A Fast, Simple, and Reliable Method to Predict Pasta Yellowness
Bibliographic record
Abstract
ABSTRACT Pasta yellowness depends on the semolina carotenoid content, carotenoid degradation by lipoxygenase (LOX), and pasta processing conditions. In breeding programs, early generation lines are selected for high grain yellow pigment content with the intent to improve pasta color. This approach has been successful in increasing the grain yellow pigment of Canadian durum wheat in the last few decades. In recent years, however, a weak relationship between pasta yellowness ( b *) as measured by a Minolta spectrophotometer and semolina yellow pigment content ( r = 0.19–0.52) was noted in the Canadian durum wheat lines. Thus, total semolina yellow pigment content cannot effectively predict the yellowness of its pasta product. Therefore, a fast and simple method was developed to predict pasta yellowness by measuring semolina dough sheet color at different time intervals after sheeting (0.5, 2.0, and 24 hr). Spaghettis were processed from the semolina samples at two drying temperature cycles (70 and 90°C). There were significant correlations between dough sheet b * values at all three times and spaghetti b * values at both drying temperatures ( r = 0.87–0.94). Semolina dough sheet can be easily prepared in 15 min and requires only 30 g of material. Shortly after sheeting (30 min), dough sheet b * values can be used to predict pasta yellowness without producing the end product (involving mixing, extrusion, and drying). In this study, we also found that dough sheet b * values increased significantly with time over the sampling intervals after sheeting for those breeding lines with superior pasta color. DNA analysis revealed that all those lines lacked the Lpx‐B1.1 duplication.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 teacher head, 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".