Lactic acid bacteria obtained from cereal-based fermented food products at different processing stages
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Selective consumption of fermented foods obtained from dairy sources have resulted from problems of lactose intolerance, dairy allergies and strict vegetarian dietary habits. Non-dairy, cereal-based fermented food products undergo different processing stages and it is believed that lactic acid bacteria (LAB), is involved in the fermentation process. However, there are little or no information on the specific LAB that are involved at the various intermediate stages. Objectives: The aim of this study was to explore indigenous cereal food products as an alternative source of LAB as well as isolate and identify LAB from cereal-based fermented food obtained at different processing stages. Methods: Two varieties of corn/maize and sorghum (guinea corn) were cleaned, steeped in water and milled. Samples obtained at different processing stages were collected into sterile containers. LAB were isolated on De Man, Rogosa and Sharpe agar and characterized using biochemical, microscopic and molecular methods. Results: The total viable bacterial cell count ranged from 9.22 to 9.66 log10 CFU/ml. Conventional identification method revealed rod-shaped, Gram positive, catalase negative, non-spore forming bacteria with single, paired and long chain cell arrangements. The 16S rRNA gene sequence analysis identified diverse species of two LAB groups namely: Lactobacillus (93.02%) and Pediococcus (6.98%) with L. fermentum as the dominant Lactobacillus spp. Conclusion: This study revealed the presence of LAB from fermented maize and sorghum at different processing stages. Our findings show that the slurry-processed-stage, which is the commonly consumed fermented food product apparently contains similar diverse LAB as identified in the milled whole product.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| 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 it