Nutritional Composition and In Vitro Starch Digestibility of Crackers Supplemented with Faba Bean Whole Flour, Starch Concentrate, Protein Concentrate and Protein Isolate
Bibliographic record
Abstract
The nutritional quality of common wheat-based foods can be improved by adding flours from whole pulses or their carbohydrate and protein constituents. Faba bean (Vicia faba L.) is a pulse with high protein concentration. In this study, prepared faba bean (FB) flours were added to wheat based baked crackers. Wheat cracker recipes were modified by substituting forty percent wheat flour with flours from whole faba bean, starch enriched flour (starch 60%), protein concentrate (protein 60%) or protein isolate (protein 90%). Baked crackers were ground into meal and analyzed for their macronutrient composition, starch characteristics and in vitro starch hydrolysis. Faba bean supplemented crackers had lower (p ≤ 0.001) total starch concentrations, but proportionally higher protein (16.8–43%), dietary fiber (6.7–12.1%), fat (4.8–7.1%) and resistant starch (3.2–6%) (p ≤ 0.001) than wheat crackers (protein: 16.2%, dietary fiber: 6.3%, fat: 4.2, resistant starch: 1.2%). The increased amylose, amylopectin B1- chain and fat concentration from faba bean flour and starch flour supplementation in cracker recipe contributed to increased resistant starch. Flours from whole faba bean, starch or protein fractions improved the nutritional properties and functional value of the wheat-based crackers. The analytical analysis describing protein, starch composition and structure and in vitro enzymatic hydrolysis advance understanding of factors that account for the in vivo benefits of faba bean flours added to crackers in human physiological functions as also previously shown for pasta. The findings can be used to guide development of improve nutritional quality of similar wheat-based food products.
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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".