The Influence of Malt Variety and Origin on Wort Flavor
Bibliographic record
Abstract
Beer flavor is primarily impacted by malt kilning and the choice of yeast/hops in the beer recipe. Although barley malt is the material backbone of most beers, variety has until recently been largely overlooked with respect to flavor differences. In this study, 11 malt variety samples from multiple Australian and international (UK, Canada, China) growing regions were infusion mashed (65 °C) at laboratory scale to produce unboiled wort to investigate differences between the flavor profiles observed with sensory assessment and headspace-SPME gas chromatography-mass spectrometry (HS-SPME GC-MS). Sensory evaluation identified wort flavor differences with the control heritage samples, Maris Otter/Schooner, having the highest overall flavor complexity and acceptability. The Chinese malted Chinese/Canadian samples had the lowest overall flavor complexity rankings. Overall, flavor complexity was correlated with KI, malt protein (negative), and β-glucosidase (negative), while sweetness intensity was correlated with limit dextrinase and pH. HS-SPME GC-MS analysis focused only on compounds that were significantly different between varieties (ANOVA, P ≤ 0.05). Overall, 107 compounds were identified with significantly different levels between the varietal worts. The resultant PCA plots (overall, aldehydes, alcohols, esters, organic acids, terpenes, ketones) supported the sensory assessment, with Maris Otter and the Australian samples clustering in different PCA sectors compared to the Chinese malted Canadian/Chinese samples. These findings provide a basis for key compound identifications that influence malt flavor through the brewing process. The results have the potential to assist barley breeders in selecting optimized germplasm for future variety development and can assist maltsters and brewers to consistently target desired flavors for finished beers and potentially whisk(e)y. Supplemental data for this article is available online at https://doi.org/10.1080/03610470.2022.2041156 .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.059 | 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 teacher head, 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".