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Record W3102797755 · doi:10.1002/cjce.23928

Elimination of tryptamines from green coffee by supercritical <scp>CO<sub>2</sub></scp> extraction

2020· article· en· W3102797755 on OpenAlexvenueno aff
Lucia Baldino, Mariarosa Scognamiglio, Ernesto Reverchon

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2020
Typearticle
Languageen
FieldMedicine
TopicCoffee research and impacts
Canadian institutionsnot available
Fundersnot available
KeywordsTryptamineChemistrySupercritical fluidTryptaminesSupercritical fluid extractionResidue (chemistry)ChromatographySelectivityGreen coffeeExtraction (chemistry)Food scienceOrganic chemistryBiochemistryCatalysis

Abstract

fetched live from OpenAlex

Abstract Supercritical CO 2 (SC‐CO 2 ) extraction is commonly used to eliminate caffeine from coffee beans. In this work, the feasibility of tryptamine elimination is considered as a further objective of the process. SC‐CO 2 extraction process parameters (eg, pressure, CO 2 flow rate, water content) were studied to obtain tryptamine reduction, starting from those used in supercritical decaffeination. A good compromise, in terms of tryptamine residue in coffee beans, and process feasibility and selectivity, was found operating at 280 bar and 0.8 kg/h CO 2 , at a starting H 2 O content in coffee beans of 20% w/w. Using these process conditions, a tryptamine residue of 218 ppm (ppm) was measured in coffee beans after 18 hours of processing. A negligible effect on process selectivity and tryptamine yield was obtained by changing the CO 2 flow rate and the initial water content. However, working at an initial water content of 30% w/w and using wet CO 2 (CO 2 plus 3% w/w water), a tryptamine residue of 107 ppm in coffee beans was obtained, but industrial complexity and costs increased.

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)0.003
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 score0.007
Threshold uncertainty score0.351

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
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 integrity0.0000.001
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 head0.017
GPT teacher head0.245
Teacher spread0.227 · 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

Citations9
Published2020
Admission routes1
Has abstractyes

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