Effects of pulse crop types and extrusion parameters on the physicochemical properties, in vitro and in vivo starch digestibility of pet foods
Bibliographic record
Abstract
Abstract Background and objectives This study aimed to tackle the research gaps regarding how different pulse types and extrusion conditions influenced starch digestibility and quality attributes of extruded dry pet foods. Flours of round pea, lentil, faba bean, wrinkled pea, and rice (control) were selected to extrude dry pet foods under “mild” (C1) and “extreme” (C2) conditions. Physicochemical properties, in vitro and in vivo starch digestibility of the resulting pet foods were characterized. Findings Under both conditions, wrinkled pea pet foods showed significantly lower damaged/gelatinized‐starch contents and less molecular breakdown than other samples. For all the formulations, the C2 condition gelatinized and degraded starch to greater extents than C1. Consistent with the structural and physicochemical properties of the extrudates, wrinkled pea pet foods exhibited notably lower digestibility in both in vitro and in vivo studies than other formulations. Conclusions Both pulse types and extrusion parameters effectively impacted the quality and starch digestibility of the extruded pet foods. Wrinkled pea flours showed high amylose contents and could be a promising ingredient for producing pet foods rich in resistant starch. Significance and novelty This study revealed the interrelationships among the structures, physicochemical properties, and starch digestibility of extruded pet foods formulated with normal and high‐amylose pulse flours.
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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".