Economical Di-Rhamnolipids Biosynthesis by Non-Pathogenic Burkholderia thailandensis E264 Using Post-Consumption Food Waste in a Biorefinery Approach
Bibliographic record
Abstract
Rhamnolipids (RLs) are one of the most promising eco-friendly green alternatives to commercially viable fossil fuel-based surfactants. However, the current bioprocess practices cannot meet the required affordability, quantity, and biocompatibility within an industrially relevant framework. To circumvent these issues, our study aims to develop a sustainable biorefinery approach using post-consumption food waste as a second-generation feedstock. In-depth substrate screening revealed that food waste hydrolysate (FWH) was rich in readily assimilable carbohydrates, volatile fatty acids, and amino acids. The fermentative valorization of FWH as a sole carbon and energy source with Burkholderis thailandensis E264 in a bioreactor showed active RLs biosynthesis of up to 0.6–0.8 g/L (34–40 mg/g FWH) in a short duration (72 h). In terms of the kinetic parameters, the FWH-RLs outperformed other supplemented pure/waste streams. Interestingly, the recovered RLs had a long chain length, with Rha-Rha-C12-C14 being the predominant isoform and exhibiting a strong emulsification ability (E24, 54.6%). To the best of our knowledge, this study is the first to prove bioreactor-level RLs production and their abundance in food waste. Moreover, the feasibility of this developed process could propel next-generation biosurfactants, lower waste burdens, and increase the industrial applicability of RLs, thereby significantly contributing to the development of a circular bioeconomy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".