Effect of Natural Preservatives (Moringa Leaf and Ginger Root) on Nutrients and Shelf Life of Smoked African Catfish
Bibliographic record
Abstract
This research investigates the effectiveness of Moringa, Ginger, and their combination as bio-preservatives for smoked catfish. The study assessed these treatments’ impacts on proximate composition, biochemical and microbiological properties, and sensory attributes over a 12 week storage period. The combination of Moringa and Ginger (T4) significantly improved the nutritional quality of smoked catfish, achieving the lowest moisture content (4.84±0.13%) and the highest protein content (66.18±0.88%). This treatment also resulted in the lowest peroxide value (PV) (6.10±1.80 meq/kg), thiobarbituric acid reactive substances (TBAR) (1.64±0.47 mg malondialdehyde/kg), total volatile base nitrogen (TVB-N) (13.21±5.73 mg N/100g), and trimethylamine nitrogen (TMA-N) (3.61±2.95 mg N/100g), indicating reduced lipid oxidation and protein degradation. Microbiological analysis revealed the lowest total viable count (TVC) (0.71±0.82×105 CFU/g) and yeast and mould count (0.09±0.04×103 CFU/g), suggesting better microbial stability. These findings confirm that Moringa and Ginger, especially in combination, are effective natural preservatives for improving the quality and extending the shelf life of smoked catfish. Their leaves contain vital phytochemicals and have intriguing applications in the pharmaceutical, cosmetic, and food industries, and this is due to the many applications that can be found for their versatility, high levels of nutrition, and potential nutraceutical benefits. The study recommends adopting these bio-preservatives in fish processing, further research on their application across different fish species, and developing training programs for fish processors on their use.
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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".