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Record W4321371838 · doi:10.1111/ijfs.16358

Antioxidant and antibacterial activities of indigenous plant leaf ethanolic extracts and their use for extending the shelf‐life of Nile tilapia ( <i>Oreochromis niloticus</i> ) mince

2023· article· en· W4321371838 on OpenAlexaff
Pitima Sinlapapanya, Punnanee Sumpavapol, Rotimi E. Aluko, Pornpot Nuthong, Bin Zhang, Soottawat Benjakul

Bibliographic record

VenueInternational Journal of Food Science & Technology · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEssential Oils and Antimicrobial Activity
Canadian institutionsUniversity of Manitoba
FundersNational Research Council of Thailand
KeywordsNile tilapiaAntioxidantChemistryFood scienceTilapiaAntimicrobialOreochromisShelf lifeMinimum inhibitory concentrationStaphylococcus aureusBacteriaTraditional medicineBiologyBiochemistryFish <Actinopterygii>FisheryMedicine

Abstract

fetched live from OpenAlex

Summary Long pepper (LP), soursop (SS), green vein kratom (GK), and red vein kratom (RK) leaf ethanolic extracts were prepared using an ultrasonic device, followed by dechlorophyllization by the sedimentation process. Antioxidant and antimicrobial activities of the extracts were studied. Highest yield and total phenolic content were found in RK extract ( P &lt; 0.05). In general, RK extract showed the lowest minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) towards four bacteria ( Escherichia coli , Staphylococcus aureus , Pseudomonas aeruginosa , and Shewanella sp.), when compared with other extracts. Addition of GK extract at 600 mg kg −1 retarded chemical changes and lowered microbiological growth in Nile tilapia mince within 12 days at 4 °C. Lipid oxidation was also impeded with the aid of GK extract. Therefore, GK extract inhibited both spoilage and pathogenic bacteria, and prevented lipid oxidation, thus extending the shelf‐life of tilapia mince.

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)0.001
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 score0.037
Threshold uncertainty score0.344

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.023
GPT teacher head0.244
Teacher spread0.221 · 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

Citations8
Published2023
Admission routes1
Has abstractyes

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