Composition, Functional Properties, Starch Digestibility, and Cookie‐Baking Performance of Dry Bean Powders from 25 Michigan‐Grown Varieties
Bibliographic record
Abstract
The chemical composition, functional properties, starch digestibility, and cookie‐baking performance of bean powders from 25 edible dry bean varieties grown in Michigan were evaluated. The beans were ground into coarse (particle size ≤1.0 mm) or fine (≤0.5 mm) powders. Starch and protein contents of the bean powders varied between 34.4 and 44.5% and between 19.1 and 26.6% (dry basis [db]), respectively. Thermal properties, pasting properties, and water‐holding and oil‐binding capacities of the bean powders differed and were affected by particle size. After blending the bean powders with corn starch (bean/starch = 7:3, db), the blends were used for cookie baking following a standard method ( AACC International Approved Method 10‐54.01). Generally, the cookies baked from the fine bean powders had smaller diameters, greater thicknesses, and greater hardness values than those from the coarse counterparts. Differences in the cookie‐baking performances of the bean powders were observed among the 25 varieties. Larger proportions of resistant starch (RS) were retained in the bean‐based cookies (54.7–126.7%) than in the wheat‐flour‐based cookies (10.4–19.7%) after baking. With higher contents of RS and protein, the bean‐based cookies had more desirable nutritional profiles than those baked from wheat flour alone.
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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) | 0.001 | 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".