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Record W2559771709 · doi:10.3390/beverages2040035

Influence of Freeze-Dried Yeast Starter Cultures on Volatile Compounds of Tchapalo, a Traditional Sorghum Beer from Côte d’Ivoire

2016· article· en· W2559771709 on OpenAlexfundno aff
Wahauwouélé Hermann Coulibaly, Kouadio Florent N’guessan, Ibourahema Coulibaly, Marlène Cot, Peggy Rigou, Koffi Marcellin Djè

Bibliographic record

VenueBeverages · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFermentation and Sensory Analysis
Canadian institutionsnot available
FundersAgence Universitaire de la FrancophonieIndian National Science Academy
KeywordsStarterFood scienceOrganolepticSorghumFlavorChemistryYeastEthanolCandida tropicalisTasteBiologyBiochemistryAgronomy

Abstract

fetched live from OpenAlex

The production of the Ivorian sorghum beer known as tchapalo remains more or less an empirical process. The use of starter cultures was therefore suggested as the appropriate approach to alleviate the problems of variations in organoleptic quality and microbiological stability. In this study, we evaluated the capacity of S. cerevisiae and C. tropicalis to produce sorghum beer as freeze-dried starter in mixed or pure cultures. Beers produced with mixed freeze-dried cultures of S. cerevisiae F12-7 and C. tropicalis C0-7 showed residual sugars and ethanol contents similar to beers obtained with S. cerevisiae F12-7 pure culture, but the total sum of organic acids analyzed was the highest with the mixed culture (15.71 g/L). Higher alcohols were quantitatively the largest group of volatile compounds detected in beers. Among these compounds, 2-phenyl ethanol, a higher alcohol that plays an important role in beer flavor, was highly produced with the mixed culture (10,174.8 µg/L) than with the pure culture (8749.9 µg/L).

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.215
Teacher spread0.191 · 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 source (direct Gemma or distilled Codex), 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

Citations16
Published2016
Admission routes1
Has abstractyes

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