Effect of Drying Methods on Physicochemical Properties of Hot Pepper
Bibliographic record
Abstract
Improper drying of hot pepper after harvest is a prominent cause of post-harvest losses.In Ethiopia, hot pepper is mostly dried using the sun drying method before being processed into a powder.However, it's physicochemical qualities and application of other drying techniques were not well studied.Therefore, this study was aimed to evaluate the physicochemical properties of two hot pepper varieties dried under sun and oven drying methods.The experiment was arranged using factorial design and conducted by drying two hot pepper varieties under sun at a maximum temperature of 28.5 C for 96 hours and oven drying at a temperature of 80 C for 7 hours.The determined physicochemical properties were moisture content, pH, total soluble solids (TSS), oleoresin content, total carotenoids, browning index, extractable and surface color.The result showed that the highest moisture content (11.33%) was recorded for sun dried Marako fana hot pepper whereas the lowest moisture content (3.17%) was noted for Gababa hot-pepper dried under oven drying method.The highest pH (5.92), bulk density (0.52 g/cm 3 ), extractable color (228.62) and surface color (22.94) were noted for sun-dried Marako fana.In addition, the highest total soluble solids (3.83 o Brix) and yellowness (29.02) were recorded for oven dried Marako fana hot pepper.However, the highest oleoresin (9.94%) and redness (a* value) (20.79) were noted for oven dried Gababa hot pepper.The present finding also revealed that Statistically, non-significant (p0.05)differences were observed for luminosity (L* value) and carotenoids of dried hot pepper under sun and oven drying methods.Some physicochemical properties of hot pepper powder were improved by sun and oven drying methods, and further study is needed to optimize these drying methods for the production of hot pepper powder.
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How this classification was reachedexpand
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 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".