Insight into how fermentation might contribute to the distinctiveness of Australian coffee
Bibliographic record
Abstract
With a view to modulating the flavour profiles of Australian coffee, this investigation focused on three estates in New South Wales. Coffee cherries were processed into beans with wet fermented and non-fermented methods to evaluate the effects of fermentation and terroir on microbial population dynamics, volatile composition, and sensory properties. Thirty-three volatiles were quantified in green and roasted coffee beans – 12 esters, 9 alcohols, 6 acids, 3 monoterpenes, 2 norisoprenoids, 1 aldehyde – and 5 thiols were quantified in roasted coffee brews. Sensory descriptive analysis defined appearance, aroma, and flavour attributes to describe the coffee brews. Fermented coffees were characterised by increased intensity of ‘black tea leaves’ and ‘dark chocolate’ aromas and ‘burnt toast’ flavour. Results suggested that wet fermentation of Australian coffee cherries could enhance the content of some volatile compounds known to convey “floral” and “fruity” aromas commonly ascribed to premium coffees from traditional producing regions. • Uninoculated fermentation trialled on Australian coffee cherries from three estates. • Major volatiles determined for treatments and thiols in coffee brews were quantified. • Quantitative sensory profiling of coffee brews was undertaken with a trained panel. • Sensory profiles were linked with volatile compound data using PLS regression. • Microbial populations monitored during fermentation could contribute to variation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".