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Effect of infrared heating on the properties of legume seeds*

2001· article· en· W1989752790 on OpenAlexafffund
Oladiran Fasina, Bob Tyler, Mark Pickard, Guohua Zheng, Ning Wang

Bibliographic record

VenueInternational Journal of Food Science & Technology · 2001
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMagnetic and Electromagnetic Effects
Canadian institutionsUniversity of Saskatchewan
FundersNational Research Council Canada
KeywordsLegumeInfraredChemistryStarchFood scienceLeaching (pedology)Infrared heaterAgronomyHorticultureMaterials scienceBiologySoil water

Abstract

fetched live from OpenAlex

Summary Five legume seeds (kidney beans, green peas, black beans, lentil and pinto beans) were heated by infrared to a surface temperature of 140 °C. The changes in chemical composition, physical, mechanical and functional properties of the processed seeds were measured and compared to those of the raw seeds. Significant changes in the properties of the seeds in terms of increased volume, lower rupture point and toughness, higher water uptake and higher leaching losses (when the seeds were soaked in water) were obtained. The changes in the physical and mechanical properties were attributed to possible cracking of the seed. Even though trypsin inhibitor activity was reduced, infrared heating did not significantly affect the starch and protein components of the seeds. The functional characteristics of flour from the infrared‐heated seeds were superior to those of flour from untreated seeds.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.247
Teacher spread0.240 · 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 source (direct Gemma or distilled Codex), 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

Citations112
Published2001
Admission routes2
Has abstractyes

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