Strategies to improve the survival of probiotic <i>Lacticaseibacillus rhamnosus</i> R0011 during the production and storage of granola bars
Bibliographic record
Abstract
The goal of this study was to evaluate the effectiveness of two approaches to protect the viability of probiotic cells during granola bar manufacturing and storage: microencapsulation (ME) and inclusion in chocolate chips. In the process used, hot honey (138 °C) was blended with cereal ingredients, resulting in an initial blend temperature of 52 °C. Chocolate chips carrying probiotics were added; however, when the blend was cooled to 42 °C. The viability of Lacticaseibacillus rhamnosus R0011 probiotic was assessed by flow cytometry (FC) and plating (CFU). There was an uneven distribution of inoculated probiotic bacteria throughout the cereal bars, resulting in variability in the CFU data. By providing total and viable counts, FC assessed the correct number of inoculated cells in the sample, which enabled the accurate calculation of survival levels. Spray coating with ME increased survival during manufacturing, but ME in alginate particles was detrimental. Including the cultures in chocolate improved the stability of the probiotics during storage at 25 °C, but only in the first 4 weeks. FC analyses showed that viability losses during bar manufacturing could be linked to damage to the cell membrane, but less so during storage.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".