The electronic nose as a tool for the classification of fruit and grape wines from different Ontario wineries
Bibliographic record
Abstract
Abstract Electronic nose technology is useful for classifying or ‘fingerprinting’ foods and beverages based on odour profiles. With a view to providing useful information on quality attributes, the Fox 3000 electronic nose (EN) was tested for the ability to characterize Ontario‐produced fruit wines. Eight fruit wines (blueberry, cherry, raspberry, blackcurrant, elderberry, cranberry, apple and peach) and four grape wines (red, Chardonnay, Riesling and ice wine) were each obtained from a minimum of five Ontario wineries. Replicates of each wine sample were dried onto membrane filters to remove ethanol, and analyzed by the EN. It was possible to separate completely each wine variety (eg blueberry) based on differences between wineries; however, when all wine data were pooled, classification by variety was poor (58.7% correctly classified). Analysis of different wine varieties from a single winery revealed some misclassification. Wines could be separated into four distinct groups based on position on the discriminant function analysis map (79.9% correct). Fruit and grape wines were well separated from each other (75.9% correct), as were red and white wines (92.2% correct). The results show that the EN can discriminate fruit and grape wines into natural and useful groupings and may become an important tool for standardization of wine quality. Copyright © 2005 Society of Chemical Industry
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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 | 0.001 | 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 | 0.000 | 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".