Chemical, proteolysis and sensory attributes, and probiotic microorganisms viability of Iranian ultrafiltered-Feta cheese as a function of inulin concentration and storage temperature
Bibliographic record
Abstract
The aim of the present work was to study the effects of inulin addition (0, 1.5 and 3% w/w) and the storage temperature (8 and 12 °C) on the viability of probiotic organisms (Lactobacillus acidophilus la-5 and Bifidobacterium lactis BB-12), chemical composition, proteolysis and sensory characteristics of ultrafiltered (UF)-Feta cheeses over 60 days storage time. Storage time was the most effective factor on dependent variables. Population of both strains remained above 106 cfu/g at the end of storage time and their viability was neither significantly affected by the inulin concentration, nor by the storage temperatures. No significant (P>0.05) differences were observed in sensory scores, syneresis, water soluble nitrogen, trichloroacetic acid soluble nitrogen and other physicochemical properties (except for dry matter) among different cheese samples at different temperatures. The levels of flavour, odour and overall acceptability were significantly increased as storage period progressed. The results suggest UF-Feta cheese is a suitable carrier for L. acidophilus la-5 and B. lactis BB-12 with increased acceptability during storage.
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) | 0.000 | 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".