Bibliographic record
Abstract
The determination of wine authenticity and the detection of adulteration are attracting an increasing amount of attention for wine producers, researchers and consumers. Wine authentication and classification based on geographical origin has been widely studied. Most of these studies have achieved successful classification results. However, these studies have involved complicated and expensive procedures. Visible and near infrared spectroscopy (Vis-NIR) is recognized as a rapid and non-destructive technique. In recent years, several studies have been conducted using Vis-NIR spectroscopy to analyze wine for both quantitative and qualitative purposes. The aim of this research was to investigate the geographical classification of wines using Vis-NIR spectroscopy. The effect of temperature and measurement mode (transmission and transflectance) on Vis-NIR spectra was investigated to identify optimal conditions for wine sample analysis. It was found the optimal temperature is between 30 to 35°C and the shorter pathlength measurement condition has better prediction ability. Classification by geographical origin using Vis-NIR spectroscopy was investigated for sixty-three Tempranillo wines from Spain and Australia, and fifty Riesling wines from Australia, New Zealand and Europe. Discriminant partial least square regression (DPLS) and linear discriminant analysis (LDA) based on PCA scores were used to perform classification. Over 90% of the Tempranillo wines were correctly classified according to their geographical region using both DPLS and LDA. A classification rate of 72% was achieved for the Riesling wines. Vis-NIR technique provides a similar degree of reliability on wine classification comparable to those obtained using chemical composition. The results of this study demonstrate potential for Vis-NIR spectroscopy combined with multivariate analysis as a rapid method for classifying wines by geographical origin.
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".