Processing of fish products in Zambia for sustainable food systems and nutritious food for the first 1,000 days of life
Bibliographic record
Abstract
Background/objectives: Fish is a highly perishable food, and in the absence of cold chain technologies, is often preserved using traditional technologies in rural fishing communities of Zambia. High fish losses contribute to food insecurity, malnutrition, and unsustainable food systems in Zambia. The objective of this research was to analyze the nutrient profiles of indigenous fish species and fish products processed using both traditional methods and modern techniques aimed at enhancing food quality, minimizing fish loss and waste, and increasing efficiency. The ultimate goal is to contribute to more environmentally and economically sustainable fish supply chains. We analyzed the proximate, vitamin, mineral and fatty acid composition of ten fish products from inland capture fisheries. Methods: Samples of ten fish products from inland capture fisheries in Zambia were collected and analyzed for their nutrient content. The potential contribution of each species to recommended nutrient intakes (RNIs) for pregnant and lactating women (PLW) and infants was calculated. Results: Iron content of fish products ranged from 0.31 to 2.49 mg/100 g, zinc from 0.2 to 1.13 mg/100 g, and calcium from 151.15 to 636.28 mg/100 g. Docosahexaenoic acid (DHA) was high in smoked fish and smoked fish powder processed using improved smoking kilns, which contributed 15-20% of daily DHA requirements for PLW and infants, based on a 25g or 10g serving (respectively). The ten products analyzed contributed 6-33% of daily requirements of DHA for both PLW and infants. Improved techniques for smoking fish resulted in greater content of Vitamins A and E, compared to traditional methods. Conclusion: This article provides evidence of the nutrient content of various fish products processed using traditional and improved technologies which are important for sustaining fish consumption and contributing to food security and nutrition in Zambia in the first 1,000 days of life.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".