Changes in Semolina Yellow Pigment Content and Carotenoid Composition During Pasta Processing
Bibliographic record
Abstract
Changes in total yellow pigment (TYP) content and carotenoid composition were examined at different stages of pasta processing. Semolina samples were milled from durum genotypes with and without the Lpx‐B1.1 gene deletion and then processed into dry pasta. Significant pigment loss (12.8–15.3%) based on TYP content was observed from semolina to dough in genotypes without the gene deletion. Such loss remained low (2.0–2.8%) for genotypes with the Lpx‐B1.1 gene deletion. Extrusion and drying processes did not result in substantial pigment loss. The overall pigment loss (from semolina to dried pasta) of genotypes with the gene deletion was 9.1–12.8%, in comparison with 19.0–21.7% in genotypes without the deletion. Changes in carotenoids examined by ultra‐performance liquid chromatography showed that lutein decreased gradually from representing 80% of total carotenoids to 70% of total carotenoids during pasta processing. The reduction of lutein was mostly during dough mixing, with a decrease of 16.7% in genotypes with the Lpx‐B1.1 deletion and 27.8% in genotypes without the deletion. Minor carotenoids increased during pasta drying, possibly at the expense of lutein. Results of this study showed that although breeding for elevated yellow pigments is the key, pasta color can be further improved by reducing pigment losses at different stages of pasta processing through selection of genotypes with Lpx‐B1.1 deletion and applying a vacuum during mixing and extrusion processes.
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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 | 0.001 |
| 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".