A Meta‐Analysis of Enriched Pasta: What Are the Effects of Enrichment and Process Specifications on the Quality Attributes of Pasta?
Bibliographic record
Abstract
Pasta products enriched with ingredients to improve their nutritional value or functionality have become increasingly popular, and substantial research efforts have been directed towards the development of new enriched pasta products. In this work, a meta-analysis was conducted to quantify the impact of enrichment and process specifications on the quality attributes of pasta. A literature search revealed 66 studies on enriched pasta. Process specifications and quality attributes, namely proximate composition, dough, drying, cooking, and mechanical properties, color, and sensory attributes, were extracted from the studies and compiled in a data set. Analysis of the data set revealed significant differences between pasta enriched with high-fiber ingredients and pasta enriched with pulse flour. High-fiber ingredients generally preserved the quality attributes of pasta more effectively than pulse flour. Comparisons based on the drying temperature showed that high drying temperatures generally improve the cooking properties of enriched pasta. Sensory evaluations indicated that enrichment levels below 10% generally do not affect consumer acceptance, but higher enrichment levels significantly decrease it. Pearson correlation coefficients showed that the gelatinization temperature and Farinograph properties are useful indicators of the mechanical properties and sensory attributes of pasta. The meta-analysis revealed the need to better understand the impact of the processing history of the enrichment ingredient on the quality attributes and the health benefits of enriched pasta.
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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 | 0.031 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.050 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".