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Record W4385544220 · doi:10.3390/su151511961

Mushroom Biomass Waste Is a Source of the Antioxidants Ergothioneine and Glutathione

2023· article· en· W4385544220 on OpenAlexafffund
Dhanya Sivakumar, Gale G. Bozzo

Bibliographic record

VenueSustainability · 2023
Typearticle
Languageen
FieldMedicine
TopicFungal Biology and Applications
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsErgothioneineMushroomChemistryOysterBiomass (ecology)GlutathioneFood scienceAgaricus bisporusWaste managementAntioxidantBiochemistryBiologyAgronomyEnzymeEcology

Abstract

fetched live from OpenAlex

Low-grade foodstuffs and unharvested materials from farms contribute a sizable amount of the waste that is disposed to landfills. Mushroom farms also contribute to this problem, as unmarketable fruiting bodies or parts of them are discarded in the waste stream. To limit the proportion of mushroom biomass waste that is deposited to landfills, we assessed whether culls of oyster and shiitake mushrooms and white button mushroom stem waste contain the antioxidants ergothioneine and glutathione. Enzyme-coupled spectrophotometric assays were used to assess the concentrations of glutathione (GSH) and its oxidized form glutathione disulfide in mushroom biomass waste. Ergothioneine analysis was performed with a high-performance liquid chromatography analysis. Most of the biomass waste contained ergothioneine and GSH concentrations that were on par with each one of the fresh mushrooms. Conversely, white button mushroom stem waste contained 77% less GSH than market-ready mushrooms. Finally, as a proof-of-concept cation exchange column chromatography was used to capture ergothioneine from oyster mushroom culls. This strategy has the potential to produce gram quantities of high value ergothioneine per tonne of mushroom biomass waste. These findings provide a strategy for the valorization of mushroom biomass waste and its diversion from landfills.

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)0.001
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 score0.177
Threshold uncertainty score0.206

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.277
Teacher spread0.268 · 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

Citations12
Published2023
Admission routes2
Has abstractyes

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