Investigation of the Potential Utility of Single-Bounce Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy in the Analysis of Distilled Liquors and Wines
Bibliographic record
Abstract
A new Fourier transform infrared (FTIR) spectroscopic method based on single-bounce attenuated total reflectance (SB-ATR) spectroscopy was developed for the analysis of distilled liquors and wines. For distilled liquors, a partial least-squares (PLS) calibration was developed for alcohol determination based on the SB-ATR/FTIR spectra of mixtures of ethanol and distilled water. An independent set of 12 different distilled liquor samples was predicted from the PLS calibration, and a standard deviation of the differences for accuracy (SDD(a)) between actual and predicted values of 0.142% (v/v) was obtained. The potential utility of SB-ATR/FTIR spectroscopy for the analysis of wines was initially evaluated based on a comparison with Fourier transform near-infrared (FT-NIR) spectroscopy and FTIR spectroscopy using a flow-through transmission cell. PLS calibrations for alcohol, total reducing sugars, total acidity and pH were developed using pre-analyzed wine samples (n = 28), and for SB-ATR/FTIR spectroscopy, the SDD(a) for the leave-one-out cross-validation statistics were of the order of 0.100% (v/v), 0.707 g L(-1), 0.189 g L(-1) (H2SO4), and 0.230, respectively. Overall, the SB-ATR/FTIR results were better than those obtained using FT-NIR spectroscopy and comparable to those obtained with transmission FTIR spectroscopy. A PLS calibration based on preanalyzed wine samples (n = 72) for the prediction of 11 different components and parameters in wines by SB-ATR/FTIR spectroscopy was subsequently developed and validated using an independent sample set (n = 77). Good coefficients of correlation between the reference and predicted values for the validation set were obtained for most of the components and parameters except citric acid, volatile acids, and total SO2. The results of this study demonstrate the suitability of SB-ATR/FTIR spectroscopy for the routine analysis of distilled liquors and wines.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".