Quality Attributes of Therapeutic Tea from Indian Herbs Sweetened with Stevia (Stevia rebaudiana)
Bibliographic record
Abstract
Eating healthy food is vital for wellness and prevention of disease. Teas are aqueous extractions of crude herbs and one of the most commonly used delivery system for natural health products. Stevia (Stevia rebaudiana) is a sweet herb having sweetness 200 to 300 times more than sugar with zero calorific value. Therapeutically stevia is antibacterial, antifungal, anti inflammatory, antimicrobial, antiviral, antiyeast, prevents cavities, cardio tonic, diuretic, hypoglycemic, hypotensive tonic and vasodilator. Hence, sensorily acceptable therapeutic tea sweetened with stevia was formulated, optimized and assessed for various quality parameters. Therapeutic tea was optimized at 10.25% stevia leaves, 7.28% nutmeg, 32.06% arjuna bark, 5.55% licorice and 6.41% of each of ginger, cinnamon, black pepper, fennel, nagarmotha and cardamom. It is recommended that 2.34 g of tea formulation is appropriate to make 100 ml of tea infusion. Herbal tea formulations contained 8.22-9.32% protein, 18.66-18.70% ash, 42.0-45.28% carbohydrates, 72.07-82.25 mg P, 268.25-271.62 mg Ca, 80.30-83.87 mg Mg, 12.8-13.65 mg Fe, 1.82-2.60 mg Cu, 1.37-1.57 mg Zn, 3.28-3.76 mg Mn and 15.84-19.80 mg ascorbic acid per 100 g of tea mix. Bright, sparkling and clear infusion of brown colour with pleasant aroma and taste was obtained from optimized therapeutic tea formulation which would be an alternative medicine for different therapeutic purposes with minimal calories. Microbial quality of the product packed in aluminium foil bag was well up to 3 months storage at ambient temperature.
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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 | 0.001 | 0.001 |
| 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".