Dietary Fiber and beta-glucan Contents of Oat Tarhana: A Turkish Fermented Cereal Food
Bibliographic record
Abstract
Tarhana is a traditional Turkish fermented cereal based food, made from wheat flour, bakers’ yeast, yogurt and different vegetables. After fermentation, the tarhana dough is dried and milled. Tarhana powder is most often used in the form of soup. In this study, oat flour (OF) and steel cut oat (SCO) were used to replace wheat flour in the tarhana formulation (control) at the levels of 10, 20, 30 and 40% (w/w). Tarhana with 40% SCO had the highest insoluble dietary fiber (IDF), soluble dietary fiber (SDF) and total dietary fiber (TDF) values, followed by tarhana with 30 and 20% SCO. Control had the lowest beta-glucan content (0.13%) while tarhana with 40% SCO had the highest value (1.50%). As the levels of OF and SCO increased in formulations, beta-glucan contents increased. Results showed that OF and SCO additions improved the nutritional quality of tarhana by causing significant increases in dietary fiber and beta-glucan contents. All of the soups with oat products and control were comparable in terms of the sensory properties. Overall, acceptances of soups were found the best at the sample with 10% OF. It can be concluded that usage of OF and SCO in tarhana did not give negative results in terms of acceptability.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".