Evaluation of the Technological Performance of Soft Wheat Flours for Fresh-Pasta Production as Affected by Industrial Refining Degree
Bibliographic record
Abstract
Abstract Nowadays, whole grain and less refined flours deriving from higher extraction rate milling processes have received much attention due to the presence of the external parts of the grain constituting the bran, with well-known health benefits. The use of these flours can represent a rational option for the valorization of native bran with minimal by-product generation while improving the nutritional and functional profile of the end products. This work aims to evaluate the techno-functional characteristics of commercial soft wheat flours with different refining degrees (proximate composition, functional, rheological, and starch-related properties) and their relation to the produced fresh-pasta quality (cooking behavior, mechanical and optical properties, and sensory assessment). Specifically, water holding capacity, fat absorption capacity, and swelling ability of flours gradually decreased with the refining degree (up to 25%, 16%, and 36%, respectively). Regarding the starch properties, the overall gelatinization process resulted to be negatively influenced by higher extraction rates, leading to a lower consistency of the whole grain starch gels (~17% in the maximum force during heating and ~12.39% peak viscosity). Cooked pasta was darker and redder when increasing the extraction rate. In addition, whole grain-based pasta had 42% higher cooking loss, and it was 86% harder and 101% firmer, leading to the production of a less elastic fresh-pasta with lower swelling ability. However, a good quality end product with naturally high nutritional value can be produced with flours with low refining degree. Results are useful to assess the best productive destination of flours basing on their technological properties.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".