Transforming traditional tarhana into a functional food: Impact of pulse flour substitution on nutritional and rheological properties
Bibliographic record
Abstract
The rising demand for functional food products has sparked interest in integrating pulse-based ingredients into traditional foods. This study investigated the effects of replacing 50% of wheat flour (WF) with pulse flours, pinto bean flour (PBF), chickpea flour (CPF), yellow pea flour (YPF), and red lentil flour (RLF), on the physical, nutritional and functional properties of tarhana, a traditional fermented food from the Middle East and Southeastern Europe. Replacement of WF with pulse flours significantly (p < 0.05) enhanced the protein content of tarhana powders. Mineral analysis revealed significant increases in key micronutrients, such as Mg, P, Fe, and Zn, particularly in PBF and CPF-substituted formulations, contributing positively to the recommended dietary allowance of these minerals. The in-vitro protein digestibility (IVPD) of pulse flour-substituted tarhana remained above 78%, with YPF-substituted tarhana demonstrating the highest IVPD (81.63%). The replacement of WF with pulse flours also led to a significant (p < 0.05) increase in total phenolic content, with the highest levels observed in PBF-substituted tarhana (849.72 mg GAE/100 g db), along with enhanced antioxidant capacity. Phenolic profiling confirmed increased levels of key bioactive compounds, including catechin, myricetin, quercetin, and ferulic acid, in pulse flour-substituted tarhana powders. Rheology tests showed that replacement of WF with pulse flours significantly (p < 0.05) lowered the viscosity of tarhana soups. These findings highlight the potential of pulse flours to enhance the nutritional and functional properties of tarhana, transforming this traditional food into a nutritionally-denser, functional food.
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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 | 0.000 | 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".