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Record W1947780535 · doi:10.1094/cchem-04-15-0065-r

Changes in Semolina Yellow Pigment Content and Carotenoid Composition During Pasta Processing

2015· article· en· W1947780535 on OpenAlexaff
Constance Chiremba, Curtis Pozniak, Bin Xiao Fu

Bibliographic record

VenueCereal Chemistry · 2015
Typearticle
Languageen
FieldMedicine
TopicAntioxidant Activity and Oxidative Stress
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCarotenoidPigmentLuteinChemistryFood scienceComposition (language)GenotypeExtrusionBotanyGeneBiologyBiochemistry

Abstract

fetched live from OpenAlex

Changes in total yellow pigment (TYP) content and carotenoid composition were examined at different stages of pasta processing. Semolina samples were milled from durum genotypes with and without the Lpx‐B1.1 gene deletion and then processed into dry pasta. Significant pigment loss (12.8–15.3%) based on TYP content was observed from semolina to dough in genotypes without the gene deletion. Such loss remained low (2.0–2.8%) for genotypes with the Lpx‐B1.1 gene deletion. Extrusion and drying processes did not result in substantial pigment loss. The overall pigment loss (from semolina to dried pasta) of genotypes with the gene deletion was 9.1–12.8%, in comparison with 19.0–21.7% in genotypes without the deletion. Changes in carotenoids examined by ultra‐performance liquid chromatography showed that lutein decreased gradually from representing 80% of total carotenoids to 70% of total carotenoids during pasta processing. The reduction of lutein was mostly during dough mixing, with a decrease of 16.7% in genotypes with the Lpx‐B1.1 deletion and 27.8% in genotypes without the deletion. Minor carotenoids increased during pasta drying, possibly at the expense of lutein. Results of this study showed that although breeding for elevated yellow pigments is the key, pasta color can be further improved by reducing pigment losses at different stages of pasta processing through selection of genotypes with Lpx‐B1.1 deletion and applying a vacuum during mixing and extrusion processes.

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 score0.055
Threshold uncertainty score0.413

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 head0.048
GPT teacher head0.266
Teacher spread0.218 · 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

Citations9
Published2015
Admission routes1
Has abstractyes

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