Optimizing Ground Roasted Coconut Quality: Effects of Coconut Maturity and Drying Temperature Using Central Composite Design
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 (R² ) 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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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".