Role of Clay Substrate Molecular Interactions in Some Dairy Technology Applications
Bibliographic record
Abstract
The use of clay materials in dairy technology requires a multidisciplinary approach that allows correlating clay efficiency in the targeted application to its interactions with milk components. For profitability reasons, natural clays and clay minerals can be used as low-cost and harmless food-compatible materials for improving key processes such as fermentation and coagulation. Under chemical stability conditions, clay materials can act as adsorbents, since anionic clay minerals such as hydrotalcite already showed effectiveness in the continuous removal of lactic acid via in situ anion exchange during fermentation and ex situ regeneration by ozone. Raw and modified bentonites and smectites have also been used as adsorbents in aflatoxin retention and as acidic species in milk acidification and coagulation. Aflatoxins and organophilic milk components, particularly non-charged caseins around their isoelectric points, are expected to display high affinity towards high silica regions on the clay surface. Here, clay interactions with milk components are key factors that govern adsorption and surface physicochemical processes. Knowledge about these interactions and changes in clay behavior according to the pH and chemical composition of the liquid media and, more importantly, clay chemical stability is an essential requirement for understanding process improvements in dairy technology, both upstream and downstream of milk production. The present paper provides a comprehensive review with deep analysis and synthesis of the main findings of studies in this area. This may be greatly useful for mastering milk processing efficiency and envisaging new prospects in dairy technology.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 |
| 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".