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Record W2530948386 · doi:10.21273/hortsci.35.3.398c

058 Variation in β-carotene and Total Carotenoid Content in Capsicum Germplasm

2000· article· en· W2530948386 on OpenAlexaff
Marisa M. Wall, Cynthia A. Waddell, Paul W. Bosland, Stephanie Walker

Bibliographic record

VenueHortScience · 2000
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Practices and Plant Genetics
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsCarotenoidGermplasmCaroteneDry weightHorticultureBiologyCultivarbeta-CaroteneFood scienceBotanyVitamin

Abstract

fetched live from OpenAlex

The β-carotene and total carotenoid content of different Capsicum fruit types and species were analyzed using HPLC. This information is useful for breeding high carotenoid chiles (New Mexican type) for the food industry, and also provides nutritional data for the range of fruit types within the Capsicum genus. Fresh fruit from 25 accessions and dried fruit from 39 accessions were evaluated in 1996 and 1997. β-carotene levels varied from 0 to 16.6 mg/100 g fresh weight, and carotenoid levels were from 0.1 to 89.6 mg/100 g in red ripe fruit in 1996. The range of values for β-carotene was similar in 1997, but a wider range in total carotenoids (0.4 to 117.3 mg/100 g fresh weight) was observed. Fresh fruit (100 g) of the cultivars `Greenleaf Tabasco', `Pulla', `Guajillo', `NuMex Conquistador', `Ring-O-Fire', and `Thai Dragon' contained greater amounts of β-carotene than the RDA for vitamin A for the average adult. For dried Capsicum entries, New Mexican, aji, pasilla, ancho, and guajillo types had the highest levels of β-carotene. In 1996, β-carotene levels among the dried Capsicum germplasm ranged from 0 to 739.2 μg/g dry weight, and carotenoid levels were from 21.3 to 6,225.9 μg/g. Values were higher in 1997, and ranged from 23.7 to 1,198.1 μg/g dry weight for β-carotene and from 76.9 to 10,120.6 μg/g for total carotenoids. A pasilla type ( C. annuum ) had the highest total carotenoid content among the dried entries in both years.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.894
Threshold uncertainty score0.741

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 head0.189
Teacher spread0.167 · 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 designObservational
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

Citations0
Published2000
Admission routes1
Has abstractyes

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