Potential for lycopene replacement of astaxanthin pigment in the diets of rainbow trout (Oncorhynchus mykiss)
Bibliographic record
Abstract
Salmonids cannot synthesize carotenoids 'de novo', and fillet colour is one of the most important factors influencing consumer acceptance. Therefore, fish produced by aquaculture rely on carotenoid pigments being added to their diet. The addition of synthetically produced carotenoids to salmonid diets is a common and expensive practice, adding 15-20% to the total feed costs. These factors create an opportunity for potential economic savings if alternative pigment sources can be developed. Tomato farming constitutes a large agricultural industry, especially in Ontario, where tomato processors produce millions of kilograms of waste tomato skins. The major carotenoid within tomatoes is lycopene, a ruby-red pigment now known for its nutraceutical properties. As part of these experiments, lycopene was extracted from tomato paste and incorporated into a salmonid diet. An analytical methodology for the extraction and analysis of lycopene in salmonid diets and flesh was developed using liquid, solid-phase-extraction and HPLC techniques. Feeding trials using rainbow trout were then undertaken to test the efficacy of uptake and retention of unmodified lycopene. No significant pigmentation or retention of either the flesh or internal organs was observed. Lycopene was then chemically modified to produce polar diols, in an attempt to improve the incorporation into salmonid tissue. An acid hydrolysis methodology was used to produce oxidized products of lycopene (2,6-Cyclolycopene-1,5-diol A and 2,6-Cyclolycopene-1,5-diol B). However, this did not improve either the uptake or retention of pigment within the carcass. (Abstract shortened by UMI.)
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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) | 0.000 | 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".