In Vivo Solid-Phase Microextraction with in Vitro Calibration: Determination of Off-Flavor Components in Live Fish
Bibliographic record
Abstract
The presence of off-flavor compounds in fish represents a significant economic problem encountered in aquaculture production. The off-flavor compounds are due to the absorption of substances produced by a range of microorganisms. Currently, a number of strategies have been used to prevent or limit the growth of these microorganisms. Therefore, it is important to evaluate the effectiveness of strategies via monitoring the concentrations of off-flavor compounds in the recirculating aquaculture system. In vivo solid-phase microextraction (SPME), a rapid and simple sample preparation method, will allow monitoring the concentration of off-flavor compounds in live fish. In this research, geosmin and 2-methylisoborneol (2-MIB) produced by cyanobacteria and actinomycetes, which are the major sources for "earthy" and "muddy" flavors in fish, were selected as representatives. In order to accurately quantify these compounds in fish muscle, two kinetic calibration methods, on-fiber standardization and measurement using predetermined sampling rate, were used as quantification methods, which were both validated by traditional methods. The detection limit of in vivo SPME in fish muscle was 0.12 ng/g for geosmin and 0.21 ng/g for 2-MIB, which are both below the human sensory thresholds.
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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 | 0.001 | 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 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".