Development of Low Glycemic Index (GI) Foods by Incorporating Pulse Ingredients into Cereal‐ based Products: Use of <i>in vitro</i> screening and <i>in vivo</i> GI determination
Bibliographic record
Abstract
Pulses are environmentally sustainable, highly nutritional and energy efficient crops. Pulse ingredients (pea and lentil flour, pea protein concentrate and pea fiber) were incorporated into food products to develop low Glycemic Index (GI) products. Over 100 different products were formulated including pastas, breads, crackers, extruded snacks, cookies, cereal bars and muffins. Products were screened for estimated GI (eGI) using an in vitro method. Total starch and resistant starch content were also determined using an assay method. Based on the screening results, five products (pasta, bread, cracker, granola bar, and cookie) were selected for in vivo GI testing. For each control (containing 100 % wheat flour), a pulse variant (containing up to 50 % of pulse flour) was developed. 10 healthy subjects (5M, 5F; 36±14y, 23±4kg/m 2 ) tested each test meal (5 control and 5 pulse variant) in addition to 3 control dextrose meals on separate days and blood samples were collected over 2 hours. GI values of the control and pulse variant meals were 61.3±5.1 vs. 54.6±7.6 (pasta), 61.4±5.6 vs. 53.4±4.7 (focaccia bread), 46.0±4.2 vs. 41.5±3.1 (cracker), 35.4±3.6 vs. 34.8±5.0 (granola bar), and 41.6±3.8 vs. 37.6±3.0 (cookie). Mean GI difference between control and pulse variant was 4.8±2.6 with all pulse variant falling into the low GI category. The greatest reduction occurred in pasta and bread products containing 50% and 30% of lentil flour, respectively. Resistant starch was significantly higher in the pulse variant except granola bar where a mean of control and pulse variant were 0.42±0.096 and 0.79±0.23 ( p< 0.05), respectively. Palatability scores showed no significant difference between control and pulse variant. The data supports that substituting wheat flour with pulse ingredients reduces 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 | 0.000 | 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) | 0.001 | 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".