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Record W4386127601 · doi:10.11159/icbb23.108

Influence Of Kluyveromyces Lactis Arranged In Suspension And Immobilized On Obtaining Lactic Acid By Cheese Whey Fermentation

2023· article· en· W4386127601 on OpenAlexvenueno aff
María Vargas, Carlos Gordillo-Andia, Danny Tupayachy-Quispe, Jonathan Almirón, Francine Roudet

Bibliographic record

VenueProceedings of the World Congress on New Technologies · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProbiotics and Fermented Foods
Canadian institutionsnot available
Fundersnot available
KeywordsKluyveromyces lactisKluyveromycesFermentationLactic acidSuspension (topology)ChemistryFood scienceLactic acid fermentationChromatographyBiochemistryYeastBacteriaBiologyMathematicsSaccharomyces cerevisiae

Abstract

fetched live from OpenAlex

Lactic acid has several applications in the pharmaceutical, food, cosmetic and chemical industries, and is currently used for its transformation into polylactic acid, which is a biopolymer used to produce environmentally friendly bioplastics.So, in the present research lactic acid was obtained from the fermentation of cheese whey, that is a waste from the cheese industry that generates high environmental pollution, hence, this research seeks to give it an added value.Thus, the Kluyveromyces lactis strain was isolated from the cheese whey, which was also used for the fermentation process and the obtaining of lactic acid, using proteinized (fresh) and deproteinized cheese whey, the strains arranged in suspension and immobilized in order to determine if those conditions have an influence on the characteristics of lactic acid.It has been confirmed that the arrangement of the strain used (in free or encapsulated form), the use of untreated and deproteinized cheese whey, and the purification conditions have an influence on the characteristics and yields of the lactic acid obtained (color, density, and the presence of other functional groups in it).

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 score0.090
Threshold uncertainty score0.224

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.015
GPT teacher head0.236
Teacher spread0.222 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on New TechnologiesSame topicProbiotics and Fermented FoodsFrench-language works237,207