Drying of Durum Wheat Pasta and Enriched Pasta: A Review of Modeling Approaches
Bibliographic record
Abstract
Models on drying of durum wheat pasta and enriched pasta were reviewed to identify avenues for improvement according to consumer needs, product formulation and processing conditions. This review first summarized the fundamental phenomena of pasta drying, mass transfer, heat transfer, momentum, chemical changes, shrinkage and crack formation. The basic equations of the current models were then presented, along with methods for the estimation of pasta transport and thermodynamic properties. The experimental validation of these models was also presented and highlighted the need for further model validation for drying at high temperatures (>-100°C) and for more accurate estimation of the pasta diffusion and mass transfer coefficients. This review indicates the need for the development of mechanistic models to improve our understanding of the mass and heat transfer mechanisms involved in pasta drying, and to consider the local changes in pasta transport properties and relaxation time for more accurate description of the moisture transport near glass transition conditions. The ability of current models to describe dried pasta quality according to the consumers expectations or to predict the impact of incorporating ingredients high in nutritional value on the drying of these enriched pasta was also discussed.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".