Electronic Tongue based Classification of Mineral Water Samples using Gramian Angular Field and Deep SAE
Bibliographic record
Abstract
Time series imaging technique: Gramian Angular Field (GAF), and a deep stacked Autoencoder (SAE) are employed to develop a multi-class classifier for the classification and authentication of water samples of bottled water brands: Aquafina (AF), Bisleri (BS), Kingfisher (KF), Oasis (OS), Dolphin (DL) and McDowell (MD) that are attainable in Indian market. The electronic tongue is an artificial taste sensor that is used in the present wok to taste the mineral water samples and subsequently produce one-dimensional current waveforms (CWFs). GAF is used to transform the 1D CWFs into images that are used to train the deep SAE based classifier. The trained classifier is tested on the test GAF images belonging to the water samples of six unknown mineral water brands. Results show that the classifier exhibits satisfactory performance with high classification rate of 93.9%.
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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 | 0.000 | 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) | 0.000 | 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".