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Record W2485097753 · doi:10.1111/1750-3841.13412

Water Diffusion from a Bacterial Cell in Low‐Moisture Foods

2016· article· en· W2485097753 on OpenAlexaff
Roopesh M. Syamaladevi, Juming Tang, Qingping Zhong

Bibliographic record

VenueJournal of Food Science · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsUniversity of Alberta
FundersFood Safety and Inspection ServiceUniversity of California, DavisU.S. Department of Agriculture
KeywordsDiffusionWater activityMoistureSorptionHumidityWater contentChemistryMultiphysicsEffective diffusion coefficientThermodynamicsBacterial cell structureWater vaporPorous mediumBacteriaMicroorganismEnvironmental chemistryPorosityBiologyPhysicsPhysical chemistryAdsorptionGeology

Abstract

fetched live from OpenAlex

We used a Fick's unsteady state diffusion equation to estimate the time required for a single spherical shaped bacterium (assuming Enterococcus faecium as the target microorganism) in low-moisture foods to equilibrate with the environment. We generated water sorption isotherms of freeze-dried E. faecium. The water activity of bacterial cells at given water content increased considerably as temperature increased from 20 to 80 °C, as observed in the sorption isotherms of bacterial cells. When the water vapor diffusion coefficient was assumed as between 10(-12) and 10(-10) m(2) /s for bacterial cells, the predicted equilibration times (teq ) ranged from 8.24×10(-4) to 8.24×10(-2) s. Considering a cell membrane barrier with a lower water diffusion coefficient (10(-15) m(2) /s) around the bacterial cell with a water diffusion coefficient of 10(-12) m(2) /s, the teq predicted using COMSOL Multiphysics program was 3.8×10(-1) s. This result suggests that a single bacterium equilibrates rapidly (within seconds) with change in environmental humidity and temperature.

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 score0.033
Threshold uncertainty score0.141

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 head0.007
GPT teacher head0.234
Teacher spread0.227 · 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

Citations35
Published2016
Admission routes1
Has abstractyes

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