Antioxidant Capacity and Consumer Acceptability of Spiced Black Tea
Bibliographic record
Abstract
Value addition of bulk curl tear cut (CTC) black tea is important to meet emerging customer needs and address challenges in a competitive beverage market. Spicing of the tea is one way of value addition but little or no research has been done on the biochemical effect of blending tea with spices and consumer acceptability. A study was conducted to determine the effect of spices on consumer acceptability; pricing and anti-oxidant capacities of black CTC tea consumed in the Kenyan market. Six spices and a spice mix including; ginger, lemon grass, nutmeg, cinnamon, rosemary and tea <em>masala</em> were used to develop aerated spice tea. The spice mix-tea <em>masala </em>comprised of ginger, cinnamon, cardamoms, cloves, black pepper and nutmeg. The threshold levels of spice-tea blends for commercial purposes were demonstrated using three highly rated spices i.e. cinnamon, lemon grass and ginger. Economic costing was done using the cinnamon spiced-tea. The results showed that black tea had the<em> highest antioxidant</em> activity of 92.66% against that of the highest spice cinnamon 89.89%. Antioxidant activity of spiced tea significantly (P&lt;=0.05) decreased with the quantity of added spice. Spices significantly (P&lt;=0.05) increased consumer preference of the black tea and the preferred spice-mix ratios also differed. Some spices were preferred more than others as shown by the three best rated spice-tea mixes including; cinnamon at 10% lemon grass at 5% and ginger at 15% which had mean scores of 6.74, 6.35 and 6.58 respectively on a hedonic scale.
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.
How this classification was reachedexpand
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 | 0.004 | 0.002 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".