Honey Varietals Differentially Impact Bifidobacterium animalis ssp. lactis Survivability in Yogurt through Simulated In Vitro Digestion
Bibliographic record
Abstract
BACKGROUND: Bifidobacterium animalis ssp. lactis DN-173 010/CNCM I-2494 (B. animalis) is a probiotic strain commonly added to yogurt. Yogurt and honey are a popular culinary pairing. Honey improves bifidobacteria survival in vitro. However, probiotic survival in yogurt with honey during in vitro digestion has not been investigated. OBJECTIVES: The study aimed to evaluate the effects of different honey varietals and concentrations on B. animalis survivability in yogurt through in vitro digestion. METHODS: Yogurt with honey or control-treated samples underwent in vitro simulated oral, gastric, and intestinal digestion. B. animalis cells were enumerated on de Man Rogosa and Sharpe (MRS) medium followed by an overlay with a modified selective MRS medium; all underwent anaerobic incubation. B. animalis were enumerated predigestion and after oral, gastric, and intestinal digestion. There were 2 study phases: Phase 1 tested 4 honey varietals at 20% wt/wt per 170 g yogurt, and Phase 2 tested 7 dosages of clover honey (20, 14, 10, 9, 8, 6, and 4% wt/wt) per 170 g yogurt. RESULTS: Similar B. animalis counts were observed between all treatments after oral and gastric digestion (<1 Log colony forming units (CFU)/g probiotic reduction). Higher B. animalis survivability was observed in yogurt with clover honey after exposure to simulated intestinal fluids (∼3.5 Log CFU/g reduction; P < 0.05) compared to all control treatments (∼5.5 Log CFU/g reduction; P < 0.05). Yogurt with 10-20% wt/wt clover honey increased B. animalis survivability after simulated in vitro digestion (≤ ∼4.7 Log CFU/g survival; P < 0.05). CONCLUSIONS: Yogurt with added honey improves probiotic survivability during in vitro digestion. The effective dose of clover honey in yogurt was 10-20% wt/wt per serving (1-2 tablespoons per 170 g yogurt) for increased probiotic survivability during in vitro digestion.
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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 | 0.001 | 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".