The Brazilian Way to Consume acai: Do guarana Extract and Sugar Concentrations Influence on Acceptance?
Bibliographic record
Abstract
Acai is a palm widely distributed in the Amazon estuary floodplains. The edible pulp of acai fruits is commonly macerated with water to produce a thick, purple beverage of creamy texture, oily appearance, and characteristic flavor. Depending on the Brazilian region, the acai based-product is prepared adding sugar and guarana extract, but their ideal proportions have never been determined in order to optimize consumers’ sensory acceptance. This research investigated these concentrations using response surface methodology (RSM) based on a five-level, two variable central composite rotatable design (CCRD). Dependent variable was consumer acceptance (flavor, texture and overall liking) and results were analyzed by multivariate regressions. Analyses of Variance (ANOVAs) showed significant models – F-test values (29.3 for flavor, for texture 37.8 and 30.4 for overall liking) higher than the critical value of 4.35 (d.f. = 3; p < 0.05; R2 of 0.926 for flavor, 0.942 for texture and 0.929 for overall liking). Acceptance models are presented (significant parameters). Results showed that guarana extract has a stronger influence (negative) on acceptance compared to sugar (positive), both not on optimal conditions yet. Therefore, more studies are needed in order to optimize acai acceptance.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".