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Record W4394850909 · doi:10.1016/j.lwt.2024.116071

Impact of NaCl on physicochemical properties, microbial community, and pathogen surveillance in the Chinese traditional fermented broad bean (Vicia faba L.) paste

2024· article· en· W4394850909 on OpenAlexaff
Zhihua Li, Chi Zhao, Ling Dong, Fengju Zhang, Xiaohang Wang, Shuang Zhao, Liang Li

Bibliographic record

VenueLWT · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProbiotics and Fermented Foods
Canadian institutionsThe Metabolomics Innovation CentreUniversity of Alberta
Fundersnot available
KeywordsVicia fabaFood scienceFermentationChemistryBacteriaLactic acidPepperAntimicrobialBiologyMicrobiologyBotany

Abstract

fetched live from OpenAlex

Doubanjiang (DBJ), a mixed fermented broad bean (Vicia faba L.) and red pepper (Capsicum annuum L.) condiment used worldwide, is known for its high-salt content (∼20g/100g). Industry demands sodium reduction in DBJs. In this study, we investigated the physicochemical properties, microbial community changes, and pathogen surveillance in DBJ samples with varying salt contents (10g/100g, 15g/100g, 20g/100g and 25g/100g). Our findings revealed that these samples could be categorized into two groups: low-salt (10g/100g) and high-salt samples (15-25g/100g). In low-salt samples, Lactobacillus emerged as the predominant bacteria, exhibiting higher antioxidant activity. The most concentrated organic acids were γ-aminobutyric acid and lactic acid. Conversely, Staphylococcus dominated the bacteria composition in high-salt samples. Human pathogenicity such as Proteus mirabilis, Escherichia coli, and Klebsiella pneumoniae were discovered in both low- and high-salt samples. Higher abundance of antimicrobial resistances was observed in high-salt samples while slightly increased biogenic amine content in low-salt samples. This knowledge provides insights into impact of NaCl on fermented broad bean (Vicia faba L.) paste.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.987
Threshold uncertainty score0.128

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 head0.030
GPT teacher head0.243
Teacher spread0.213 · 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 designObservational
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

Citations11
Published2024
Admission routes1
Has abstractyes

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