Reduced <i>in vitro</i> starch hydrolysis and <i>in vivo</i> glycemic effects after addition of soy presscake to corn tortillas
Bibliographic record
Abstract
BACKGROUND: Chronically elevated blood glucose leads to development of prediabetes and type 2 diabetes, as well as increased risk for heart and kidney disease and vision loss. For many, elevated blood glucose can be managed through diet and exercise. Consequently, the availability of foods that limit blood glucose elevation would aid in addressing this global problem. This paper investigated the effect of adding soy presscake (SP) to corn tortillas on starch hydrolysis in vitro as well as the glycemic responses elicited in vivo upon consumption of these modified tortillas. RESULTS: SP in corn tortillas decreased the rate and extent of starch hydrolysis in vitro. The in vivo glycemic index (GI) values decreased from 43 for corn control tortillas to 31 with 40% SP fortification. A high correlation (r = 0.9781) was found between the GI values from in vivo analysis and the area under the curve of starch hydrolysis in vitro. The best correlations (r > 0.96) between GI and degree of hydrolysis were found at 45-90 min of in vitro starch hydrolysis. CONCLUSIONS: Incorporating SP into corn-based tortillas lowers glycemic responses to them. In addition, in vitro starch hydrolysis could be used to estimate the GI values of food products and, in particular, the comparison of multiple items during food product development. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| 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".