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Record W4405900841 · doi:10.1021/acs.jafc.4c08975

Dynamic Metabolomic Changes in the Phenolic Compound Profile and Antioxidant Activity in Developmental Sorghum Grains

2024· article· en· W4405900841 on OpenAlexaff
Carolina Thomaz dos Santos D’Almeida, Marie‐Hélène Morel, Nancy Terrier, Hamza Mameri, Mariana Simões Larraz Ferreira

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGABA and Rice Research
Canadian institutionsImpactIONICS Mass Spectrometry (Canada)
FundersUniversidade Federal do Estado do Rio de JaneiroFundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de JaneiroConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsMetabolomicsSorghumAntioxidantChemistryFood scienceBiologyBiochemistryChromatographyAgronomy

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide Phenolic compounds (PC) were analyzed by UHPLC-ESI-QTOF-MS E in two sorghum genotypes, harvested in two growing seasons (GS) at five distinct days after flowering (DAF) to evaluate how genotype/GS influences the PC synthesis and antioxidant capacity during grain growth. Total phenolic contents were strongly correlated with antioxidant capacity ( r > 0.9, p < 0.05). Globally, 97 PC were annotated, including 20 PC found irrespective of the grain developmental stage and genotype/GS. The phenolic profile clearly differs between stages: phenolic acids were the most abundant class in early stages (50%), and flavonoid accumulation becomes predominant in late ones (3/5 of total ion abundance). Dimeric and trimeric tannins were identified even in 10DAF grains. Chemometry revealed great PC variability between genotypes (27%) and important biomarkers of GS differentiation (e.g., ferulic acid). This work can input open databases of PC and paves the way to understand biosynthetic pathways of PC in sorghum and future sorghum selection

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.971
Threshold uncertainty score0.187

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 head0.022
GPT teacher head0.237
Teacher spread0.215 · 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

Citations6
Published2024
Admission routes1
Has abstractyes

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