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Record W2754379370 · doi:10.1094/asbcj-2017-3464-01

Mechanical Characterization of Individual Brewing Yeast Cells Using Microelectromechanical Systems (MEMS): Cell Rupture Force and Stiffness

2017· article· en· W2754379370 on OpenAlexafffund
Bruno Barazani, Stephan Warnat, Ted Hubbard, Andrew J. MacIntosh

Bibliographic record

VenueJournal of the American Society of Brewing Chemists · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Inactivation Methods
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsBrewingYeastFermentationMicroelectromechanical systemsStiffnessSaccharomyces cerevisiaeMaterials scienceChemistryFood scienceComposite materialBiochemistryNanotechnology

Abstract

fetched live from OpenAlex

The mechanical properties of individual yeast cells were measured using microelectromechanical systems (MEMS). Samples were taken throughout two controlled fermentations conducted as per ASBC Yeast-14: one utilized ale yeast (Saccharomyces cerevisiae, “red ale”) and the other utilized a lager strain (S. pastorianus, “SMA”). At least five lager and five ale cells were tested at each fermentation phase (start, middle, and end). Cell compression was induced by a MEMS squeezer, and displacement measurements were taken using optical microphotographs. The failure of each cell was similar; the cell would undergo minor deformation until visible rupture occurred, followed by significant cell shrinkage. Across all fermentation phases ale cells ruptured under an average force of 0.28 ± 0.05 μN, whereas lager cells ruptured at 0.47 ± 0.10 μN. The average stiffness at the midpoint of fermentation was found to be 4.8 ± 1.0 and 5.3 ± 0.9 μN/μm for ale and lager cells, respectively. The use of MEMS technology to study physical characteristics of brewing yeast during fermentation has not previously been attempted (to the authors' knowledge). This study may assist brewers in the selection of process parameters to improve yeast health and in the design of novel yeast handling technologies.

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 score0.001
Threshold uncertainty score0.002

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 communication0.0000.000
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 head0.019
GPT teacher head0.283
Teacher spread0.264 · 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

Citations4
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueJournal of the American Society of Brewing ChemistsSame topicMicrobial Inactivation MethodsFrench-language works237,207