Perceived Creaminess and Viscosity of Aggregated Particles of Casein Micelles and κ‐Carrageenan
Bibliographic record
Abstract
Abstract: Creaminess, in terms of sensory science, is a very complex and multifaceted term. It is a descriptor often reserved for fat‐containing dairy emulsions, however, has also been used to describe nondairy food emulsions. In the food industry, it is a great challenge to decrease fat content while maintaining the original quality and sensory characteristics of a food product. An aggregated particle consisting of casein micelles and κ‐carrageenan has the potential to enhance the perceived creaminess of a low‐fat food product, due to its colloidal size. In this study, these aggregates were incorporated into nonfat dairy beverages and subjected to sensory studies. In the 1st sensory study, the aggregates, either as a powdered ingredient or a fresh ingredient, were added to thickened dairy beverages and compared to similar beverages containing skim milk powder and either no fat or 2% dairy fat. The panelists found the aggregate‐containing beverages to be creamier and more viscous in comparison to the control beverages. In the 2nd sensory study, fresh and powdered aggregates, at 2 concentrations, were added to a sweetened nonfat dairy beverage and compared to a similar beverage containing 2% dairy fat. The results of this panel showed that aggregates, especially at increased concentrations, were perceived as more creamy than the fat‐containing beverage. Panelists described the creaminess of the aggregates as more thick and viscous while the dairy fat was described more in terms of mouth‐coating. Thus, we have developed a nonfat milk ingredient that can contribute creaminess to a food product. Practical Application: This study shows potential applications of aggregates of casein micelles and κ‐carrageenan as a fat‐mimetic or creaminess‐enhancing ingredient. These particles may be produced as either fresh aggregates directly formed in a dairy product or as powdered aggregates added to dairy or nondairy products.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".