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Record W3215373900 · doi:10.13005/ojc/370613

Impact of Heat on the Different Stages of the Production of Local Beer “Tchoukoutou” Made from Sorghum

2021· article· en· W3215373900 on OpenAlexaff
Adjo Cynthia Kouakou, Kokou Agbékonyi Agbodan, Kwami Lumo Awaga, Awèdéou Bakpo, Eyawèdeou Yelegue, Koffi Jondo

Bibliographic record

VenueOriental Journal Of Chemistry · 2021
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsMinistère des Ressources naturelles et des Forêts (Québec)
Fundersnot available
KeywordsBrewingGerminationSorghumFood scienceChemistryMathematicsHorticultureAgronomyBiologyFermentation

Abstract

fetched live from OpenAlex

Tchoukoutou is a local alcoholic drink made from sorghum. The optimization and good quality of this drink depend on certain factors such as temperature that were the subject of our work. To carry out this study, we have set ourselves the general objective of structuring the production of Tchoukoutou. Specifically, the study aims to systematize the cooking of the wort, to verify the properties of the beer through tests and to produce a quality drink. An investigation was made on the influence of temperature on sorghum germination and wort cooking. The study of the effect of temperature on the germination of sorghum was carried out. It concerned four different temperature levels which are: 23 ° C, 26 ° C, 30 ° C and 35 ° C. A study of the influence of temperature on brewing was done with different varieties of sorghum and also at different temperature ranges: 78 ° C, 83 ° C, 89 ° C, and 92 ° C. The results showed that the ideal germination temperature is 30 ° C and for brewing it is 78 ° C. The soaking time during germination, for a good germination rate, is between 16h and 26h. In conclusion, the temperature factor is a parameter that should not be overlooked when making Tchoukoutou. Our study thus made will serve as a basis for subsequent studies in the same direction to achieve a standardized industrial production of Tchoukoutou.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.282

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.244
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2021
Admission routes1
Has abstractyes

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