Development of Cheese Analogue using Olive Oil and Lactobacillus Bulgaricus
Bibliographic record
Abstract
Cheese considered as a well-known dairy product which is manufactured in many varieties according to its texture and flavors. Cheese is formed by coagulation of casein and having high protein contents. Due to increase awareness of modern consumers’ fortification of dairy foods including fresh cheese are in demand. Cheese analogues are made for fulfilling the demand of cheese. Cheese analogues are processed cheese-like product and enriched in nutrients. It is healthy and seems to be attractive when it is rearranged and prepared by using ingredients coming from natural source. Cheese analogue produced from olive oil is used as an alternate of cheese. Olive oil improves cardiovascular risk factors, such as endothelial dysfunction, blood pressure, postprandial hyper-lipidemia, lipid profiles, antithrombotic profiles and oxidative stress. The objective of present study is to develop cheese analogue using olive oil and Lactobacillus bulgaricus. Single step emulsification was done for fat stabilization. L. bulgaricus was isolated from yoghurt. Cheese analogue was subjected to physicochemical, microbiological and sensory analysis. Proximate analysis (moisture, pH, fat contents, ash, total solids and acidity) physicochemical analysis, sensory analysis and rheological analysis were performed. Physiochemical investigation has demonstrated that, with an increase in the olive oil level in cheese, non-significant pH, moisture, fat, total solids, total nitrogen and protein content were considerably influenced by olive oil amounts. Rheological research showed that olive oil quantity has a substantial effect on curd texture. Flavor and overall acceptance were significantly affected by days and concentration. Samples indicate more substantial results and general acceptance compared to other samples treated with minimum olive oil concentrations. The data obtained was analyzed statistically.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".