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Record W3110260743 · doi:10.1177/1082013220973804

The levels of bioactive compounds found in raw and cooked Canadian pulses

2020· article· en· W3110260743 on OpenAlexafffundabout
AK Stone, KN Waelchli, Burcu Çabuk, TC McIntosh, Janitha P.D. Wanasundara, SD Arntfield, MT Nickerson

Bibliographic record

VenueFood Science and Technology International · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetic and Environmental Crop Studies
Canadian institutionsUniversity of ManitobaAgriculture and Agri-Food CanadaUniversity of Saskatchewan
FundersAgriculture and Agri-Food CanadaSaskatchewan Pulse GrowersMinistry of Agriculture - Saskatchewan
KeywordsStachyoseRaffinosePolyphenolFood scienceChemistryVicia fabaSaponinOligosaccharideLegumeBotanyBiologyAntioxidantBiochemistrySucrose

Abstract

fetched live from OpenAlex

The effect of cooking on the levels of bioactive compounds (oligosaccharides, polyphenols and saponins, and vicine/convicine for faba bean only) were examined in a wide range of Canadian pulses. The total oligosaccharide concentrations were reduced ∼40% for chickpea, 11-81% for lentils, 41-43% for faba beans, 10-51% for beans, and 20-44% for peas. Individual oligosaccharides, raffinose, ciceritol, stachyose and verbascose, increased or decreased in the cooked samples depending on each pulse sample. Cooking reduced the total polyphenol content by 13-25% for chickpeas, 0-83% for lentils, 47-54% for faba beans, 47-54% for beans, and 48-70% for peas. And, the total saponin concentrations were reduced by 11-30% for chickpeas, 0-40% for lentils, 32-46% for beans, 14-30% for peas and increased by 8-26% in faba bean. The vicine and convicine levels in faba bean were reduced by 26-38% with cooking. The reduction in bioactive compounds after cooking depended on the specific compound and specific type of pulse. This large analyses of 20 different pulse samples allows for comparison between and within different types of pulses.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.329
Threshold uncertainty score0.994

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 studies0.0000.001
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 head0.025
GPT teacher head0.209
Teacher spread0.184 · 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

Citations23
Published2020
Admission routes3
Has abstractyes

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