MétaCan
Menu
Back to cohort
Record W2996600852 · doi:10.3390/foods8120679

Inactivation of Escherichia Coli and Salmonella Using 365 and 395 nm High Intensity Pulsed Light Emitting Diodes

2019· article· en· W2996600852 on OpenAlexafffund
Amritha Prasad, Michael G. Gänzle, M. S. Roopesh

Bibliographic record

VenueFoods · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicListeria monocytogenes in Food Safety
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of AlbertaAlberta Agriculture and Forestry
KeywordsSalmonella entericaSalmonellaEscherichia coliLight-emitting diodeFood scienceBacteriaChemistryMicrobiologyAntibacterial activityMaterials scienceBiologyOptoelectronicsBiochemistry

Abstract

fetched live from OpenAlex

High intensity pulsed light emitting diode (LED) treatment is a novel approach to inactivate foodborne pathogens. The objective of this study was to evaluate the antibacterial potential of high intensity 365 (UV-A) and 395 nm (NUV-Vis) LED treatments against Escherichia coli and Salmonella enterica at high and low water activity (aw) conditions, and to understand the influence of different process parameters on their antibacterial efficacy. Bacteria at high (in phosphate buffer saline, PBS) and low aw (aw = 0.75) conditions were treated with both the LEDs with specific doses at a fixed distance from the LEDs. The 365 nm LED showed more effectiveness in reducing the dried bacteria compared to 395 nm LED. The dry E. coli showed more resistance to LED treatments compared to Salmonella. The 365 and 395 nm LED treatments with ~658 J/cm2 dose resulted in reductions of 0.79 and 1.76 log CFU/g of Salmonella, respectively, on 0.75 aw pet foods. The LED treatments increased the surface temperature, resulting in water loss in the treated samples. This study showed that the dose, duration of light exposure, bacterial strain, and aw played a major role in the antibacterial efficacy of the 365 and 395 nm LEDs.

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 score0.009
Threshold uncertainty score0.475

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 head0.033
GPT teacher head0.281
Teacher spread0.248 · 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

Citations36
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueFoodsSame topicListeria monocytogenes in Food SafetyFrench-language works237,207