Rheological Approaches Suitable for Investigating Starch and Protein Properties Related to Cooking Quality of Durum Wheat Pasta
Bibliographic record
Abstract
Abstract Starch and protein properties of semolina and pasta samples were investigated using MVAG and GPT, which are generally used for starch and common wheat flour characterization. From two semolina, which have different starch and protein content and pasta‐making qualities, four spaghetti samples were produced and dried using low‐ or high‐temperature drying. Starch and protein arrangements in dried pasta were related to pasta cooking behavior. The tests discriminated semolinas according to their technological quality. Good quality semolina (A) exhibited a high pasting temperature, low hot viscosity, and high and earlier protein aggregation properties. In regard to pasta, when dried at a low temperature, spaghetti from sample A showed lower cooking loss than pasta from poor quality semolina (B), which is probably related to the low starch swelling and a strong network. The use of HT cycle lowered the differences in cooking quality and starch and protein properties related to the raw‐materials features. Practical Applications The development of a rapid method for evaluating semolina quality and how it relates to starch, protein properties and pasta cooking quality is of great interest for the pasta‐making industry. This research highlights that MVAG and GPT tests are able to discriminate semolina according to their technological quality in a short time and using a low amount of sample. In addition, the tests gave useful information for understanding the effect of both raw‐materials characteristics and drying conditions on starch and protein macromolecules in determining the final cooking quality.
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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) | 0.001 | 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 | 0.001 |
| 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".