Selection, use and the influence of starter cultures in the nutrition and processing improvement of ‘ogi’
Bibliographic record
Abstract
Abstract Starter cultures for ‘ogi’ production were selected with the aim of improving processing technique, lysine and methionine levels of ‘ogi’, a lactic acid fermented weaning food. Technological features of microorganisms from fermenting raw materials were screened to develop quality starter cultures. Lactobacillus brevis XO43 and Saccharomyces cerevisiae OY4 were finally selected and used as starter cultures in a pilot plant study. Fermentation with starter cultures showed constant final pH level of 3.35 in modified substrates after fermentation unlike spontaneously fermented; and significantly higher acidity (%TTA) indicative of faster fermentation. Addition of sugar (2%w/w) and soybean flours (1% w/w) also increased the acid levels during fermentation. Fermentation of dehulled maize grains fortified with glucose (2%w/w) alone by starter cultures shows the best quality in all the parameters considered except the available niacin yield. It is 24% higher than oven-dried ‘ogi’ flour from the traditional process and 11% higher than unprocessed whole maize grains. The methionine is 92% and 77% higher than the traditionally prepared ‘ogi’ and whole maize grains, respectively. The total amino acids level of the sample was 32% more than the traditionally fermented ‘ogi’ flour and 55% more than maize grains. Although, the soluble protein level was 23% lesser than the unfermented whole maize grains, it was 12% more than the dehulled maize substrate and ‘ogi’ from traditional process. Fermentation of corn into ‘ogi’ led to losses in the initial quantity of niacin. Starter cultures significantly improve the nutrients such as lysine, methionine, total amino acids, soluble protein and niacin more than spontaneous fermentation. Dehulling of grains, dewatering and oven drying significantly reduce nutrients. ‘Ogi’ fermentation process with the use of starter cultures and dehulled maize guarantee organoleptic qualities, improve ‘ogi’ production and create a better nutritional products.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".