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Record W4297494241 · doi:10.1016/j.fm.2022.104146

Candida krusei is the major contaminant of ultrafiltration and reverse osmosis membranes used for cranberry juice production

2022· article· en· W4297494241 on OpenAlexaffabout
Sherazade Fikri, Marie-Hélène Lessard, Véronique Perreault, Alain Doyen, Steve Labrie

Bibliographic record

VenueFood Microbiology · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant-Microbe Interactions and Immunity
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsReverse osmosisMembraneUltrafiltration (renal)Candida kruseiChemistryTorulaspora delbrueckiiFood scienceChromatographyBiologyYeastBiochemistryCandida tropicalis

Abstract

fetched live from OpenAlex

Ultrafiltration (UF) and reverse osmosis (RO) are commonly used for the clarification and concentration of fruit juices. However, one of the main limitations of filtration membranes is biofouling, which reduces membrane efficiency and can contaminate the filtered product and lead to spoilage. In this study, the microbial fouling layers of UF and RO membranes from a Canadian cranberry juice processing plant were characterized. Unlike the microbiota found in cranberry juice, which is dominated by Bacillus sp. and other bacteria, both UF and RO membranes were mainly colonized by several strains of the yeast Candida krusei. A variation in bacterial and yeasts count was observed between tubular UF and spiral-wound RO membranes, and the analysis of the spatial distribution highlighted the homogeneity of the contamination across each membrane. Surprisingly, RO membranes had a higher level of contamination when compared to UF membranes. Furthermore, six strains of C. krusei were further characterized through multilocus sequence typing analysis, five of which exhibited unique allelic profiles and two of which were found to contain a new TRP1 allele.

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 score0.309
Threshold uncertainty score0.389

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 studies0.0010.000
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 head0.017
GPT teacher head0.212
Teacher spread0.195 · 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

Citations6
Published2022
Admission routes2
Has abstractyes

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