86 Impact of steeping corn DDGS over 72 hours without or with fiber degrading enzymes and protease alone or in combination on concentration of sugars and organic acids and pH in the liquid medium
Bibliographic record
Abstract
Abstract We investigated the impact of steeping corn DDGS over 72 h with or without fiber degrading enzymes (FDE) and protease (PRO) on concentration of sugar, acetic and lactic acid, and pH. The concentration of crude fat, CP, NDF and ADF in DDGS sample was 8.6, 30.8, 36.1, 12.2% DM, respectively. Treatments were: 1) DDGS steeped without enzymes (Control), 2) DDGS steeped with FDE (FDE), 3) DDGS steeped with protease (PRO), 4) DDGS steeped with combination (FDEPRO). Enzymes were added at 1% of DDGS. Briefly, 50g of DDGS was mixed with 500-mL water with or without enzymes in sterile plastic bottles and steeped for 0, 12, 24, 48 and 72-h at 37℃ with continuous agitation. Samples were aliquoted to individual bottle for each timepoint. At each time point, bottles were pulled out from the incubator for pH measurements and liquid medium supernatant for sugars and acids. Highest arabinose, xylose and glucose was observed at 12 and 24 h, followed by decrease at 48 and 72 h. At 12 h, arabinose was higher (P < 0.05) for FDE and FDEPRO than control. The highest xylose and glucose was seen at 12 h for PRO, FDE and FDEPRO while control was highest at 24 h. Acetic acid and lactic acid increased over time. At 72 h, FEDPRO had highest acetic acid compared to control and PRO, while lactic acid was highest for FDE and FDPRO compared with (P < 0.0001) control and PRO. The pH decreased (P < 0.05) over time for all treatments. At 72 h, FDE had lowest pH followed by FDEPRO, PRO and control (P < .0001). Overall, arabinose, xylose and glucose increased to a certain timepoint and decreased, while lactic and acetic acid increased over time, subsequently leading to lowest pH at 72 h. In conclusion, FDE increased concentration of sugars and acids in steeped DDGS whereas PRO had no effect.
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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) | 0.001 | 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 | 0.001 |
| 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".