Physicochemical Quality of Kernels from Terminalia catappa L. and Sensory Evaluation of the Concocted Kernels
Bibliographic record
Abstract
<p>This study focused on the physicochemical characterization of the kernels from <em>Terminalia catappa</em> L. and sensory evaluation of appetizers concocted from these kernels. The results of the physicochemical analyzes were as follow: ash (4.0±0.1%), proteins (40.9±1.3%), lipids (50.6±1.0%), total sugars (1.4±0.1%), reducing sugars (0.3±0.01%) and moisture content (3.8±0.4%). Acid and peroxide values were respectively 1.3±0.2% and 6.30±0.23 meq O<sub>2</sub>/kg oil. Two appetizers were concocted from the fresh kernels of <em>Terminalia catappa</em> L: Salted Roasted Kernels (SRK) and Unsalted Roasted Kernels (URK). A comparison of sensory profiles of both appetizers showed that they were not significantly different (p&lt;5%) for the parameters sweet and oily but different (p&lt;5%) for the parameters salty, bitter and firm. However, the tasters’ preference for the Salted Roasted Kernels was not significantly different from their preference for the Unsalted Roasted Kernels. The appetizers from <em>Terminalia catappa</em> L. (SRK and URK) were then compared to other appetizers readily available in markets and malls: Salted Roasted Peanuts (SRP), Unsalted Roasted Peanuts (URP), Unsalted Roasted Hazelnuts (URH) and Salted Roasted Cashew nuts (SRC). The preference order was: URH&lt;URK&lt;SRK&lt;URP&lt;SRC&lt;SRP.</p>
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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 | 0.001 |
| 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".