Chemical and Physical Characterization of Peanut Powder Extracts
Why this work is in the frame
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Bibliographic record
Abstract
The production of lyophilized foods is a market with great growth potential, for providing important preservation characteristics, such as stability at ambient temperature, versatility of the product and preservation of the chemical compounds. Given the functional effects of peanut powder extracts, this study aimed to quantify the bioactive compounds and determine physical and chemical characteristics, comparing samples with and without skin. After obtaining the aqueous peanut extract the samples were frozen at -18 °C for 24 h. The formulated extracts were dried in a benchtop lyophilizer operating at temperature of -55 °C for a period of 48 hours. The powder extracts were disintegrated in a multiprocessor for 30 seconds and the samples were physically and chemically evaluated. The powder extracts were classified as non-hygroscopic, exhibiting poor fluidity and intermediate cohesiveness in samples with skin, and high cohesiveness in samples without skin. The powders showed agglomerated particles, with irregular and non-uniform shape. Potassium was the mineral found in largest amounts, as well as oleic and linoleic fatty acids. The particles of the powders exhibit a spherical shape, showing the presence of amorphous surfaces, in which there is no repetition of geometric forms. The peanut powder extracts are classified as non-hygroscopic, have poor fluidity, intermediate cohesiveness in samples with skin and high cohesiveness in samples without skin.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 it