Characteristics of Modern Triticale Quality: The Relationship Between Carbohydrate Properties, α‐Amylase Activity, and Falling Number
Bibliographic record
Abstract
ABSTRACT Triticale is a high‐yielding cereal crop with potential to increase grain production for human consumption over the coming decades. Minimal targeted selection has been conducted to produce cultivars with α‐amylase, amylose, and nonstarch polysaccharide (NSP) content appropriate for a milling market. Nevertheless, genotypic variability exists. Standard quality screening methods used for wheat, including pasting properties, falling number, and quantification of α‐amylase activity were employed to assess the environmental and genotypic variability among modern triticale cultivars and to investigate the suitability of these tests for triticale. Samples of 11 triticale lines from four environments were compared with five wheat cultivars bred for various end uses. Triticale exhibited a greater range than wheat for most tested variables, and the ranges usually overlapped. Triticale exhibited higher NSP content, generally equivalent pasting properties, higher α‐amylase activity, and lower falling number on average compared with wheat checks. However, low falling number was not indicative of high α‐amylase activity; the relationship with NSP level and other factors is discussed, and caution is recommended for interpretation of previous research. Three cultivars with equivalent α‐amylase activity to wheat and two with partially waxy starch were identified. These findings have great significance for research and the emerging triticale milling market.
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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".