Effect of the Heat Exchanger Type on Stirred Yogurt Properties Formulated at Different Total Solids and Fat Contents
Bibliographic record
Abstract
In this work stirred yogurts were produced using a technical scale pilot in which the cooling step was processed using either a tubular (THX; low shear) or a plate (PHX, high shear) heat exchanger. The aim was to determine how total solids (TS, adjusted using lactose) and fat contents (FC) impact stirred yogurt properties during storage, depending on the heat exchanger used. Using raw milk, cream, skim milk powder, and lactose, four yogurts were formulated at 16.5% TS and 4.2% proteins, with different FC (0.0, 1.3, 2.6, and 3.9%); one more control yogurt was formulated at 14% TS, 4.2% proteins, and 0.0% FC. Analyses of yogurts (firmness, viscosity, induced syneresis) were realized at days 1, 3, 7, 21, and 34 after production. The addition of lactose between the non-fat yogurt at 14 or 16.5% TS had little to no effect on stirred yogurt properties. Increasing FC reduced syneresis while increasing firmness and viscosity. The use of PHX reduced the syneresis compared to THX; however, it also tended to reduce the firmness of the yogurts with 3.9% FC.
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".