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Record W2075943178 · doi:10.1021/jf100161r

Impact of Postharvest Handling on Carotenoid Concentration and Composition in High-Carotenoid Maize (<i>Zea mays</i> L.) Kernels

2010· article· en· W2075943178 on OpenAlexaff
Andrew Burt, Christopher M. Grainger, J. Christopher Young, Barry J. Shelp, Elizabeth A. Lee

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2010
Typearticle
Languageen
FieldMedicine
TopicAntioxidant Activity and Oxidative Stress
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Guelph
Fundersnot available
KeywordsCarotenoidLuteinXanthophyllZeaxanthinEndospermPostharvestFood scienceFood storageChemistryWarehouseBotanyHorticultureAgronomyBiology

Abstract

fetched live from OpenAlex

High carotenoid maize is an ideal source of high value dietary carotenoids, especially lutein and zeaxanthin, in human and animal feed and has been proposed as a feedstock for high carotenoid egg production. A modified analytical method was demonstrated to have reliability, reproducibility, and improved run-time and separation of xanthophylls. This method was used to confirm the localization of carotenoids in endosperm and to determine the effects of drying and storage on carotenoid levels in maize grain. A preliminary trial using room temperature drying indicated that while carotenoid profiles remain stable during storage, carotenoid levels decrease significantly from initial levels between 3 and 6 months of storage, but then remain stable for another year. A more rigorous trial using three drying and storage regimes (freeze-drying and storage at -80 degrees C; room temperature drying and storage; 90 degrees C drying and room temperature storage) indicated that extreme caution is needed to maintain carotenoid levels in maize during handling and storage, but in situations where freeze-drying is not possible, high heat drying is no more detrimental than low heat drying.

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 score0.508
Threshold uncertainty score0.314

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 head0.007
GPT teacher head0.233
Teacher spread0.225 · 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

Citations64
Published2010
Admission routes1
Has abstractyes

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