Measurement of Condensed Tannins and Dry Matter in Red Grape Homogenates Using Near Infrared Spectroscopy and Partial Least Squares
Bibliographic record
Abstract
Samples (n = 620) of homogenized red grape berries were analyzed using a visible and near-infrared (NIR) spectrophotometer (400-2500 nm) in reflectance. The spectra and the analytical data were used to develop partial least-squares calibrations to predict dry matter (DM) content and condensed tannins (CT) concentrations. The coefficient of determination in cross-validation and the standard error of cross-validation were 0.92 and 0.83% w/w for DM and 0.86 and 0.46 mg/g epicatechin equivalents for CT, respectively. The standard error in prediction was 1.34% w/w for DM and 0.89 mg/g epicatechin equivalents for CT, respectively. By implementing a NIR spectroscopy method to measure DM and CT in red grape homogenates, we have developed an approach that is suited to large-scale compositional analysis in commercial wine production facilities, as it enables the analysis of large numbers of samples needed to stream batches of fruit. From an economical point of view, the calibration models could be achieved with relatively small data sets. Thus, NIR offers a suitable and efficient tool for the simultaneous measurement of DM and CT in addition to other important parameters in red grape homogenates such as total anthocyanins, total soluble solids, and pH, with minimal sample preparation and low cost.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".