Optimizing Ground Roasted Coconut Quality: Effects of Coconut Maturity and Drying Temperature Using Central Composite Design
Why this work is in the frame
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Bibliographic record
Abstract
Ground roasted coconut is a widely recognized traditional spice in Southeast Asia.Its quality is significantly affected by factors such as harvest maturity and post-harvest handling practices.However, systematic investigations into these variables remain limited.This study aimed to evaluate the effects of coconut maturity (9-13 months) and drying temperature (40-60) on the physicochemical properties of ground roasted coconut.A response surface methodology (RSM) employing a central composite design (CCD) was applied to develop a mathematical model describing the relationship between moisture content, free fatty acids (FFA), and fat content (FC) in relation to the treatment variables.The coefficients of determination (R ) were 0.927 for moisture content, 0.649 for FFA, and 0.50 for fat content.Coconut harvest maturity and drying temperature exerted a significant effect on moisture content, whereas FFA and fat content were not significantly influenced.Optimal processing conditions were identified at 10-11 months of harvest maturity and a drying temperature of 58-60, yielding ground roasted coconut with a moisture content of 0.8%, FFA content of 0.5%, and fat content of 64.47%.Under these optimised conditions, the colour parameters of ground roasted coconut were as follows: L* = 44.39,a* = 13.53,b* = 22.36, chroma = 26.13,and hue angle = 58.80.The resulting product also exhibited consistent colour, texture, and aroma characteristics comparable to those of commercially available ground roasted coconut, thereby confirming its suitability for market applications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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