Modeling study of coffee extraction at different temperature and grind size conditions to better understand the cold and hot brewing process
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 , k 1 , and k 2 ) with brewing temperature and particle size to achieve the prediction of brewing extent (TDS t /TDS eqm ) 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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it