RETRACTED ARTICLE: Next-generation -omics approaches to drive carboxylate production by acidogenic fermentation of food waste: a review
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
Acidogenic fermentation of food waste using mixed microbial cultures can produce carboxylates [or volatile fatty acids (VFA)] as high-valued bioproducts via a complex interplay of microorganisms during different stages of this process. However, the present fermentation systems are incapable of reaching the industrially relevant VFA production yields of ≥50 g/L primarly due to the complex process operation, competitive metabolic pathways, and limited understanding of microbial interplays. Recent reports have demonstrated the significant roles played by microbial communities from different phyla, which work together to control the process kinetics of various stages underlying acidogenic fermentation. In order to fully delineate the abundance, structure, and functionality of these microbial communities, next-generation high-throughput meta-omics technologies are required. In this article, we review the potential of metagenomics and metatranscriptomics approaches to enable microbial community engineering. Specifically, a deeper analysis of taxonomic relationships, shifts in microbial communities, and differences in the genetic expression of key pathway enzymes under varying operational and environmental parameters of acidogenic fermentation could lead to the identification of species-level functionalities for both cultivable and non-cultivable microbial fractions. Furthermore, it could also be used for successful gene sequence-guided microbial isolation and consortium development for bioaugmentation to allow VFA production with high concentrations and purity. Such highly controlled and engineered microbial systems could pave the way for tailored and high-yielding VFA synthesis, thereby creating a petrochemically competitive waste-to-value chain and promoting the circular bioeconomy.Research HighlightsMixed microbial mediated acidogenic fermentation of food waste.Metagenomics and metatranscriptomics based microbial community analysis.Omics derived function-associated microbial isolation and consortium engineering.High-valued sustainable carboxylate bio-products, i.e. volatile fatty acids.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.012 |
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".