Synthesis of fruity ethyl esters by acyl coenzyme A: alcohol acyltransferase and reverse esterase activities in<i>Oenococcus oeni</i>and<i>Lactobacillus plantarum</i>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AIMS: To assess the abilities of commercial wine lactic acid bacteria (LAB) to synthesize potentially flavour active fatty acid ethyl esters and determine mechanisms involved in their production. METHODS AND RESULTS: Oenococcus oeni AWRI B551 produced significant levels of ethyl hexanoate and ethyl octanoate following growth in an ethanolic test medium, and ester formation generally increased with increasing pH (4.5 > 3.5), anaerobiosis and precursor supplementation. Cell-free extracts of commercial O. oeni strains and Lactobacillus plantarum AWRI B740 were also tested for ester-synthesizing capabilities in a phosphate buffer via: (i) acyl coenzyme A: alcohol acyltransferase (AcoAAAT) activity and (ii) reverse esterase activity. For both ester-synthesizing activities, strain-dependent variation was observed, with AcoAAAT activity generally greater than reverse esterase. Reverse esterase in O. oeni AWRI B551 also esterified 1-propanol to produce propyl octanoate, and deuterated substrates ([(2)H(6)]ethanol and [(2)H(15)]octanoic acid) to produce the fully deuterated ester, [(2)H(5)]ethyl [(2)H(15)]octanoate. CONCLUSIONS: Wine LAB exhibit ethyl ester-synthesizing capability and possess two different ester-synthesizing activities, one of which is associated with an acyl coenzyme A: alcohol acyltransferase. SIGNIFICANCE AND IMPACT OF THE STUDY: This study demonstrates that wine LAB exhibit enzyme activities that can augment the ethyl ester content of wine. This knowledge will facilitate greater control over the impacts of malolactic fermentation on the fruity sensory properties and quality of wine.
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.
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 it