Justification of replacement of pasteurization equipment in dairy
Bibliographic record
Abstract
Purpose: The purpose of this paper is to study the influence of new equipment on the effect of pasteurization in the department for pasteurization. The total bacterial count (TBC) in receiving raw cow milk and after termical treatment pasteurization has been measured. Design/methodology/approach: The measurements were performed with old Alfa Laval Pasteur from year 1982 and after replacement with new Fischer pasteur 43 year 2006. The line between receiving tank to pasteur in dairy remain the same. The daily sampling and analyses of finale receiving tanks were made on department for receiving milk for October 2005 (93 analysis) and December 2006 (96 analysis). Findings: The raw milk was in October 2005 on average for 10,6% worse quality (calculation of average value of TBC per ml), then in December 2006. The effect of pasteurization was after test, at working old equipment only 32,25%, at working new equipment was 100,00%. Research limitations/implications: The new equipment for pasteurization allows the production of safe milk products in accordance with hazard analyses of critical control points (HACCP). Practical implications: The effect of pasteurization with the new pasteur was perfect. At internal margin 95% this level overreach all 31/31 analysing samples. We get more stable production and reduce expense of steam, which serves for reaching of appropriate temperature of pasteurization. Originality/value: to the use of new equipment allowing 20 second maintaining time of pasteurization the pasteurization temperature has been reduced from 78°C to 76°C and, thus, the profitability of the pasteurization process has been improved.
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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 | 0.004 | 0.016 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".