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Effect of solid-state fermentation on select antinutrients and protein digestibility of cold-pressed and hexane-extracted canola meals

2022· preprint· en· W4312012159 on OpenAlexafffund
Chenghao Li, Dai Shi, Andrea K. Stone, Janitha P.D. Wanasundara, Takuji Tanaka, Michael T. Nickerson

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicPhytase and its Applications
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Saskatchewan
FundersMinistry of Agriculture - Saskatchewan
KeywordsCanolaFermentationFood scienceAspergillus oryzaePhytic acidSolid-state fermentationAspergillus nigerChemistryMeal

Abstract

fetched live from OpenAlex

In this study, the effects of solid-state fermentation (SSF), including strain ( Aspergillus niger NRRL 334 and A. oryzae NRRL 5590) and fermentation time (24, 48, and 72 h) on the nutritional value of cold-pressed (CP) and hexane-extracted (HE) canola meals were examined. SSF increased the protein content of both types of meals (from ~36 to ~40%) while reducing the oil content of CP meals (from ~12 to 9%). There was a significant reduction (~80%) in the phytic acid content of both types of meals after fermentation using either fungi. Overall, fermented samples showed a decrease in the total phenolic content from 2.7-3.1 to ~1.0 mg gallic acid equivalents (GAE)/g DM (a ~65% reduction), of which specifically the HE meal fermented with A. niger sample had the greatest decrease from 3.1 to 0.6 mg GAE/g DM (~81% reduction). Seventy-two hours of fermentation decreased the in vitro protein digestibility (IVPD) of the meals. In contrast, a shorter fermentation time (24 h) increased the IVPD as compared to the controls (from ~73% to 77-81%), with the exception of the CP meal fermented with A. niger which had decreased IVPD at all fermentation times. Overall, the changes indicate that SSF using A. niger or A. oryzae can be useful to positively modify the composition of different canola meals and improve their nutritional value by significantly increasing the protein content, decreasing the levels of antinutrients, while only slightly reducing IVPD.

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 score0.365
Threshold uncertainty score0.282

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 head0.013
GPT teacher head0.276
Teacher spread0.263 · 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".

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Citations4
Published2022
Admission routes2
Has abstractyes

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