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Record W4406309089 · doi:10.1016/j.lwt.2025.117356

Enhancement of taste of low-sodium dry-cured mackerel (Scomberomorus niphonius) via the light fermentation with Lactobacillus plantarum R-1

2025· article· en· W4406309089 on OpenAlexaff
Juan Yang, Pingru Liu, Yongtao Fei, Hao Dong, Xiaofang Zeng, Weidong Bai

Bibliographic record

VenueLWT · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Hydrolysis and Bioactive Peptides
Canadian institutionsMinistry of Agriculture
FundersInnovative Research Group Project of the National Natural Science Foundation of China
KeywordsFood scienceLactobacillus plantarumFermentationTasteMackerelChemistryBiologyFisheryLactic acidBacteriaFish <Actinopterygii>

Abstract

fetched live from OpenAlex

This study aimed to evaluate the improvement in taste and safety of low-sodium dry-cured mackerel ( Scomberomorus niphonius ) through light fermentation with Lactobacillus plantarum R-1 (LPDSM). The treatment with this bacterium enhanced its umami and salty flavors while decreasing bitter and astringent tastes The concentrations of umami amino acids and small peptides <1 KDa in LPDSM were higher than those in the low-salt group (LSDSM) and normal-salt group (NSDSM). However, there were only six myofibrillar protein-derived umami peptides detected in LPDSM, less than NSDSM with ten detected umami peptides. These results suggested that fermentation with Lactobacillus plantarum R-1 enhanced the taste due to increased total concentration of peptides and amino acids rather than producing more specific sequences of umami peptides. 16S rRNA analysis revealed that spoilage bacteria such as Staphylococcus , Macrococcus , and Enterobacteriaceae were inhibited by Lactobacillus plantarum R-1, which became the dominant bacterium in LPDSM. • Lactobacillus plantarum R-1 can enhance sensory properties • Eighteenth umami peptides from myofibrillar proteins were detected • Bacteria did not change the number of taste peptide sequences • Lactobacillus plantarum R-1 makes more taste substances by making more protease

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 score0.006
Threshold uncertainty score0.340

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 head0.004
GPT teacher head0.218
Teacher spread0.214 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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