Bibliographic record
Abstract
Food waste is a major environmental concern, often ending up in landfills or incinerators, contributing to greenhouse gas emissions and the loss of valuable organic matter. Conventional treatment methods like composting or anaerobic digestion offer limited resource recovery, particularly in colder climates where energy demands for heating remain high. As the demand for sustainable and climate-adaptable solutions grows, volatile fatty acids (VFAs) have emerged as valuable intermediates for bio-based products such as bioplastics and biofuels This research explores the microbial production of VFAs from food waste under psychrophilic conditions (≤20 °C), presenting a low-energy alternative aligned with cold-climate needs. Compared to traditional mesophilic systems, fermentation at 17 °C resulted in slower hydrolysis but showed a distinct shift in the VFA profile, with enhanced butyric acid accumulation. Microbial community analysis revealed the dominance of psychrotolerant genera such as Solibacillus, Sporosarcina, and Paenibacillus, which supported butyrate-producing Clostridium species. These findings highlight the potential for pathway-specific adaptation at low temperatures. To improve process efficiency, substrate solubilization was enhanced using thermal-alkaline pretreatment and rhamnolipid biosurfactants, which led to a twofold increase in VFA yield (up to 4.4 g/L). The addition of rhamnolipids not only improved lipid accessibility but also favored acidogenic microbial populations over lactic acid producers, promoting more efficient fermentation. Further targeted butyric acid was enhanced through bioaugmentation with Clostridium butyricum, a known butyrate producer. Its introduction significantly increased butyric acid concentration by sevenfold (reaching 1.4 g/L), validating the approach of targeted microbial steering even under low-temperature conditions. Overall, this study demonstrates the feasibility of psychrophilic fermentation as a sustainable platform for producing VFAs from food waste. By integrating pretreatment, microbial community insights, and bioaugmentation, the research offers a practical framework for resource recovery in cold regions, advancing circular bioeconomy goals while addressing food waste challenges.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".