Bibliographic record
Abstract
This study analyzes flavor ingredients according to types of whisky and maturation periods based on total 40 different types of whisky that are mainly distributed to Korea via imports. Whisky was classified into four categories based on origin, and also into different categories based on maturity period, ingredients such as fusel alcohol, fatty acid, and fatty acid esters and proportion of patterns were analyzed. As a result of an analysis for fusel alcohol, high qualified types of alcohol including 3-methylbutanol, 2-methylbutanol, iso-butanol, and 1-propanol were detected from all Scotch whiskys, America whiskys, and Canadian whiskys. In particular, the proportional sum of 3-methylbutanol and 2-methylbutanol, and the sum of 3-methylbutanol and 2-methylbutanol/iso-butanol were regarded as core factors to determine each type of whisky. Acetaldehyde, ethyl acetate, and acetic acid increased as maturation and storage period became longer. As a result of the fatty acid and fatty acid ethyl ester analysis, the major fatty acids were dodecanoic acid and decanoic acid, both with detection of octanoic acid and hexanoic acid. However, dodecanoic acid, decanoic acid, and octanoic acid were lower than the detectable limit in American and Canadian whiskys, showing a unique phenomenon that hexanoic acid was detected only in very small amounts. Malt Scotch whisky showed higher significance than blended Scotch whisky, making it possible to classify types of whisky. Fatty acid ethyl ester contents showed significance with fatty acid either. In addition, changes in the whiskys based on maturation period were confirmed via proportions of fatty acids and fatty acid ethyl esters. In general, the proportion of fatty acids and fatty acid ethyl esters decreased as the storage period increased. This study provided basic data to classify types of whisky based on maturation periods by analyzing the proportion of flavor ingredients such as fusel alcohols, fatty acids, and fatty acid ethyl esters.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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; both teacher heads agree on what is shown here.
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".