Valorizing Canadian Oat Cultivars to Obtain Non‐Conventional Starch: Pasting, Physicochemical, and Morphological Properties
Bibliographic record
Abstract
Abstract Valorizing non‐conventional raw materials to obtain food products has become a crucial strategy of food processing industries for curbing the current depletion of natural resources worldwide. Thus, non‐conventional starches from protein extraction residues of Canadian oat cultivars are studied regarding their technological properties. Consequently, 29 variables are evaluated in starches originating from 18 oat grain samples harvested from nine locations in Canada. One‐way analysis of variance (ANOVA) is used to analyze the effects of location, cultivar, and their interaction on the properties. Data are also processed using principal component analysis (PCA) and hierarchical cluster analysis (HCA) to reduce the dataset's dimensionality. ANOVA reveals that the starches technological features are greatly affected by harvesting location. PCA and HCA grouped samples into three clusters, explaining >70% of the data variability. Cluster 1 shows the lowest means for each response variable. Cluster 2 presents the highest trough viscosity values, while cluster 3 shows a low swelling factor. For proximate analysis, significantly different values are found for ash and amylose contents. Structural and morphological studies indicate starches with low crystallinity and small, polygonal, and irregular starch granules.
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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 | 0.001 | 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".