Physical, Chemical-Physical Characterization and Determination of Bioactives Compounds of the Pimtobeira Fruits (Talisia esculenta)
Bibliographic record
Abstract
Pitombeira fruits have characteristics that provide them with industrial and processed consumption, but they are barely studied, resulting in the need to obtain more information about the species’ potential and its utilization to various purposes. In face of these facts, a physical, chemical-physical and a determination of bioactive compounds post-harvest characterization of pitombeira fruits was done. The fruits were acquired in a street Market in the municipality of Sousa-PB, Brazil, and taken to the Food Analysis Laboratory of the Center of the Federal University of Campina Grande, in the municipality of Pombal-PB, Brazil. Fruits were selected by the absence of physical damage and diseases, as well as by their ripening stage and size, and refrigerated at 4 ºC. Gone 15 repetitions with 25 fruits, 20 fruits were destined to chemical-physical and determination of bioactive compounds analysis and the 5 remaining fruits to the physical analysis. Pitombeira fruits had ideal functional characteristics and necessary to the development and processing of new products, such as high protein content (31.72% in the seed and 39.72% in the skin), phenolic compounds (101.47% in the seed and 106.61% in the skin) and carotenoids (10.14% in the seed and 23.39% in the seed husk). In particular, Pitomba’s pulp can be used for in natura consumption as well as processed, since it has high contents of mineral residue, soluble solids and vitamin C. Pitomba fruits have excellent physical, chemical-physcial and bioactive compounds characteristics, as observed in the high contents of proteins, phenolic compounds, carotenoids and flavonoids in all parts of the fruit. With all these characteristics presented, products such as juices, beverages, bakery products and even food supplements can be made form the pitomba.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| 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".