The effect of post-packaging pasteurization on physicochemical and microbial properties of beef ham
Bibliographic record
Abstract
In this study, we aimed to investigate the impact of three different post-packaging pasteurization temperatures (55, 65, and 75°C) on the physicochemical (pH, drip loss, texture profile, and color), microbial (lactic acid bacteria, mesophilic and psychrotrophic bacteria, as well as mold and yeast), and sensory (odor, taste, texture, color, slime, exudates, swelling, and overall acceptability) characteristics of vacuum-packed beef ham during 30 days of storage at two different temperatures (5 and 12°C). Lactic acid bacteria and total mesophilic and psychrotrophic counts were reduced to zero by post-packaging pasteurization at 65 and 75°C. Higher post-packaging pasteurization temperatures resulted in a significant increase in drip loss in the treated samples at 65 and 75°C, as well as a small rise in pH in all the samples. Furthermore, higher post-packaging pasteurization temperatures decreased lightness, yellowness, and h° values while increasing redness and ΔE. During post-packaging pasteurization, Chroma remained constant. The textural profile analysis revealed that post-packaging pasteurization and storage had a significant impact on the texture of beef ham. The sensory analysis showed no changes after post-packaging pasteurization in the samples, and the sensory parameters remained stable during their storage at 65 and 75 °C. Finally, our investigation showed that 65°C is an optimal post-packaging pasteurization temperature for increasing the shelf-life of beef ham under refrigeration.
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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 | 0.000 | 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 | 0.001 |
| 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".