Microbiome Evolution of Brewer’s Spent Grain and Spent Coffee Ground Solid Sidestreams Under Industrial Storage Conditions
Bibliographic record
Abstract
Brewer’s spent grain (BSG) and spent coffee ground (SCG) are solid sidestreams from beverage production increasingly being upcycled into food, feed and other value-added products. These solid sidestreams are prone to microbial spoilage, negatively impacting their upcycling potential. Three samples each of BSG and SCG were obtained from generators and recycling facilities in Singapore, and their chemical, elemental, and microbial composition was characterized. The spoilage mechanisms were investigated during storage under anaerobic and aerobic conditions. Bacterial loads of sidestreams were low from craft brewery and café sources (<1 and 3.53 ± 0.03 log10 CFU/g) and high from recycling facilities (>6 log10 CFU/g). The microbiome of BSG from recycling facilities was dominated by Bacilli, and B. coagulans was identified as the most prevalent species. SCG from recycling facilities was dominated by lactic acid bacteria, with L. panis being the most prevalent species. Storage up to 14 days under anaerobic conditions led to further bacterial proliferation mainly by Bacilli, lactic acid bacteria, and acetic acid bacteria, while aerobic storage led to extensive fungal contamination, including potential aflatoxin-producing Aspergillus flavus. The results shed light on the spoilage mechanisms, while highlighting the short shelf-life and food safety risks of BSG and SCG to inform valorization strategies.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".