Can Cold Brew Coffee Be Convenient? A Pilot Study For Caffeine Content in Cold Brew Coffee Concentrate Using High Performance Liquid Chromatography
Bibliographic record
Abstract
Cold brew coffee is a brewing method that is increasing in prevalence. While it has been anecdotally suggested that this method may provide a more aromatic and flavourful coffee product, there is little research published that looks at the concentration of caffeine or other coffee substituents in cold brew coffee. The potential alteration in chemical composition in cold brew provides a few interesting avenues for research. Can caffeine in cold brew be quantified by conventional methods? If so, how does the caffeine profile of cold brews relate to hot brew methods? Here we report the caffeine content and variability in small batch cold brew coffee and show that HPLC/UV-Vis, a standard method for quantitation of caffeine in other extraction methods, is useful for detection of caffeine in cold brew coffee. The mean concentration of caffeine in an average 355 mL serving was found to be 207.22 ± 39.17 mg over five distinct batches of cold brew coffee concentrate. Cold brew preparation methods produce similar quantities of caffeine as hot brew preparation, yet may have increased storage capabilities including improved retention of flavonoids and other secondary metabolites. Therefore, cold brew may provide utility in clinical trials examining caffeine and the effect of other components of coffee as it is commonly consumed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".