Effect of Dehydration Methods on Okra Chemical and Physical Composition
Bibliographic record
Abstract
The agricultural processing industry is continually working to meet consumer demand for new products, diversifying the supply of non-perishable items ready for consumption, conveniently prepared to maintain the main characteristics of the raw material. The objective of this work was to dehydrate whole okra by lyophilization and convective drying at 50, 60, 70 and 80 °C and to evaluate the influence of drying processes on the chemical and physical quality of powdered products. The powders had acceptable contents of lipids, sugars, proteins, pectin, chlorophyll and carotenoids, high hygroscopicity and low solubility. Lyophilization produced powders with characteristics closer to those of the fresh raw material. Regarding the contents of ashes, pectin, lipids and chlorophyll b, the samples obtained by convective drying showed characteristics close and even superior to those of the lyophilized powder.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".