Impact of starch-hydrocolloid interaction on pasting properties and enzymatic hydrolysis
Bibliographic record
Abstract
Hydrocolloids are extensively used for food processing because their techno functional properties (emulsifier, stabilizer, and structural agent). But there is increasing interest in their role connected with nutritional improvements, particularly related to starch hydrolysis rates, which might involve the viscosity resulting from starch-hydrocolloid interaction. The objective of this research was to investigate the impact of gels viscosity on the enzymatic hydrolysis of a range of starch gels made with different starches and hydrocolloids. Heterogeneous systems (starch-hydrocolloid) were prepared with several starches (corn, wheat, rice, potato, cassava, pea) and hydrocolloids (locust bean gum, guar gum, xanthan gum, hydroxypropylmethylcellulose K4M, psyllium) at different concentrations (0%–0.5% - 2.5%). The starch-hydrocolloid pasting behavior and their susceptibility to amylase hydrolysis was recorded with the Rapid Viscoanalyzer following a rapid method (Santamaria, Montes, Garzon, Moreira, & Rosell, 2022a). The viscosity decay due to alpha-amylase activity was modeled to obtain starch gels hydrolysis rate (k). A negative correlation was found among kinetic constant (k) and viscosity at 37 °C (r = −0.55), setback (r = −0.50), and area under the pasting curve (r = −0.42). For instance, xanthan gum and psyllium addition showed strong negative correlation between kinetic constant and viscosity at 37 °C (r = −0.75) and setback (r = −0.79), respectively, particularly when blended with potato starch. These correlations indicate that pasting properties of the starch-hydrocolloid systems might be predictors of the enzymatic digestion rate of the gels, allowing the design of foods with controlled postprandial glucose response.
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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 | 0.000 | 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".