Development of a New Deodorization Method of Herring Milt Hydrolysate: Impacts of pH, Stirring with Nitrogen and Deaerator Treatment on the Odorous Content
Bibliographic record
Abstract
Herring milt hydrolysate (HMH) presents the disadvantage of being associated with an unpleasant smell limiting its use. Thus, to develop a new effective and easy-to-use deodorization method, this research aimed to deepen the knowledge regarding the impacts of pH (pH 7 vs. pH 10), overnight stirring with nitrogen (+N vs. −N) and deaerator treatment (+D vs. −D) on the odorous content of HMH. This latter included dimethylamine (DMA), trimethylamine (TMA), trimethylamine oxide (TMAO) and the most potent odor-active compounds of HMH. Results showed that pH had a huge impact on the targeted compounds resulting in higher detected concentrations of DMA, TMA and TMAO at pH 10 than at pH 7 (p < 0.05) while the opposite trend was observed for the most potent odor-active compounds of HMH (p < 0.05). Moreover, independently of the pH condition, the overnight stirring with or without nitrogen had no impact (p > 0.05). Finally, the deaerator treatment was more effective to remove TMA and DMA at pH 10 than at pH 7 (p < 0.05) while the opposite trend was observed for the most potent odor-active compounds (p < 0.05). Sensory analysis confirmed that the application of pH 10 −N +D and pH 7 −N +D + alkalization pH 10 conditions led to the least odorous products (p < 0.05).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".