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Record W2517716290 · doi:10.1094/cchem-05-16-0155-r

Changes in the Phenolic Acid Content and Antioxidant Activity During Kernel Development of Corn (<i>Zea mays</i> L.) and Relationship with Mycotoxin Contamination

2016· article· en· W2517716290 on OpenAlexaff
Debora Giordano, Trust Beta, Amedeo Reyneri, Massimo Blandino

Bibliographic record

VenueCereal Chemistry · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycotoxins in Agriculture and Food
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMycotoxinChemistryCaffeic acidFerulic acidPhenolic acidContaminationFumonisinFood scienceChlorogenic acidHorticultureAntioxidantBiologyBiochemistry

Abstract

fetched live from OpenAlex

Corn grain production could be affected by several fungal pathogens responsible for the production of mycotoxins. The aims of this study were to determine the evolution of phenolic acids and total antioxidant activity (TAA) during kernel development and to evaluate their potential protective role in minimizing mycotoxin contamination in six corn genotypes (four open‐pollinated varieties and two hybrids) characterized by a wide array of kernel traits. TAA and free and cell wall‐bound phenolics showed significant differences among corn genotypes at different stages of development, with the highest values found at the beginning of kernel development. Ferulic, p ‐coumaric, and caffeic acids were the main cell wall‐bound phenolic acids during kernel development, whereas chlorogenic acid was the main free phenolic acid. A significant negative correlation was observed between deoxynivalenol contamination at harvest maturity and free phenolic acids and TAA at the beginning of kernel development, whereas no significant correlation was observed with fumonisin contamination. In conclusion, free phenolic acids are evidently involved in the resistance mechanism toward deoxynivalenol contamination, whereas their role toward fumonisin contamination was not elucidated under field conditions, implying that components other than phenolic acids may be responsible for this latter type of resistance.

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 score0.518
Threshold uncertainty score0.114

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 head0.026
GPT teacher head0.199
Teacher spread0.173 · 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

Citations18
Published2016
Admission routes1
Has abstractyes

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