Whole Grain Gluten-Free Vegetable Savory Snacks
Bibliographic record
Abstract
Gluten-free savory snacks were formulated and evaluated to offer nutritious treats for all and healthy option for gluten intolerance individuals. Four kinds of savory snacks (gluten-free, whole grains with fresh vegetables, low in fat and salt) were developed using base formulation (BF) of brown rice flour (45%), sorghum flour (20%), tapioca flour (7%), mashed potato (8%), canola oil (6%), guar gum (2%), baking powder (1.5%) and salt (0.5%). Fresh vegetables (carrots, broccoli, spinach, and red onion) were chopped and mixed with the base formulation (1:1). Chopped fresh garlic (5%) was added to carrot, broccoli and spinach (base-vegetable mix, BFV). Snack dough was prepared using 100 mL water per 100 g BFV. Two portions of snack dough (about 10 g each) were placed on the preheated KrumKake Baker and cooked for 2 minutes. Sixty two in-house volunteers judged Broccoli-Garlic snacks as significantly (p ? 0.05) better in color/appearance than Carrot-Garlic, Spinach-Garlic and Red Onion savory snacks. A 30 g serving of these low salt, low fat healthy snacks would provide 5-7% of daily recommended dose of dietary fiber and potassium. This is the first report of developed whole grain gluten-free, 50% vegetable snacks. Texture and water activity of the developed snacks suggests the crispiness and potential long shelf stability. Tasters judged Carrot-Garlic (88%), Broccoli-Garlic (77%), Spinach-Garlic (68%) and Red Onion (65%) acceptable. Data suggest that the acceptability of gluten-free whole grain vegetable savory snacks is very encouraging and offers healthy alternative for all and especially for those sensitive to gluten.
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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 | 0.000 | 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) | 0.003 | 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".