Experimental Characterization of the Drying of Kampot Red Pepper (Piper nigrum L.)
Bibliographic record
Abstract
L.), which is produced in the Kampot Province, Cambodia. Indeed, even if the Kampot pepper is recognized for its organoleptic qualities, no research works were dedicated to the drying of its mature grains, in order to yield red pepper. Experiments with different pretreatment and drying conditions were performed. The results of these experiments were analyzed, regarding the drying kinetics, the color of the dry product, and the degradation of the bioactive compounds during the drying. Regarding these bioactive compounds, several parameters were considered: the total phenolic content, the total flavonoid content, and the piperine content. The results show that the Kampot mature pepper is prone to alterations when dried at a temperature of 55∘C or 65∘C: the color, the total phenolic content, and the flavonoid content are significantly altered, while the piperine content, important for the pungency of this spice, seems unaltered. Raising the temperature leads to more important degradations. However, performing a pretreatment by dipping the pepper grains into boiling water appears to significantly reduce these alterations and, concomitantly, to accelerate the drying. As a conclusion of the analysis of the results, it can be stated that, to increase the product quality, it is recommended to pretreat the pepper by dipping it into boiling water during 5 min., before drying at 55∘C.
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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) | 0.001 | 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) | 0.002 | 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".