A “berry” small inclusion: 40 types of commercial dog and cat kibble with added blueberries provide low levels of quercetin, free phenolics, and alkali-labile phenolics
Bibliographic record
Abstract
Blueberries provide dietary polyphenols and are often included in dog and cat kibble as a source of antioxidants. For this study, we investigated the polyphenol content in commercial dog and cat extruded food that lists blueberries on their ingredient deck. We sampled 40 bags of kibble (18 cat and 22 dog) from four pet food stores in Guelph, Canada. High-performance liquid chromatography was employed for the determination of quercetin, free phenolics (caffeic acid; cinnamic acid; ferulic acid; gallic acid; hesperetin; naringin; p-coumaric acid; p-hydroxybenzaldehyde; protocatechuic acid; syringaldehyde; syringic acid; vanillic acid; vanillin), and antioxidant-protected alkali-labile phenolics (caffeic acid; chlorohenic acid; cinnamic acid; ferulic acid; gallic acid; hesperetin; naringin; p-coumaric acid; p-hydroxybenzaldehyde; protocatechuic acid; sinapic acid; syringaldehyde; syringic acid; vanillic acid; vanillin). Mean concentration ± SE was calculated for all assessed polyphenol types. The ANOVA procedure was used to determine if the intended species (cat or dog) affected quercetin and antioxidant-protected alkali-labile polyphenol concentrations. Quercetin concentrations were found at 5.05 ± 4.32 µg/g across all bags. Cat kibble had lower average concentrations of quercetin and alkali-labile phenolics compared to dog kibble, which is likely linked to the quantity of added fruits and vegetables. Concentrations of free phenolics were minimal to nonexistent for all kibble types. Dog and cat kibble containing blueberries do not provide significant amounts of dietary polyphenols and, therefore, do not contribute to enhanced sources of antioxidants and anti-inflammation. Future research should assess optimal polyphenol doses in healthy cats and dogs to determine the target doses to achieve physiological benefits.
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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) | 0.003 | 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".