Mathematical modeling of coffee beans drying: addressing challenges of determining moisture ratio under natural conditions
Bibliographic record
Abstract
Coffee bean drying is a critical step in post-harvest processing because it directly affects the quality of the final product. The moisture ratio (MR) during drying is a key parameter that must be carefully controlled to prevent over- or under-drying, both of which can degrade coffee quality. In this study, we investigate fourteen mathematical models existing in the literature that describe the MR during the drying of coffee beans. Each model is evaluated in terms of its formulation, applicability, and relevance to coffee drying. Furthermore, we numerically simulate each model using experimental drying data collected from Génova, Quindío (Colombia), under natural conditions, a region renowned for its high-quality coffee production. The simulations provide a comparison of the accuracy and predictive capability of the models under real-world environmental conditions, highlighting the need for more sophisticated models that integrate humidity, temperature, and real-time weather data to improve the accuracy and efficiency of the drying process. Key words: Experimental data; natural drying; model evaluation; model validation; weather; integration.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".