Evaluating Moringa Oleifera as a Nutritious and Acceptable Food Fortificant (P10-022-19)
Bibliographic record
Abstract
Moringa oleifera is an edible tropical plant with the potential to alleviate micronutrient deficiencies in low and middle income nations. This study addresses two barriers for the consumption of M. oleifera leaves: (1) inconsistencies between the actual nutritional value and common nutritional claims and (2) its bitterness. It was hypothesized that the reduction of bitterness in M. oleifera-fortified foods would increase their acceptability. The micronutrient content of M. oleifera leaves from India and Nigeria was determined in this study and compiled along with literature values. Data on M. oleifera were compared against spinach and carrot for minerals and carotenoids respectively. The effectiveness of acid soaking and addition of sweetness (aspartame) as debittering treatments was examined using a full factorial design. A sensory evaluation (randomized complete block, 9-point hedonic scale) with 50 assessors examined the preference and bitterness in 7 fortified instant soup mix formulations with varying levels of debittered M. oleifera leaf powder. Results of this study do not support nutritional claims asserting that M. oleifera contains 25 times more iron than spinach and 10 times more vitamin A than carrots. M. oleifera contains, at most, 3.4 times more iron than dried spinach. The equivalent vitamin A content in M. oleifera was found to be 55% lower than that in dried carrots. These inconsistencies may be a result of nutritional comparisons made between inequivalent moisture contents, as dried M. oleifera leaves have 18 times more iron than fresh spinach by weight. The control (no fortification) formulation was most preferred, followed by formulations with 50% replacement by M. oleifera. The addition of sweetness significantly reduced bitterness and increased acceptance of fortified soup mix formulations. M. oleifera’s nutritional content is lower than that asserted by common nutritional claims, but its abundance in food insecure regions and wide range of nutrients maintain its potential as a nutritious food source for populations low and middle income nations. It is recommended that M. oleifera-fortified food products include a sweet excipient to reduce bitterness to increase acceptability. This research was funded by the Centre for Global Engineering (University of Toronto) and Mitacs Canada.
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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) | 0.002 | 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".