Modeling study of coffee extraction at different temperature and grind size conditions to better understand the cold and hot brewing process
Bibliographic record
Abstract
Abstract In this study, the extraction kinetics, based on total dissolved solid (TDS), at different temperatures (4, 23, 50, and 93°C) for different grind sizes (VMD = 139, 643, 1,450, 1,747 μm) were investigated. Coffee extraction proceeded in initial fast extraction stage followed by a significantly slower extraction stage, which correspond to the extraction from surfaces of broken cells and the extraction from intact coffee cells, respectively. Diffusion inside the coffee particle is a very slow process, so breaking the cells is a very efficient way to increase the mass transfer rate. In addition, the ultimate extraction yield increased with increasing brewing temperature and decreasing of particle size. The Weibull distribution, pseudo‐first order and pseudo‐second order model were fitted to the kinetics data, with high coefficients of determination (0.687–0.998), and low root mean square error (0.02–0.26%). Meanwhile, exponential equations were created to correlate the derived rate constants (1/α, k1, and k2) with brewing temperature and particle size to achieve the prediction of brewing extent (TDSt/TDSeqm) at different temperature‐particle size combinations. Practical applications This study investigated the kinetics of coffee extraction at different grind size and temperature conditions with the purposes to better understanding the cold and hot coffee brewing process, as well as to predict the coffee extraction. The findings in this study will have practical applications in three folds: Help manufacturers of coffee brew products (ready to drink or concentrate) to better design and control their coffee extraction process. Aid manufacturers of coffee extraction equipment and coffee brewers to improve their products Provide reference information for coffee store, barista, coffee enthusiast, and consumers to brewing a better cup of coffee
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".