The effects of cooking, cooling and reheating on the Glycemic Index depends on potato variety
Bibliographic record
Abstract
Potatoes are a nutrient dense, low calorie food, but are often perceived as unhealthy in part because they are thought to have a high Glycemic Index (GI). High GI diets are associated with increased risk of diabetes and cardiovascular disease. The GI is a measure of the blood glucose raising potential of carbohydrate containing foods. We previously found that eating cooled or reheated potatoes reduces their GI by 30–40%. The aim of this study was to see if cooling and reheating had the same effect on the GI in different potato varieties. Thus, we determined the GI of four different types of potato, each of which was boiled and served in 3 ways: freshly cooked, cooled and served cold, or cooled and reheated. ANOVA showed, no main effect of potato variety, but a significant main effect of cooling with cold potatoes having a lower GI than freshly cooked or reheated potatoes (P<0.05). However, there was also a significant cooking*variety interaction, with cooling having a significantly greater effect in one variety than two of the others (P<0.05). We conclude that the effect of cooling and reheating on the GI of boiled potatoes differs in different varieties. Further work is required to determine the physiochemical properties of the starch which may explain these effects. Funded by the Agriculture and Agri‐Food Canada‐ Agriculture Bioproducts Innovation Program (ABIP).
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".