MétaCan
Menu
Back to cohort
Record W2560466567 · doi:10.5539/jfr.v6n1p22

Combining Modified Atmosphere Packaging and Nisin to Preserve Atlantic Salmon

2016· article· en· W2560466567 on OpenAlexvenueno aff
Dong Han, Inyee Han, Paul Dawson

Bibliographic record

VenueJournal of Food Research · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsnot available
Fundersnot available
KeywordsNisinModified atmosphereFood spoilageLactic acidFood scienceShelf lifeBacteriaAerobic bacteriaChemistryBacterial growthBiology

Abstract

fetched live from OpenAlex

Preservation effects of modified atmosphere package combined with nisin on fresh Atlantic salmon were evaluated. Farm-raised Atlantic salmon were purchased from the local market and packaged using either 19 % CO2: 70 % N2 : 11 % O2 , 38 % CO2 : 51 % N2 : 11 % O2, and under atmospheric air (with and without nisin at 400 IU/g) resulting in a total of 6 treatments. The microbiological (aerobic plate count, psychrotrophic bacteria, and lactic acid bacteria) and the total volatile basic nitrogen analyses were evaluated on Day 0, 2, 4, 7 and 10. Package headspace and sensory evaluation were also conducted on Day 0, 2 and 4. The presence of CO2 effectively inhibited the growth of all three types of bacteria while nisin significantly inhibited the growth of aerobic microorganisms with less impact on lactic acid bacteria. The TVB-N test indicated that CO2 delayed the spoilage of Atlantic salmon while nisin had a lesser but measurable impact on Atlantic salmon shelf-life. The experiments support the potential for combining modified atmosphere package and nisin as an effective method to limit the spoilage of Atlantic salmon compare to traditional preservation methods.

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 score0.003

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 head0.161
GPT teacher head0.348
Teacher spread0.187 · 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

Citations5
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Food ResearchSame topicMeat and Animal Product QualityFrench-language works237,207