Applicability of Nixtamalization in the Processing of Millet-based Maasa, a Fermented Food in Ghana
Bibliographic record
Abstract
Maasa is a spontaneously fermented millet-based fried cake in Ghana. Nixtamalization, a process of cooking and soaking cereals (usually maize) in lime solution, was applied in the traditional processing of the Ghanaian millet-based fermented maasa. During the processing, Lime cooked millet dough (LCMD) and water soaked millet dough (WSMD) samples were analyzed for proximate composition, pH, total titratable acidity and microbial counts were assessed for fermenting millet dough samples. Finally, maasa prepared from nixtamalized and non-nixtamalized fermented millet dough samples were assessed for consumer sensory acceptability on a five-point hedonic scale. Nixtamalization improved crude protein and ash contents of millet dough samples whereas fat and fiber contents decreased. During fermentation, a reduction in pH and increase in total titratable acidity was observed for both nixtamalized and non-nixtamalized millet dough samples. Lactic acid bacteria (LAB) and yeasts count reached 9.4 and 8.0 logcfu/g respectively for non-nixtamalized millet after 14 hours of fermentation, whereas for nixtamalized millet samples, LAB and yeasts count reached 7.6 and 7.5 logcfu/g respectively. Consumer sensory evaluation of Maasa produced from nixtamalized fermented millet had improved texture, colour and overall acceptability as compared to the traditional non-nixtamalized fermented millet-based maasa. Nixtamalization can thus be applied in the production of Ghanaian millet-based maasa to improve nutritional quality and acceptability as well as maintain the benefits associated with traditional cereal fermentation.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".