Physicochemical and Sensory Quality of Fermented Milk With Different Blends of Theobroma grandiflorum and Theobroma cacao
Bibliographic record
Abstract
The production of fermented milk added of fruits is a considerable alternative for dairy industries and can constitute a rational and logical form of different types of processing, in various flavors, being part of a promising market in the Brazilian scenario. In view of the above, the present study aimed to research the biotechnological processes of production of fermented milk with different blends of cupuaçu (Theobroma grandiflorum) and cocoa (Theobroma cacao). The experimental design used was completely randomized, with six treatments, consisting of different concentrations of pulp (blends) of cocoa and cupuaçu (T1: 100% cupuaçu, T2: 100% cocoa, T3: 50% cupuaçu + 50% cocoa, T4: 60% cupuaçu + 40% cocoa, T5: 70% cupuaçu + 30% cocoa, and T6: 80% cupuaçu + 20% cocoa) and four replicates. The results showed that the production of fermented milk added with 100% cocoa and 70% cupuaçu + 30% cocoa showed good lightness, yellowish color, slight sweetness and moderate acidity, according to the data of a*, b*, %Brix and pH. Fermented milk added of 100% cupuaçu, 70% cupuaçu + 30% cocoa and 60% cupuaçu + 40% cocoa had greater acceptability in regard to color and texture, while fermented milk added of 70% cupuaçu + 30% cocoa had the highest values of acceptability for aroma and flavor. The production of fermented milk with the use of different regional raw materials is feasible, because there is great potential as a sustainable alternative, and the addition of pulp of Amazonian fruits promotes greater nutritional and sensory value in fermented milk, contributing to the regional economy.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".