Effects of Genotype and Growing Year on the Nutritional Composition and Pasting Properties of Glabrous Canary Seed (Phalaris canariensis L.) Groat Flours
Bibliographic record
Abstract
Canary seed flour is a new food ingredient that the United States Food and Drug Administration (FDA) and Health Canada recently granted Generally Regarded as Safe (GRAS) status. Stability in nutritional composition and functional properties is an essential characteristic of food ingredients for consistency in nutritional quality and performance in processing. This work assessed the effect of genotypic and environmental variation on the nutritional (protein, starch, amylose, oil, dietary fiber, minerals and fat-soluble vitamins) and pasting (as measured in viscosity (peak, trough, breakdown, final, and setback), peak time, and pasting temperatures) properties of Canary seed. The samples included four Canary seed varieties grown in randomized complete block design experiments at one location for two growing seasons. In general, the nutritional composition of Canary seed flour was not affected by genotype, growing year, and their interaction except for starch content, which was significantly affected by the growing year (p < 0.0001), and iron content, which was affected by genotypic variation (p < 0.0001). The pasting properties of Canary seed flour were significantly (p < 0.001) affected by both genotypic and growing year variation but not their interaction. Our results suggest that the food industry should measure starch and iron content prior to processing to ensure consistency in nutritional labeling. Also, for those applications where starch pasting properties are essential, the manufacturer should consider measuring the RVA pasting viscosities for every batch of raw material. The results have provided the baseline knowledge of which nutritional or functional properties of Canary seed flour can be improved through breeding and agronomy programs to ensure the reliability of Canary seed as an ingredient.
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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".