Thermal and alkaline pre-treatments of inoculum halt methanogenesis and enables cheese whey valorization by batch acidogenic fermentation
Bibliographic record
Abstract
Abstract Carboxylates like volatile fatty acids (VFAs) can be produced by acidogenic fermentation (AF) of dairy wastes like cheese whey, a massive residue produced at 160.67 million m 3 of which 42% are not valorized and impact the environment. In mixed-culture fermentations, selection pressures are needed to favor AF and halt methanogenesis. Inoculum pre-treatment was studied here as selective pressure for AF demineralized cheese whey in batch processes. Alkaline (NaOH, pH 8.0, 6 h) and thermal (90°C for 5 min, ice-bath until 23°C) pre-treatments, were tested together with batch operations run at initial pH 7.0 and 9.0, food-to-microorganism (F/M) ratios of 0.5 to 4.0 g COD g -1 VS, and under pressurized and non-pressurized headspace, in experiments duplicated in two institutes. Acetic acid was highly produced (1.36 and 1.40 g COD AcOH L -1 ) at the expense of methanogenesis by combining a thermal pre-treatment of inoculum with a non-pressurized batch operation started at pH 9.0. Microbial communities comprised of VFAs and alcohol producers, such as Clostridium , Fonticella , and Intestinimonas , and fermenters such as Longilinea and Leptolinea . Communities also presented the lipid-accumulating and bulk and foaming Candidatus Microthrix and the metanogenic Methanosaeta regardless of no methane production. An F/M ratio of 0.5 g COD g -1 VS led to the best VFA production of 1,769.38 mg L -1 . Overall, inoculum thermal pre-treatment, initial pH 9.0, and non-pressurized headspace acted as a selective pressure for halting methanogen and producing VFAs, valorizing cheese whey via batch acidogenic fermentation.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".