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Record W4317356525 · doi:10.1016/j.crfs.2023.100448

Conversion of (poly)phenolic compounds in food fermentations by lactic acid bacteria: Novel insights into metabolic pathways and functional metabolites

2023· review· en· W4317356525 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCurrent Research in Food Science · 2023
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicProbiotics and Fermented Foods
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsBiochemistryFermentationLactic acidHydroxycinnamic acidMetabolic pathwayGlycosideEnzymeMetabolismChemistryHydroxybenzoic acidBacteriaBiotransformationBiologyOrganic chemistry

Abstract

fetched live from OpenAlex

Lactobacillaceae are among the major fermentation organisms in most food fermentations but the metabolic pathways for conversion of (poly)phenolic compounds by lactobacilli have been elucidated only in the past two decades. Hydroxycinnamic and hydroxybenzoic acids are metabolized by separate enzymes which include multiple esterases, decarboxylases and hydroxycinnamic acid reductases. Glycosides of phenolic compounds including flavonoids are metabolized by glycosidases, some of which are dedicated to glycosides of plant phytochemicals rather than oligosaccharides. Metabolism of phenolic compounds in food fermentations often differs from metabolism in vitro, likely reflecting the diversity of phenolic compounds and the unknown stimuli that induce expression of metabolic genes. Current knowledge will facilitate fermentation strategies to achieve improved food quality by targeted conversion of phenolic compounds.

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.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.964
Threshold uncertainty score0.589

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.006
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.293
GPT teacher head0.389
Teacher spread0.096 · 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