Development of a Culture-ripened Semi-hard Kishk-cheese Containing Bourghol or Semolina
Bibliographic record
Abstract
In order to develop a new Kishk-based ripened semi-hard cheese, two different wheat types, namely Bourghol and Semolina, were used. The process involved adding together cereal, goat strained yogurt, starter cultures and rennin enzyme. Physicochemical parameters and sensory attributes were assessed during 4 weeks of ripening at 10°C. The results showed an increase in titratable acidity, pH and solubility index in both types of cheese over ripening period reaching final levels of 1.95%, 3.75 and 85% respectively. Free amino acids were accumulating in Semolina cheese (17.75 mg Leu/g in final product), with a sharp increase after week 2 concurring with a marked decrease in residual lactose. A higher depletion rate of residual lactose was observed for Semolina cheese (49% in Semolina vs 40% in Bourghol) (p<0.05), reflecting better utilization of lactose by LAB. Both types of cheese showed limited oxidation rates (low peroxide and TBARS values) and balanced lipolysis, where FFA formed by the latter decreased after week 2. Texture analysis showed that Kishk-cheese made using Bourghol was always harder (p<0.05) than that of Semolina. Sensory analysis showed that cheese obtained from Bourghol tends to be more yellowish, harder with closed and crumbly body, together with a grainy texture in mouth and pronounced cereal flavor, whereas cheese obtained from Semolina tends to be sour, creamy with a sticky and cohesive texture, and rich lactic, buttery and cheesy flavors. Hedonic tests showed a significant difference (p<0.05) in preference between both types of cheese where Semolina was always preferred except for odor.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".