A Comparison of the Polyphenolic and Free Radical Scavenging Activity of Cold Brew versus Hot Brew Black Tea (Camellia Sinensis, Theaceae)
Bibliographic record
Abstract
Recently, a new trend called cold brewing gained popularity in the tea and coffee beverage industry. Cold brew and hot brew black tea may have different sensory qualities and antioxidant levels because of their polyphenolic properties and brewing processes. The objectives of this study were to determine antioxidant properties and polyphenolic content of commercial brands of cold brew and hot brew black tea. The total phenolic content of the cold brew tea was determined to be 0.19 mg/mL gallic acid equivalents/100 g and hot brew tea was 0.43 mg/mL gallic acid equivalents/100 g when assayed by Folin-Ciocalteu’s reagent method. The total flavonoid content of the cold brew tea was 0.40 mg/mL catechin equivalents/100 g and hot brew was 1.01 mg/mL catechin equivalents/100 g. Moreover, antioxidant capacity of cold brew and hot brew black tea was analyzed where their ability to scavenge DPPH radicals was 86.3% and 88.1% respectively. There was a significant difference in total phenolic content between hot brew and cold brew (p = 0.004). Similarly, there was a significant difference in total flavonoid between cold brew and hot brew (p = 0.004). Additionally, there was a significant difference in DPPH scavenging activity between cold brew and hot brew (p = 0.016). Overall, it can be concluded that although cold brew tea contained a lower amount of phenolics and flavonoids as compared to hot brew tea, they both were able to scavenge DPPH radicals in nearly same capacity.
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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) | 0.000 | 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) | 0.001 | 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".