Extraction and physicochemical characteristics of high pressure-assisted cold brew coffee
Bibliographic record
Abstract
Over the past decade, cold brew coffee has gained increasing popularity due to its perceived smoother, sweeter, and less acidic sensory profile than the hot brew counterpart. However, the preparation of cold brew coffee is time-consuming, ranging from 6 to 24 h of extraction at refrigerated to room temperature. To address this challenge, the present study explored the feasibility of using high pressure processing (HPP) treatment to accelerate the extraction and evaluated its effects on physicochemical and sensory properties of the brew. In addition to preparing brews using the conventional coffee grounds, whole beans were also evaluated. Results from this study showed that HPP treatment could increase both extraction rate and extraction yield, especially for the whole beans, at ∼72 % and ∼36 % levels, respectively. At the same concentration, cold brew samples prepared from beans had lower polyphenol, caffeine and chlorogenic acid, but higher titratable acidity contents than brews from coffee grounds. These differences might have resulted in unique sensory profiles for bean-brewed coffee. In addition, when infused with nitrogen gas, the bean-brewed samples had a more stable and smoother foam head than the ground-brewed counterparts, implying that bean-brewed coffee may be promising for enhancing the nitrogen-infused coffee beverages.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".