Food Extrusion Technology: Initiatives to Address Food and Nutrition Insecurity in South Africa
Bibliographic record
Abstract
The use of extrusion can be regarded as beneficial due to its short production time and wide variety of foods produced by this method. South Africa as a developing country has been involved in food extrusion since the 1980’s and this technology is gaining momentum in academic research areas. A number of research efforts related to extrusion in South Africa have shown the consumption of extruded dry beans can reduce plasminogen activator inhibitor levels in hyperlipidaemic men; the production of sorghum-cowpea extruded instant porridge resulted in a nutritional acceptable product and can be used to supplement the diet of young children to assist with protein deficiencies. Furthermore, research has proven extruder parameters play a role in the outcome of the product and can influence product properties. Based on these research initiatives, Vaal University of Technology/Centre of Sustainable Livelihoods (VUT/CSL) has acquired an Extrusion Pilot Plant to implement interdisciplinary research of nutrition and engineering science. The research will look at process optimisation studies to obtain maximum product output and evaluating nutritional compositions of the products under various conditions. It is hoped the future research efforts at VUT/CSL will address food and nutrition insecurity and showcase the pilot plant as a testing facility and potential advancement to commercialisation.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".