Red tilapia by‐product hydrolysates: A new nitrogen source for <i>Bifidobacterium lactis</i><scp>HN019</scp>
Bibliographic record
Abstract
Abstract Background Red tilapia by‐products possess ample protein and are either discarded or processed into low‐value products. The nitrogen source is the most expensive part of the microbial culture, so finding a cheap alternative can better promote the microbial economy. In this study, different combinations of enzymes were used to hydrolyze the by‐products of red tilapia, and its effects on the growth of Bifidobacterium lactis HN019 were investigated. Results The results showed that hydrolysate hydrolyzed by enzyme combination 4 (alcalase: neutrase: papain: flavorzyme = 1:1:2:1) (EC4) obtained the highest nitrogen recovery (53.63%) and >2000 Da peptide proportion (5.07%). Hydrolysate hydrolyzed by enzyme combination 2 (alcalase: neutrase: papain: flavorzyme = 2:1:1:1) (EC2) has less hydrophobic amino acids and could improve the growth rate in 10 h–14 h in 50% nitrogen source substitution but had worst viable count after 24 h cultivation. Conclusion These results indicated that red tilapia by‐product hydrolysate was an excellent nitrogen source substitution and suggested that the hydrophobic amino acids in the nitrogen source might be an essential factor affecting the growth of Bifidobacterium lactis HN019. This research enhanced the economic value of red tilapia by‐products, minimized the waste of aquatic resources, and provided directions for the utilization of red tilapia by‐products.
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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) | 0.000 | 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 | 0.000 |
| 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".