MétaCan
Menu
Back to cohort
Record W2769213823 · doi:10.5539/jfr.v6n6p83

The Effects of Replacing Pork Fat with Cold-Pressed Coconut Oil on the Properties of Fresh Sausage

2017· article· en· W2769213823 on OpenAlexvenueno aff
Wannee Tangkham, Frederick LeMieux

Bibliographic record

VenueJournal of Food Research · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsnot available
Fundersnot available
KeywordsTBARSFood scienceChemistryThiobarbituric acidCoconut oilLipid oxidationMoistureComposition (language)AntioxidantLipid peroxidationBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The effects of substituting coconut oil on the chemical composition, microorganism, and sensory properties of fresh sausages were investigated. This experiment evaluated three (0, 10%, 20%) levels of cold-pressed coconut oil (CPCO) and pork fat stored at 3°C for 14 days. The following treatments: 1) control (20% pork fat: 0% CPCO), 2) 0% pork fat: 20% CPCO and 3) 10% pork fat: 10% CPCO were replicated three times. Treatments were analyzed for pH value, % moisture content, % drip loss, lipid stability (thiobarbituric acid-reactive substances TBARS), aerobic plate counts, Escherichia coli, Staphylococcus aureus, and sensory analysis. The initial moisture content of fresh sausage in this experiment ranged from 63.44 to 65.67%. Fresh sausage with 20% CPCO inhibited the growth of aerobic bacteria and obtained the highest TBARS values (4.25 mg MDA/kg) compared to the control treatment. In addition, fresh sausage (10% pork fat and 10% CPCO) decreased the % drip loss, pH value and obtained the highest overall rating (6.45) of sensory testing (n = 75). No E. coli and S. aureus were found in this study for 14 d at 3°C.

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)0.004
metaresearch head score (Gemma)0.003
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 score0.015
Threshold uncertainty score0.674

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.145
GPT teacher head0.323
Teacher spread0.178 · 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

Citations7
Published2017
Admission routes1
Has abstractyes

Explore more

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