Honey varietals differentially impact <i>Bifidobacterium animalis</i> ssp <i>lactis</i> survivability in yogurt through simulated <i>in vitro</i> digestion
Bibliographic record
Abstract
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. Objective 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 MRS medium followed by an overlay with a modified selective MRS medium; all underwent anaerobic incubation. B. animalis were enumerated pre-digestion and after oral, gastric, and intestinal digestion. There were two study phases: phase 1 tested four honey varietals at 20% w/w per 170g yogurt, and phase 2 tested seven dosages of clover honey (20, 14, 10, 9, 8, 6, and 4% w/w) per 170g yogurt. Results Similar B. animalis counts were observed between all treatments after oral and gastric digestion (<1 Log 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 to 20% w/w clover honey increased B. animalis survivability after simulated in vitro digestion (up to ∼4.7 Log CFU/g survival; P < 0.05). Conclusion Yogurt with added honey improves probiotic survivability during in vitro digestion. The effective dose of clover honey in yogurt was 10 to 20% w/w per serving (1 – 2 tablespoons per 170g 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 | 0.001 |
| 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".