Acidogenic Fermentation of Food Waste for the Production of Short-Chain Fatty Acids: The Impact of Inoculum Type and Inoculum Heat Pretreatment
Bibliographic record
Abstract
Acidogenic fermentation is an emerging biotechnology that allows for the utilization of food waste as a feedstock to produce high-value products such as short-chain fatty acids (SCFAs), effectively offering a tangible solution for food waste management as well as resource recovery. The objectives of the current study were to identify the ideal inoculum, waste-activated sludge (WAS) or anaerobic digester sludge (AD), for the acidogenic fermentation of food waste at room temperature, as well as to evaluate the impact of heat pretreatment of these inoculums on fermentation performance. The maximum hydrolysis yield of 399 g sCOD/kg VS added was obtained when untreated AD was used as the inoculum, whereas the pretreated AD inoculum provided the highest SCFA yield and conversion efficiency of 238 g sCODSCFA/kg VS added and 71%, respectively. Heat pretreatment had a detrimental impact on the WAS inoculum, leading to lower hydrolysis and SCFA yields, but exerted a positive influence on the AD inoculum. The microbial community showed that heat pretreatment negatively impacted the abundance of non-spore-forming hydrolytic and acidogenic microorganisms. Overall, this study demonstrates the critical role of inoculum type and heat pretreatment in optimizing the acidogenic fermentation process, laying the groundwork for future refinements in SCFA production from food waste through inoculum design.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.000 |
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".