Advancing Flavor Stability Improvements in Different Beer Types Using Novel Electron Paramagnetic Resonance Area and Forced Beer Aging Methods
Bibliographic record
Abstract
Three different electron paramagnetic resonance (EPR) metrics—lag time, T150, and area—were compared with sensory data from two forced beer aging methods. Three types of beers were evaluated, namely, 1) standard lager, 5% alcohol by volume (ABV) and 3.30° standard reference method (SRM) color; 2) low-alcohol lager, 3% ABV and 2.20° SRM color; and 3) standard ale, 5% ABV and 4.00° SRM color. The two accelerated aging time–temperature treatments evaluated in this study were 18 days at 27°C and six days at 40°C, both being equivalent sensory-wise to our standard flavor stability staling rate of six months at 20°C. A significant relationship between EPR area and sensory staleness scores was found using the 40°C for six days temperature abuse treatment protocol (R2 = 0.9209–0.9567), not the 27°C for 18 days abuse treatment protocol (R2 = 0.0319–0.2046). In addition, the area under the curve metric displayed significant correlation coefficients (R2 = 0.9209–0.9567) with the 40°C for six days abuse treatment staling scores, whereas the traditional lag time and T150 values were not significant (R2 = 0.0174–0.2238 and 0.1010–0.8565, respectively). Using this analytical and sensory chemoetricstyle approach, subsequent brewing materials and process changes were made in the brewing process to lower the EPR area values in five breweries spanning three years of profiling. This resulted in improved packaged beer flavor stability. Using the connection between these novel, rapid methods, this study also highlights three causative beer conditions that have traditionally shown major impacts on flavor stability: namely, trace metals (Cu and Fe), dissolved oxygen, and bisulfite (SO2) levels. Using the EPR area metric, which represents the total amount of free radicals generated during the beer sample analysis time, led the way to engage a rapid forced beer sensory method. Once employed, longer packaged beer shelf-life improvements over the three-year period were realized. This new dynamic program is currently being used in our breweries.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".