Automated Beer Analysis by NMR Spectroscopy
Bibliographic record
Abstract
Previously, we reported on the development of MagMet, a tool capable of automatically processing and quantifying 1D 1 H NMR spectra of complex chemical mixtures, including biofluids such as human serum or plasma and, more recently, beverages such as wine. In this article, we present an extension of MagMet, called MagMet-B, for the automated profiling of 1D 1 H NMR spectra of beer. We curated a comprehensive 1D 1 H NMR spectral library comprising 81 more abundant metabolites commonly found in beer samples and optimized the MagMet algorithm to accurately fit these compounds. A comparison with manual profiling using the Chenomx NMR Suite (Version 8.3) showed a strong correlation between the manually measured and automated MagMet metabolite concentrations, with a mean absolute percent error of 13% and a median absolute percent error of 9%. Time-to-process comparisons show that MagMet-B is up to 45× faster than manual analysis. The MagMet-B Web server, which is specifically tailored for profiling beer NMR spectra at 700 MHz, is now accessible at https://magmet.ca .
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".