Quality Index Method (QIM) for Gutted Ice-stored piauçu (Leporinus macrocephalus) and Determination of Shelf Life
Bibliographic record
Abstract
The Quality Index Method can be applied to different fish species with the main advantage of quantifying the freshness level of the fish without destroying a sample. The freshness level is decisive for the commercial shelf life of fish, therefore this study aimed to develop a Quality Index Method for gutted piauçu (Leporinus macrocephalus) stored in ice and estimate its shelf life according to sensory, physical-chemical and microbiological characteristics. Nine trained evaluators evaluated the gutted fish during 18 days of storage, using the Quality Index Method protocol. The samples were also characterized for mesophilic bacteria, psychrotrophic bacteria, hydrogenionic potential, total volatile bases and thiobarbituric acid reactive substances. Partial least squares regression was used to correlate the QIM attributes, and linear regression analysis was used to verify the variables as a function of storage time. The Quality Index Method scheme was developed based on 19 merit points, where zero indicates total freshness. The coefficient of determination for the linear regression between QI and storage time in ice was R2=0.78. Microbiological count results ranged from 4.43 to 15.59 log CFU/g for mesophilic bacteria and from 1.76 to 12.08 log CFU/g for psychrotrophic bacteria during ice storage. The results of the hydrogenionic potential, total volatile bases and thiobarbituric acid reactive substances ranged from 6.32 to 6.96; 10.57 to 13.30 mg N/100g; and 0.35 to 0.88 mg of malondialdehyde/kg, respectively. The integration of sensory, microbiological and physicochemical data allowed the shelf life of eviscerated piauçu stored on ice to be estimated at eight days.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".