices and the use of brand-new equipment. Lower levels of contamination of colostrum samples were found in phase 2 (TBC: −0.92 log [±0.19], P < 0.001; TCC: −1.14 log [±0.30], P < 0.001), associated with good operator hygiene practices. Significant correlations (Spearman's rho, r s ) were observed between visual cleanliness scores and RLU values from both swabbing techniques, as well as between ATP-L RLU values and bacterial counts (TBC: r s = 0.744, 95% CI: 0.556–0.859, P < 0.0001; TCC: r s = 0.475, 95% CI: 0.183–0.690, P < 0.002). Based on ATP-L swab results, optimal luminometry cutpoints maximizing (sensitivity + specificity) were identified at ≥6,950 RLU (sensitivity: 79.17%; specificity: 100%) for TBC, and ≥11,912 RLU (sensitivity: 88.89%; specificity: 70.97%) for TCC. This study demonstrates that targeted hygiene improvements can significantly reduce both equipment surface and colostrum bacterial contamination and support the use of ATP luminometry as a practical tool for hygiene monitoring on dairy farms.
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 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 | 0.002 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".