Effect of Drying on Quality and Sensory Attributes of Lemongrass (Cymbopogon citratus) Tea
Bibliographic record
Abstract
The aim of this study was to investigate the effect of drying on quality and sensory attributes of lemon grass (Cymbopogon citratus) tea. Lemongrass (C. citratus) leaves were dried using four different drying methods: sun, solar, oven (40, 50, and 60°C), and microwave (50 W). Teas made from the grass were analyzed for colour, pH and sensory attribute. Data obtained were statistically analyzed using SPSS Version 23 one way analysis of variance and means were compared using Duncan multiple comparison test (p ˂ 0.05). Results obtained indicate that after drying the moisture content were significantly reduced. Ash content results showed no significant difference amongst lemongrass samples dried under difference drying methods. However, there was a significant difference (p < 0.05) in the pH of tea made from the lemongrass dried under different drying methods. Results indicate that drying temperature and time are the main factors affecting the colour of dried lemongrass leaves for tea. There was a significant difference (p < 0.05) in the colour profile of the dried leaves. Sensory evaluation results showed that the colour, aroma, taste, and overall acceptability scores of tea from lemongrass dried with oven at 40°C was highest. The study revealed that oven drying at 40°C for 15 hours was found to be most suitable for drying of lemongrass leaves for tea production in order to retain appreciable sensory attributes.
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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".