Influence of Heating on the Physico-Biochemical Attributes of Milk
Bibliographic record
Abstract
Background: Milk, the fluid secreted by the female of all mammalian species, fulfills the complete nutritional and energy requirements. Milk is a single balanced diet enriched in physiologically important proteins and peptides, enzymes, enzyme inhibitors, immunoglobulins, growth factors, hormones, and antibacterial agents. Milk can be converted to different dairy items that occupy an important place in confectioneries and beverages and thus are subjected to various processing conditions. Objective: This review aims to discuss how the processing conditions affect the physicobiochemical and nutritional attributes of milk protein and influence its functionality with a major focus on heating or thermal treatment. Methods: Detailed literature surveys with keywords ‘thermal effect of milk proteins’, ‘dairy chemistry’, ‘Maillard reactions have been done in food science, food chemistry, dairy science, functional foods journals, PubMed, and Scopus for gathering information on thermal effects on milk proteins. Out of 25 shortlisted review and research articles, 20 most relevant ones were cited and enlisted as references. Results: Due to thermal treatment during dairy processing, the chemical characteristics of milk proteins are altered because of chemical changes like glycation, aggregation and denaturation. Chemical modifications influence the functionality, digestibility, and nutritional quality of milk proteins. Conclusion: Novel milk processing technologies viz. ohmic and microwave heating, pulsed electric field, high hydrostatic pressure, microfiltration and ultrasound find applications in dairy processing. Such non-thermal technologies do not involve heat to kill the microbes; thus reducing the detrimental effect of conventional heat treatments on milk quality.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".