Hydrodynamic study of a mixture of West Indian Cherry Residue and Soybean Grains in a spouted bed
Bibliographic record
Abstract
Abstract West Indian cherry, widely known as acerola in Latin America, is a fruit rich in vitamin C and other bioactive compounds. In Brazil, the largest producer of acerola in the world, the processing of this fruit results in large amounts of waste or residues. A method that allows these residues to be reused is drying. However, acerola residue has low flowability in spouted beds due to its low density and high moisture content. Therefore, in this study, soybean was used as an auxiliary material to maintain the stability of the fluid dynamics and the characteristics of the food end product. Because this process involves a mixture of solids of different sizes, shapes and densities, particle segregation may occur. This article reports on a study of the fluid dynamics of the mixture of acerola residue and soybean in a spouted bed, operating with different mass fractions of residue and different static bed heights. Particle segregation was analysed, allowing for the quantification of the effect of the initial concentration of acerola residue on the degree of miscibility. The content of phenolic compounds, flavonoids and ascorbic acid, as well as the moisture and mixture indices at different drying times, were also quantified.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".