Handheld Electronic Nose (HEN) for Detection of Optimum Fermentation Time during Tea Manufacture and Assessment of Tea Quality
Bibliographic record
Abstract
Fermentation is an important process in tea manufacturing cycle and proper fermentation of tea leaves determines the quality of made tea. Both over and under fermentation affects the quality of made tea. To understand the optimum fermentation time and flavour of the processed tea, CDAC Kolkata had developed a hand-held electronic nose system (HEN). The system consists of top, mid and bottom section. Top section contains major portion of electronics, display, pump, sensor array and battery. The detachable mid-section holds the valve and provides an air path between pump and sample holder and between sample holder and sensor array. The bottom section is a threaded glass jar that acts as the sample holder. The HEN can be handy in determining the optimum fermentation end point during manufacturing process and to assess the quality of processed tea based on its aroma. Fixed quantity (half of the sample holder) of sample was taken from fermentation bed at an interval of five minutes and the generated aroma volatiles were discharged to the sensor array. The captured aroma was plotted against time to get the second aroma peak, which determined the optimum end point of fermentation. This was compared with the existing chemical method. The results obtained through HEN were correlated with the existing chemical method. The processed teas consisting CTC and orthodox types of manufacture and different grades were evaluated through HEN and the results were compared with organoleptic evaluation of tea by national and international tea tasters. The results revealed that the Handheld Electronic Nose is a suitable instrument for determining the optimum fermentation time during tea manufacture based on aroma.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".