Modeling quality changes in Pacific white shrimp ( <i>Litopenaeus vannamei</i> ) during storage: Comparison of the Arrhenius model and Random Forest model
Bibliographic record
Abstract
To investigate the quality changes of Pacific white shrimp stored at 4, −3, and −20°C, indicators including sensory assessment, pH, texture (hardness, springiness, gumminess, chewiness), thiobarbituric acid (TBA), total sulfhydryl content, Ca2+-ATPase activity, and total viable counts (TVC) were studied in this work. The Random Forest model was chosen to estimate the quality changes of these indicators in comparison with the Arrhenius model. During different temperatures, pH, TBA, and TVC increased with the extension of storage time, while the other indicators decreased. Compared with the Arrhenius model, the relative errors of quality indicators of the Random Forest model were below 10%, r2 was close to 1, and root mean square error was mostly below 0.1, which meant a better fitting property for these indicators. Thus, the Random Forest model with higher prediction accuracy is a hopeful method for predicting the changes in the quality of Pacific white shrimp. Practical applications The Random Forest model provides a more accurate and convenient model to predict the quality changes of Pacific white shrimp under the temperature range from −20°C to 4°C, which shows a potential use for shrimp preservation and processing. Random forest model cannot only be used for estimating soil calcium carbonate and other regression issues, but also assessing the shelf life by predicting the values of quality indicators of aquatic products during its storage.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".