Survival of Lactobacillus rhamnosus GG during Simulated Gastrointestinal Conditions Depending on Food Matrix
Bibliographic record
Abstract
When developing new probiotic foods, their protective properties in maintaining viability of probiotics under gastrointestinal conditions should be evaluated. In the current study, human upper gastrointestinal tract simulator (GITS) was used to compare the effect of different food matrixes on the survival of Lactobacillus rhamnosus GG (LGG). pH-auxostat was chosen for the cultivation of LGG cells to obtain culture samples in the same physiological state at maximum growth rate for the GITS experiments. The LGG culture was centrifuged and fast frozen in liquid nitrogen in various liquid food matrixes (commercial UHT milk, soymilk, apple juice, titrated apple juice, whey protein powder drink and M.R.S. Broth as reference) and stored at -400C. During 3-month storage, reduction of viability was significant only for apple juice. In the GITS experiments, bile had a greater negative impact on LGG than acid conditions, also the effect of food matrix was noted - in the case of milk, soymilk and whey protein powder drink only the highest concentration of bile (0.4%) caused a significant drop in the viability of bacteria when compared to apple juice. To maximize the health benefits of foodstuffs, it should be taken into account that the survival of probiotics during fast freezing, storage and gastrointestinal passage is dependent on the food matrix.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".