Bioproduction of Conjugated Linoleic Acid in Yogurt by Probiotic Bacteria
Bibliographic record
Abstract
Conjugated linoleic acid as unique fatty acid of milk fat has beneficial properties including antioxidant‚ anticarcinogen‚ antidiabetic‚ antiblood pressure‚ stimulating the body immune system and reducing cholesterol.The aim of this study was assessing ability of some probiotics for biotransformation of linoleic acid to conjugated linoleic acid.Effect of process variables were investigated on production of conjugated linoleic acid in probiotic yogurt.An 8 run Plackett-Burman design was used to study the effect of 7 variables included: addition of supplements (whey powder‚ the amount of added grape seed oil), fermentation (temperature, pH, incubation time) and inoculum condition (age and size) on biomass and conjugated linoleic acid production in probiotic yogurt containing strains of Lactobacillus acidophilus La5, Bifidobacterium bifidum and Propionibacterium freudenerchii grown in medium containing free linoleic acid.The highest amount of conjugated linoleic acid was obtained by addition of 8% w/v whey powder, addition of 4%v/v grape seed oil in pH=6.0,inoculation of 0.7%v/v Inoculum of 36 h age and fermentation at temperature of 35°C for 27 h.This research showed that at the optimized conditions‚ the amount of conjugated linoleic acid in probiotic yogurt was increased by 40% from an average of 8.01 mg/g fat in non-treated yogurt to 11.03 mg/g fat of probiotic yogurt containing grape seed oil.
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) | 0.000 | 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) | 0.001 | 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".