Comparison of HTST and UV-C treatments on the quality attributes of whole milk, skim milk, and whey protein concentrate
Bibliographic record
Abstract
Ultraviolet (UV-C) treatment is a proposed alternative to conventional high temperature-short time (HTST) processes for milk pasteurization. However, little information is available on its impact on the chemical composition of milk, as well as a comparison with HTST treatment. Consequently, the aim of this study was to compare the effects of HTST (72 °C, 16 s) and UV-C (coil diameters of 5- and 7-mm, 1 or 2 passes) with turbulent flow on the proximate composition, protein profiles and surface properties, as well as off-flavor development in whole milk (WM), skim milk (SM), skim milk generated from whole milk (SM WM ), and whey protein concentrate (WPC). Our results on proximate composition showed that only the UV-C treatment with 2 passes using a 7-mm coil resulted in lower lipid content in SM WM compared to HTST. Protein profiles, free thiol content, surface hydrophobicity, and aromatic amino acid levels remained similar between the control, HTST and UV-C treated samples. However, higher alcohol levels were detected in HTST-treated samples, while UV-C-treated dairy fluids had increased levels of free fatty acids and ketones. In addition, hexanal concentration increased only after HTST treatment of WM. This study highlights the potential of UV-C treatment in the dairy industry, demonstrating its ability to preserve nutritional quality while also offering valuable insights into lipid oxidation and the formation of light-induced off-flavor compounds.
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 | 0.000 | 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) | 0.001 | 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".