Use of Amazon Fruits Barks as Source of Nutrients
Bibliographic record
Abstract
The barks of fruits are usually discarded as organic waste; a valuable source of nutrients is obtained are used as a starting source in the preparation of functional foods. In this work, the physicochemical properties (pH, titrable acidity and soluble solids), mineral and bromatological analysis of nine Amazonian fruits were studied: abiu, acerola, araçá, bacupari, biribá, camu-camu, fruta-do-conde, araçá and taperebá. The most acidic values stand out for the different fruits, with the exception of the abiu bark (pH = 4.7). As for its nutritional contribution, it was the araçá barks that presented the highest energy value of 276.29 Kcal 100 g-1. Among the macrominerals, the potassium concentration stands out, being the highest concentration for the graviola bark, 521.04 mg 100 g-1 followed by magnesium, where the concentration in the biribá was 64.21 mg 100 g-1. On the other hand, the husks are rich in micronutrients, highlighting the concentration of zinc in the bark of araçá, 12.23 mg 100 g-1 and manganese in the bark of abiu, 6.84 mg 100 g-1. The Pearson correlation coefficient presented a highly significant correlation for Fe-Al (0.96), P-Fe (0.94) and Fe-Zn (0.89). O bligpot of principal components (PCA) explains 56% of the cases, being the minerals Mg, Na, Co, K, S and Ca highly associated for the graviola and bacuparí.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".