Controlling the Traditional Fermentation Process for the Production of “attiéké” in Dabou (Côte d’Ivoire)
Bibliographic record
Abstract
Introduction: “Attiéké” plays a crucial role in the daily lives and diets of many communities in Côte d'Ivoire. However, the national market offers a wide variety of “attiéké” qualities, which affects the uniformity of its production. One of the main causes of this variation is poor control of the fermentation parameters. Objective: The aim of this study was to standardise attiéké production by optimising fermentation, focussing on ferment control, the rate of incorporation, and fermentation time, to guarantee consistent quality. Methods: To this end, “attiéké” samples were prepared from fermented doughs containing 8%, 10% and 12% inoculum, with fermentation periods of 0, 6, 12 and 24 hours. These samples were then compared with a reference “attiéké” produced in Dabou. The effectiveness of standardising fermentation parameters was assessed by measuring microbial loads of lactic acid bacteria, Bacillus, yeasts, and moulds, and by analysing physicochemical parameters such as pH, glucose, sucrose, fructose, lactic acid, acetic acid and ethanol. Results: The results showed that the fermentation that produced an “attiéké” similar to the control sample was carried out with 10% ferment for 12 hours (C10T12). This “attiéké” showed Bacillus loads of 2.6 log10 cfu/g, lactic bacteria loads of 1.1 log10 cfu/g, as well as a glucose concentration of 6.35 g/L, sucrose of 4.8 g/L, lactic acid of 4.45 g/L, acetic acid of 1.35 g/L, and a pH of 4.8. Conclusion: Therefore, this work answers the question of the reproducibility of this traditional dish, “attiéké”.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".