Effects of Sodium Alginate and Guar Gum Matrices on the Structure and In Vitro Digestion of Native Corn Starch after Encapsulation Using Spray Drying
Bibliographic record
Abstract
Abstract The study aims to investigate the influence of hydrocolloid matrices, including sodium alginate (SA) and guar gum (GG), on the digestive properties of native corn starch (NCS). The native starch is encapsulated with SA (1% w/v), SA (0.75% w/v) + GG (0.25% w/v), SA (0.50% w/v) + GG (0.50% w/v), SA (0.25% w/v) + GG (0.75% w/v), and GG (1% w/v) through a laboratory‐scale spray drying process. The results reveal novel comparative and cooperative effects of encapsulant materials on starch hydrolysis. SA is found to be a superior encapsulant in terms of enhancing the slowly digestible starch (SDS) and resistant starch (RS) contents and improving the thermal stability of NCS, which is attributed to its rough matrix structure. This effect is attributed to the rough matrix structure of SA, which may offer greater resistance to water and enzyme penetration. The combination of 0.75% w/v SA + 0.25% w/v GG is found to be the most effective in reducing digestibility and enhancing thermal stability (p < 0.05), likely due to synergistic interactions between the two hydrocolloids. These findings provide valuable information on the effects of hydrocolloid matrices on the digestive properties of NCS and lay the foundation for future research.
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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) | 0.000 | 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".