In vitro fermentation characteristics of acacia fiber using canine fecal inoculum
Bibliographic record
Abstract
Acacia fiber is a highly concentrated source of soluble fiber extracted from the natural resin of acacia trees. This fiber is frequently utilized in the pet food industry as a processing aid to improve texture and stability. The objective of this study was to evaluate the fermentation characteristics of acacia fiber, inulin, pectin (positive control), and cellulose (negative control) using an in vitro fermentation system and canine fecal inoculum. Triplicate samples of each fiber were fermented for 0, 6, 12, and 18 h. Blank-corrected data were analyzed using PROC GLIMMIX of SAS to evaluate the effects of fiber, time, and fiber × time interactions; significance was declared at P ≤ 0.05. Significant fiber × time interactions were observed for pH change, gas and short-chain fatty acid (SCFA) production, and microbiota populations. Pectin and inulin had greater (P < 0.01) gas production than acacia fiber and cellulose. Inulin had the greatest (P < 0.01) pH reduction, followed closely by pectin. Acacia fiber had a small reduction in pH, being lower (P < 0.01) than cellulose after 18 h. Pectin and inulin had greater (P < 0.01) acetate, propionate, butyrate, and total SCFA production than acacia fiber and cellulose. Acetate and total SCFA production were also greater (P < 0.01) in acacia fiber than in cellulose. Bacterial alpha diversity increased (P < 0.01) during acacia fiber fermentation but decreased (P < 0.01) during inulin fermentation. Beta diversity shifted over time and showed separate clustering of bacterial communities among the different fiber sources. The relative abundances of approximately 40 bacterial genera were affected by fiber × time interactions. Specifically, acacia fiber had a greater (P < 0.01) increase in Bacteroides, Faecalibacterium, and Ruminococcus torques group abundance than other fibers. Inulin had a greater (P < 0.01) increase in Prevotella, while pectin tubes had a greater (P < 0.01) increase in Bifidobacterium than other fibers. Overall, the results of this study suggest that acacia fiber is a moderately fermentable fiber, producing low levels of gas and SCFA, increasing bacterial richness and diversity, and modifying several bacterial taxa. Although these data suggest beneficial responses in vitro, research in live animals is needed to confirm potential benefits in dogs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".