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Record W3010155226 · doi:10.3390/nu12030648

Nutritional, Microbial, and Allergenic Changes during the Fermentation of Cashew ‘Cheese’ Product Using a Quinoa-Based Rejuvelac Starter Culture

2020· article· en· W3010155226 on OpenAlexafffund
Jennifer M. Chen, Kait F. Al, Laura J. Craven, Shannon Seney, Margaret Coons, Heather McCormick, Gregor Reid, Colleen O’Connor, Jeremy P. Burton

Bibliographic record

VenueNutrients · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProbiotics and Fermented Foods
Canadian institutionsNetwork for Business SustainabilityLawson Health Research InstituteWestern University
FundersNatural Sciences and Engineering Research Council of CanadaW. Garfield Weston FoundationOntario Centres of Excellence
KeywordsStarterWeissellaFermentationFood scienceAnacardiumPediococcusLactococcusBiologyBiotechnologyLeuconostocLactobacillusLactococcus lactisLactic acidBacteriaHorticulture

Abstract

fetched live from OpenAlex

Fermentation has been applied to a multitude of food types for preservation and product enhancing characteristics. Interest in the microbiome and healthy foods makes it important to understand the microbial processes involved in fermentation. This is particularly the case for products such as fermented cashew (Anacardium occidentale). We hereby describe the characterisation of cashew samples throughout an entire fermentation production process, starting at the quinoa starter inoculum (rejuvelac). The viable bacterial count was 108 -109 colony forming units/g. The nutritional composition changed marginally with regards to fats, carbohydrates, vitamins, and minerals. The rejuvelac starter culture was predominated by Pediococcus and Weissella genera. The ‘brie’ and ‘blue’ cashew products became dominated by Lactococcus, Pediococcus, and Weissella genera as the fermentation progressed. Cashew allergenicity was found to significantly decrease with fermentation of all the end-product types. For consumers concerned about allergic reactions to cashew nuts, these results suggested that a safer option is for products to be made by fermentation.

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 score0.428
Threshold uncertainty score0.165

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 head0.033
GPT teacher head0.228
Teacher spread0.195 · 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".

Quick stats

Citations53
Published2020
Admission routes2
Has abstractyes

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