Microbial and Sensory Analysis of Soy and Cow Milk-Based Yogurt as a Probiotic Matrix for Lactobacillus rhamnosus GR-1
Bibliographic record
Abstract
Plant-based milk alternatives represent a growing sector of the functional food industry due to consumer demand for more nutritious and sustainable options. Soymilk is abundant in fibre, phytosterols, and isoflavones. In contrast, cow milk has a high cholesterol and caloric content, superior organoleptic characteristics, and a well-established probiotic delivery matrix. Supplementing cow milk with soymilk to produce probiotic yogurt may enhance the nutritional value, sensory profile, and probiotic delivery capacity of the final product. In order to investigate the probiotic potential and sensory appeal of this blend, four yogurt mixtures were prepared by incorporating 0% (T1), 25% (T2), 50% (T3), or 75% (T4) soymilk in cow milk. The viability of Lactobacillus rhamnosus GR-1 and pH were evaluated during fermentation (6 h) and refrigerated storage (30 days). Additionally, consumer acceptability was determined through a sensory evaluation. L. rhamnosus GR-1 reached viable counts of 108 colony forming units (CFU)/mL in all treatments. Sensory panellists provided higher hedonic scores to T1 for appearance and texture compared to T2–T4, but flavour and overall acceptability ratings amongst T1–T4 were comparable. These results serve as an indication for the successful fortification of cow and soymilk yogurt mixtures with L. rhamnosus GR-1.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".