Effect of cooked rice with added fructo-oligosaccharide on faecal microorganisms investigated by in vitro digestion and fermentation
Bibliographic record
Abstract
In the present study, the effects of cooked rice (CR) with added fructo-oligosaccharide (FOS) on faecal flora were studied by a simulated in vitro digestion and fermentation method. The total carbohydrate content, pH, and short-chain fatty acids (SCFAs) were determined during in vitro digestion and fermentation. The change in the bacterial phase distribution after the fermentation was also analysed. The results showed that the total carbohydrate content of the CR with added FOS (FCR) significantly decreased during the simulated digestion. Meanwhile, the pH of the FCR decreased and the SCFAs concentration increased significantly compared to those of the CR during the simulated fermentation. In addition, the FCR showed the advantage of promoting beneficial bacteria, such as Bifidobacterium and Lactobacillus, and inhibiting harmful bacteria, such as Bacteroides and Klebsiella compared to the CR. Therefore, the FOS as a prebiotic could be recommended to produce the high-quality healthy rice food.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.001 | 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".