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A new strategy to apply<i>Bacillus subtilis</i>MA139 for the production of solid-state fermentation feed

2009· article· en· W2033944202 on OpenAlexfundno aff
Ronghua Zhu, Wen‐Qing Lu, Lan Gong

Bibliographic record

VenueLetters in Applied Microbiology · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProbiotics and Fermented Foods
Canadian institutionsnot available
FundersNational Key Laboratory of Animal NutritionChinese Academy of SciencesUniversity of Saskatchewan
KeywordsLactobacillus fermentumBacillus subtilisFermentationFood scienceBranSolid-state fermentationEnterobacteriaceaeStarterBiologyBacteriaSalmonellaLactic acidStrain (injury)Escherichia coliMicrobiologyBiochemistryLactobacillus plantarum

Abstract

fetched live from OpenAlex

AIM: The study investigated the potential of using Bacillus subtilis MA139 in combination with Lactobacillus fermentum and Saccharomyces cerevisae to produce solid-state fermentation feed. METHODS AND RESULTS: In a pure fermentation, B. subtilis MA139 was able to grow and synthesize antimicrobial substances at temperatures from 25 to 37 degrees C and at a pH from 5.0 to 9.0. Subsequently, B. subtilis MA139, Lact. fermentum and S. cerevisae were used as starter strains co-inoculated in unsterilized substrate (feed-grade soybean meal and wheat bran). Following 10 days of fermentation in a newly developed plastic bag equipped with a one-way valve, lactic acid bacteria and Bacillus became the predominant strains while S. cerevisae cells decreased slightly. Enterobacteriaceae (Escherichia coli K88 and Salmonella typhimurium) were not detected. CONCLUSIONS: Use of B. subtilis MA139 as a starter strain co-inoculated with S. cerevisae and Lact. fermentum successfully controlled the growth of enterobacteriaceae. SIGNIFICANCE AND IMPACT OF THE STUDY: This study provided a facile and low-cost way to produce solid-state fermentation feed.

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 score0.098
Threshold uncertainty score0.227

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 head0.014
GPT teacher head0.231
Teacher spread0.217 · 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

Citations16
Published2009
Admission routes1
Has abstractyes

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