Optimization of L-methionine Bioconversion to Aroma-active Methionol by Kluyveromyces lactis Using the Taguchi Method
Bibliographic record
Abstract
<p>The bioproduction of methionol through fermentation was performed by <em>Kluyveromyces lactis</em> KL71 in coconut cream supplemented with L-methionine (Met). This bioprocess was successfully optimized with the Taguchi method applying the L<sub>27</sub> (3<sup>13</sup>) orthogonal array. Among these studied factors, shaking speed was found to be the most significant factor that affected the bioproduction of methionol, followed by incubation time, pH level and Met concentration. The optimum fermentation conditions were determined as follows: 0.45% (w/v) of Met, 48 h of incubation, shaking speed of 160 rpm, 0.05% (w/v) of yeast extract (YE), 0 mg/L of diammonium phosphate (DAP) and pH of 6.3. Under the optimum conditions, the signal to noise (S/N) ratio achieved was 59.9 decibels (dB), which was in good agreement with the predicted S/N ratio of 58.2 dB. The average yield of methionol obtained under the optimum fermentation conditions was 990.1 ± 49.7 µg/mL. This indicates that the Taguchi method was effective for the optimization of bioproduction of methionol by <em>K. lactis</em>.</p>
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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 | 0.001 | 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".