Influence of Cultivar, Processing, and Food Form on the Glycemic Index of Barley
Bibliographic record
Abstract
Replacement of refined high glycemic index (GI) carbohydrates by whole grain products such as barley may prevent the progression of cardio‐metabolic disorders due to their favourable effects on glycemia. However, processing, cultivar variation, physiochemical properties and food form may alter their impact on glycemia. To examine these factors on the GI of barley, 3 experiments were performed in separate groups of 10 healthy subjects. GI was determined using standard methodology and in vitro starch digestibility was measured. Cultivars varied in starch and β‐glucan content. In Experiments 1 and 2 cultivars were processed into four fractions ranging from hull removal only to hull, bran, germ, and crease removal. Experiment 3, a wet pasta‐like product was made from 100% barley flour with wet semolina pasta as a control. In experiments 1 and 2 the GI range was (22.8 – 40.9); processing increased the GI significantly (p>0.05). Cultivars differed in their nutrient composition (p = 0.024), but without significant interaction between pearling and cultivars (p>0.05). Regardless of the cultivar, the least processed kernels had significantly lower total starch and higher total low molecular sugars, insoluble and total fiber. Resistant starch (RS) was inversely correlated with the GI (r = −0.58; p< 0.05). The barley pasta had medium‐high GI values similar to semolina pasta. Results suggest that processing affects the GI and the physiochemical properties. These results will improve knowledge in the development of low‐GI functional foods and improve future consumer guidelines. Financial support from the Ontario Ministry of Agriculture.
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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 | 0.001 |
| 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 | 0.001 | 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".