Reformulating partially hydrogenated vegetable oils to maximise health gains in India: is it feasible and will it meet consumer demand?
Bibliographic record
Abstract
BACKGROUND: The consumption of partially hydrogenated vegetable oils (PHVOs) high in trans fat is associated with an increased risk of cardiovascular disease and other non-communicable diseases. In response to high intakes of PHVOs, the Indian government has proposed regulation to set limits on the amount of trans fat permissible in PHVOs. Global recommendations are to replace PHVOs with polyunsaturated fatty acids (PUFAs) in order to optimise health benefits; however, little is known about the practicalities of implementation in low-income settings. The aim of this study was to examine the technical and economic feasibility of reducing trans fat in PHVOs and reformulating it using healthier fats. METHODS: Thirteen semi-structured interviews were conducted with manufacturers and technical experts of PHVOs in India. Data were open-coded and organised according to key themes. RESULTS: Interviewees indicated that reformulating PHVOs was both economically and technically feasible provided that trans fat regulation takes account of the food technology challenges associated with product reformulation. However, there will be challenges in maintaining the physical properties that consumers prefer while reducing the trans fat in PHVOs. The availability of input oils was not seen to be a problem because of the low cost and high availability of imported palm oil, which was the input oil of choice for industry. Most interviewees were not concerned about the potential increase in saturated fat associated with increased use of palm oil and were not planning to use PUFAs in product reformulation. Interviewees indicated that many smaller manufacturers would not have sufficient capacity to reformulate products to reduce trans fat. CONCLUSIONS: Reformulating PHVOs to reduce trans fat in India is feasible; however, a collision course exists where the public health goal to replace PHVOs with PUFA are opposed to the goals of industry to produce a cheap alternative product that meets consumer preferences. Ensuring that product reformulation is done in a way that maximises health benefits will require shifts in knowledge and subsequent demand of products, decreased reliance on palm oil, investment in research and development and increased capacity for smaller manufacturers.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".