Shelf-life Prediction Modeling and Physicochemical Changes of Canned African Giant Snail (Achachatina achatina) Based Products during Storage Using Sensory and Kinetic Data
Bibliographic record
Abstract
Canned African giant snails (brine, egusi and sauce) of low acidity (pH 4.5) were produced using Thermal Death Time (TDT) techniques. Sixty cans of each product were stored for 6 months at normal temperature (30oC). The physicochemical and sensory properties of these products were evaluated monthly. And also, 28 cans of each product were equally evaluated at accelerated storage temperatures stress (50, 60, 70 & 80oC) for 6 weeks. Changes in pH and overall acceptability were described by first and second order kinetics respectively. The extent of proteolysis, peroxidase activities, and extent of fat hydrolysis i.e peroxide and thiobabituric acid values did not change significantly (P > 0.05) during storage. Regression analysis was used to fit models for Gibbs free energy of activation for physicochemical changes as a function of temperature and to predict models for overall acceptability as a function of pH. Shelf-life prediction models were fitted based on sensory and pH kinetic data. Shelf-lives of 27.6 months, 27-25 months and 18-16.8 months were estimated for snail in brine (SIB) snail in egusi (SES) and snail in sauce (SIS) respectively during storage at 30OC.
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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 | 0.001 |
| 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 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".