Factors affecting dehulling of hairless canary seed (<i>Phalaris canariensis</i> L.)
Bibliographic record
Abstract
Abstract Background and Objectives Canary seed is a novel cereal grain that needs dehulling before being used in food applications. This work aimed to develop dehulling quality scoring criteria and study the hullability of Canary seeds. Methods for dehulling Canary seeds were studied using compressed air, impact, and abrasion‐type dehulling equipment. A response surface methodology (RSM) was used to design experiments to assess the effect of initial sample weight, heat treatment, tempering, and dehulling time on the dehulling efficiency and groat quality. Findings The results suggest that tempering level and starting weight had a significant ( p < .0001) positive effect, whereas time and heat treatment had a negative effect on the dehulling efficiency. Compressed air dehulling resulted in reduced broken groats and groat abrasion compared to the impact dehulling conditions studied. An optimized method was used to study the effect of genotype and growing environment on the hullability of four hairless Canary seed varieties grown in two growing years at one location. The milling yield ranged from 57% to 67% and was affected by genotype ( p < .0001), growing year ( p < .001), and their interaction ( p < .001). Conclusions The results suggest that breeding programs should consider the dehulling and milling quality of Canary seed. Apart from milling yield, the breeding programs and milling industry should also consider adopting visual assessment of the groat quality using the tool presented in this work. Significance and Novelty In this work, a visual assessment tool for Canary seed groat quality is developed that can be used for breeding programs and industry to compare sample performance. Also, our research has provided factors to be considered by the industry when dehulling hairless Canary seeds to maximize milling yield and groat quality.
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) | 0.000 | 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".