Phytosterol Effects on Milk and Yogurt Microflora
Bibliographic record
Abstract
Phytosterols play a major role in functional foods. Their ability to reduce serum cholesterol in humans has been widely proven and they are now being added to various dairy based products. The present study investigated the potential antimicrobial activity of phytosterols in milk and their effect on yogurt starter cultures. A commercial phytosterol (0.26% to 1.8%, w/v) preparation (CPP) consisting of ss-sitosterol, campesterol, sitostanol, and campestanol had no effect on the standard plate count (SPC) and psychrotroph population in pasteurized milk stored at 4 degrees C. In addition, a challenge study employing Pseudomonas spp. in milk at 4 to 7 degrees C confirmed that the CPP was not antimicrobial. However, the addition of a dispersible CPP consisting of 0.72% phytosterol containing 0.02% to 0.03% sodium stearoyl lactylate (SSL) did appear to affect the SPC and psychrotrophic bacteria in refrigerated milk. The dispersible preparations did not, however, inhibit the growth of Pseudomonas. An investigation into the antimicrobial activity of SSL revealed that it alone had no effect on the SPC in milk. The CPP had no effect on growth and acid development by Lactobacillus bulgaricus and Streptococcus thermophilus during yogurt production at 33 degrees C and storage at 4 degrees C for 30 d. This is seen as a beneficial feature since growth and acid development by these organisms are crucial for yogurt quality. Saccaromyces cerevisiae and Aspergillus ochraceous added to yogurt as typical contaminants also were not inhibited. While the CCP was somewhat antimicrobial when formulated with dispersing agents, it otherwise had no antimicrobial activity.
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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 | 0.000 | 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".