Nutritional composition and antioxidant compounds of coconut candy with added açaí pulp
Bibliographic record
Abstract
The açaí (Euterpe oleracea Mart.) is an Amazonian fruit that is widely consumed in Brazil and around the world, particularly due to its large number of bioactive compounds. In order to verify the bioactive characteristics of açaí in popular consumer products, the aim of this study was to test the increasing function of açaí pulp (bioactive compounds and antioxidant activity) in the production of coconut candy. The treatments consisted of four formulations of coconut candy (C): with no açaí pulp (CA0), with 10% açaí pulp (CA10), with 20% açaí pulp (CA20) and with 30% açaí pulp (CA30), in addition to the sugar, grated coconut, gum agar, citric acid and water that are part of the standard formulation of each treatment. The experimental design was completely randomised with three replications. The following analyses were carried out: total sugars, lipids, proteins, moisture, calorific value, phenolic compounds, anthocyanins, and antioxidant activity using the DPPH method. The increasing concentrations of açaí in the formulations significantly increased the total sugar and lipid concentrations, moisture, and calorific value. The anthocyanin concentration increased linearly with the increase in pulp concentration. The coconut candy from formulation CA20 showed the greatest antioxidant activity between the proposed formulations. The formulations had no effect on the levels of phenolic compounds. The anthocyanin content differed statistically between formulations, with the highest values seen in formulation CA30. Formulations CA20 and CA30 are sources of bioactive components.
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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) | 0.001 | 0.000 |
| 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".