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Record W2312486907 · doi:10.1021/jf4022916

Seed Coat Removal Improves Iron Bioavailability in Cooked Lentils: Studies Using an in Vitro Digestion/Caco-2 Cell Culture Model

2013· article· en· W2312486907 on OpenAlex
Diane M. DellaValle, Albert Vandenberg, Raymond P. Glahn

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Micronutrient Interactions and Effects
Canadian institutionsUniversity of Saskatchewan
FundersU.S. Department of Agriculture
KeywordsBioavailabilityAntinutrientLegumeFood sciencePhytic acidChemistryCoatAgronomyBiologyPharmacology

Abstract

fetched live from OpenAlex

In this study we examined the range of Fe concentration and relative Fe bioavailability of 24 varieties of cooked lentils, as well as the impact of seed coat removal on Fe nutritional as well as antinutrient properties. Relative Fe bioavailability was assessed by the in vitro/Caco-2 cell culture method. While the Fe concentration of the whole lentil was moderately high (72.8 ± 10.8 μg/g, n = 24), the relative Fe bioavailability was moderate (2.4 ± 1.0 ng of ferritin/mg of protein). Although removing the seed coat reduced the Fe concentration by an average of 16.4 ± 9.4 μg/g, the bioavailability was significantly improved (+5.3 ± 2.2 ng of ferritin/mg of protein; p < 0.001), and the phytic acid concentration was reduced by 7% (p = 0.04). Like most legume seeds, the lentil seed coat contains a range of polyphenols known to inhibit Fe bioavailability. Thus, along with breeding for high Fe concentration and bioavailability (i.e., biofortification), seed coat removal appears to be a practical way to improve Fe bioavailability of the lentil.

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.

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 score0.061
Threshold uncertainty score0.261

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 communication0.0000.001
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 head0.021
GPT teacher head0.225
Teacher spread0.203 · 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