Effect of Ferulic Acid and Catechin on Sorghum and Maize Starch Pasting Properties
Bibliographic record
Abstract
ABSTRACT The effects of ferulic acid and catechin on starch pasting properties were studied as part of an investigation into the structure and functionality of phenolics in starch‐based products. Commercial maize starch, starches from sorghum cultivars (SV2, Chirimaugute, and DC‐75), and the phenolic compounds ferulic acid and catechin were used in the investigation. Pasting properties were measured using rapid viscosity analysis. Ferulic acid and catechin (up to 100 mg each) were added to maize or sorghum starch (3 g, 14% mb) in suspensions containing 10.32% dry solid content. Addition of catechin resulted in pink‐colored pastes, whereas ferulic acid had no effect on paste color. Ferulic acid and catechin decreased hot paste viscosity (HPV), final viscosity, and setback viscosity of maize and sorghum starch pastes, but had no influence on the peak viscosity (PV) of the former. Both phenolics increased breakdown viscosity. Ferulic acid had greater influence on HPV, final viscosity, breakdown, and setback than catechin. Addition of catechin under acidic conditions (pH 3) decreased HPV, final viscosity, and setback of maize starch, but alkaline conditions (pH 11) slightly increased setback. Both acidic and alkaline conditions resulted in increased breakdown. Investigations on model‐system interactions between ferulic acid or catechin and starch demonstrated that phenolic type and pH level both significantly influence starch pasting properties, with ferulic acid producing a more pronounced effect than catechin. The significance of these interactions is important, especially in food matrices where phenolics are to be added as functional food ingredients.
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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) | 0.001 | 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".