Grain constituents and starch characteristics influencing in vitro enzymatic starch hydrolysis in Hungarian triticale genotypes developed for food consumption
Bibliographic record
Abstract
Abstract Background and objectives Triticale is mostly used for feed but there is an increasing interest for food consumption. In cereals, major storage carbohydrate is starch, whose concentration and composition influence the end use of the grain. Therefore, grain constituents focused on starch characteristics were analyzed in 11 triticale genotypes and compared to wheat and rye. Findings Significant genotypic variation was detected among the analyzed parameters, in triticale. Starch concentrations (59.2%–66.1%) were similar to wheat, and amylose varied in wide range (23.9%–34.5%). The A‐type starch granules had higher volume (70.5%–81.9%) similar to rye, but the amylopectin structure was in between rye and wheat. Both grain constituents and starch properties influenced starch in vitro enzymatic hydrolysis, where the triticale average was similar to wheat in meal, but extracted starch was more like in rye. Conclusions The advantageous properties and genotypic variation possessed by triticale starch suggest that targeted selection can be used to improve triticale grain quality and opens up the opportunity to use triticale not just in human food but develop value‐added products. Significance and novelty This research provides knowledge to understand better the food‐use aspects of triticale and revealed novel information about starch characteristics and amylopectin structure in relation to starch hydrolytic properties.
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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".