Variation in Nutritional Composition of Australian Mungbean Varieties
Bibliographic record
Abstract
Australian mungbean production is primarily focused in Central and Southern Queensland and Northern NSW, with around 95% of the total mungbean crop exported as a commodity to overseas markets. Significant improvement in pulse crops such as mungbean have primarily been achieved from plant breeding approaches focused on increasing yield and disease resistance. Whilst this remains crucial for the ongoing success and production of the crop, further improvements could be achieved through an increased understanding of nutritional variation, varietal performance and the impact of environmental and agronomic factors on overall nutritional quality. In this survey, the primary objective was to evaluate and compare the nutritional composition of three commercial Australian mungbean varieties (Crystal, Satin II and Celera II-AU), grown in different regions in Queensland (Warra and Hermitage sites) and New South Wales (Liverpool Plains and Northern NSW sites). Mungbean varieties were evaluated in terms of visual seed appearance, measuring seed colour and size, prior to comprehensive nutritional evaluation of milled mungbean flours in order to determine the extent of variation between varieties and regions. Moisture, protein, ash, fat, dietary fibre (total, insoluble and soluble fibre), starch and amylose composition, starch pasting properties (RVA profile) and amino acid compositions were evaluated and compared. This survey may potentially lead to a larger scale evaluation in the future, broadening the scope to include other significant Australian pulse crops such as faba bean and chickpea. Ultimately, the information gathered from this survey may assist plant breeders, producers and processors in regard to improving, growing, processing and value-adding Australian mungbean for both domestic and export markets.
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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 | 0.001 | 0.001 |
| 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".