Grape and wine analysis - enhancing the power of spectroscopy with chemometrics..
Bibliographic record
Abstract
Spectroscopic techniques offer the potential to simplify and reduce analytical times for a range of grape and wine analytes. It is this aspect, together with the ability to simultaneously measure several analytes, which was the impetus for developing spectroscopic methods. The Australian Wine Research Institute (AWRI) has long used spectroscopic analysis of wines in the ultraviolet (UV) and visible (Vis) wavelengths, and since 1998 has been investigating applications of spectroscopic techniques in the near infrared (NIR) and mid-infrared (MIR) wavelength regions of the electromagnetic spectrum for the rapid analysis and quality control of both grapes and wine by the Australian wine industry. This paper reviews the use of several spectroscopic techniques, including NIR, MIR, and Vis, combined with chemometrics, to assess grape and wine composition in the Australian wine industry. The achievements, current research, and proposed further applications of different spectroscopic techniques are discussed in studies into the assessments of red grape composition and of fungal diseases in grapes, monitoring phenolic compounds during red wine fermentations, quality grading of red, white and fortified wine styles, monitoring wine distillation processes, and yeast strain classification.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".