Development of Low Glycemic Index (GI) Foods by Incorporating Pulse Ingredients into Cereal‐Based Products: Use of In Vitro Screening and In Vivo Methodologies
Bibliographic record
Abstract
Pulse ingredients (pea and lentil flour, pea protein, and pea fiber) were incorporated into 94 different food products. Products included pastas, breads, crackers, extruded snacks, cookies, cereal bars, and muffins. Products were screened for estimated glycemic index using an in vitro method. Based on the screening results, five products (pasta, bread, cracker, granola bar, and cookie) were selected for in vivo glycemic index (GI) testing. For each control (containing 100% wheat flour), a pulse variant (containing up to 50% pulse ingredients) was developed. Ten healthy subjects consumed each test meal in addition to three control white bread meals on separate days during the in vivo GI testing. GI values of the control and pulse variant meals were 61.3 ± 5.1 versus 54.6 ± 7.6 (pasta), 61.4 ± 5.6 versus 53.4 ± 4.7 (focaccia bread), 46.0 ± 4.2 versus 41.5 ± 3.1 (cracker), 35.4 ± 3.6 versus 34.8 ± 5.0 (granola bar), and 41.6 ± 3.8 versus 37.6 ± 3.0 (cookie). The difference did not reach statistical significance (P > 0.05). Mean GI difference between control and pulse variant was 4.8 ± 2.6, with all pulse variants falling into the low GI category. Palatability scores showed no statistically significant difference (P > 0.05) between the control and pulse variant. The data support substituting wheat flour with pulse ingredients to reduce the GI value without changing palatability of the products.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".