Exploration of the functional properties of hydrothermally treated Canary seed (Phalaris canariensis L.) flour for food applications
Bibliographic record
Abstract
• Hydrothermal treatment (HT) affected the functional properties of canary seeds significantly. • HT affected mean particle size, damaged starch content, and water absorption capacity. • HT affected the starch pasting and thermal properties of flours. • Nutritionally, the HT significantly improved in vitro protein digestibility of flour. • HT eliminated the microbial load present in the raw hairless Canary seed flour. This research investigated the changes in physiochemical and techno-functional properties, protein nutritional quality, and microbial properties of Canary seed flour following hydrothermal treatment. Four hairless Canary seeds were dehulled, steamed, dried, and ground to flour. The hydrothermal treatment increased the mean particle size (∼80 %), damaged starch content (∼260 %), and water absorption capacity (∼46 %), while not affecting the flour’s acidity (pH ∼6.4), bulk density (∼0.6 g/mL), or oil absorption capacity (∼2.0 %). Hydrothermal treatment affected all the pasting and thermal properties of Canary seed flour except for the pasting temperature. Nutritionally, the treatment significantly improved in vitro protein digestibility, although it did not affect protein content (∼20.0 %), amino acid content, amino acid score (0.3 – 0.5), or in vitro protein digestibility corrected amino acid score (in vitro PDCAAS) (0.2 – 0.4). Most importantly, heat treatment significantly enhanced the microbial quality by eliminating the microbial load in the raw Canary seed flour, ensuring the safety of the product. Our results suggest that hydrothermally treated canary seed flour has improved physicochemical and functional properties, making it a better substitute than its raw counterpart.
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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 | 0.000 |
| 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 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".