Elaboration of Blends of Pitaya Pulps With Acerola
Bibliographic record
Abstract
Pitaya and acerola are fruits rich in nutrients and can be used in blends formulation in order to improve the sensory characteristics of both pulps in isolation and complement each other in terms of nutritional aspects. Thus, the aim of this research was to develop different blends of pitaya pulp with acerola and choose the best formulation based on physical-chemical and colorimetric characteristics. Three blends formulations were prepared: F1-90% pitaya and 10% acerola; F2-70% pitaya and 30% acerola; and F3-50% pitaya and 50% acerola. The formulations were evaluated for physical-chemical parameters of water activity, water content, ash, total soluble solids (SST), pH, total titratable acidity (ATT), SST/ATT ratio, ascorbic acid, proteins, lipids, sugars totals, reducers and non-reducers and colorimetric analysis. The obtained data were subjected to variance analysis (ANOVA) and to comparison between means by the Tukey test at 5% probability. The formulation F1 stood out when compared to the others. The parameters pH, soluble solids, ratio SS/ATT, ash, water content, water activity, proteins, sugars, luminosity and hue angle were the ones that gave the formulation F1 the best results. However, it is noteworthy that the formulation F3 presented a greater amount of ascorbic acid and higher values of a, b and chroma in the colorimetric analysis. The use of these fruits allows to obtain an innovative product with excellent nutritional and functional characteristics. The blend is a viable alternative for the use of perishable and seasonal fruits, adding greater economic value to the very promising product to the market.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".