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Novel continuous cold brewing method for coffee using a combined screw extraction and mechanical expression approach

2024· article· en· W4400896085 on OpenAlexafffund
Matthew Zwicker, John W. Phillips, Loong‐Tak Lim

Bibliographic record

VenueJournal of Food Engineering · 2024
Typearticle
Languageen
FieldMedicine
TopicCoffee research and impacts
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaOntario Centres of Excellence
KeywordsBrewingGrindChemistryCaffeineExtraction (chemistry)PolyphenolTitratable acidFood scienceParticle sizeChromatographyTotal dissolved solidsPulp and paper industryFermentationMaterials scienceEnvironmental scienceEnvironmental engineeringMetallurgyEngineeringBiochemistry

Abstract

fetched live from OpenAlex

This study investigated a novel continuous cold brewing method accomplished through a combined countercurrent single-screw extraction process and mechanical expression approach. The effects of four processing parameters (grind size (GS), motor rotation speed (RPM), press cone piston pressure (PCPP), and dry unbrewed coffee grounds feed rate (GFR)) on the particle size of coffee grounds, the mass balance of coffee grounds and water/brew in the brewer, and the physicochemical quality (total dissolved solids (TDS), extraction yield (EY), titratable acidity (TA), total polyphenols (TPP), pH, caffeine and 5-caffeoylquinic (5-CQA) concentration) of the brew were evaluated. The processing parameters significantly affected the mechanical interactions between the coffee grounds and brewer, resulting in changes in the packing density of coffee grounds, the moisture content of the spent coffee grounds, the extent of grind size reduction, and the amount of fines in the brew. These changes affected the physicochemical quality of the resultant brew leading to significant differences in TA, TPP, and 5-CQA, which were positively correlated with TDS.

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)0.001
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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.656
Threshold uncertainty score0.385

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.055
GPT teacher head0.348
Teacher spread0.293 · 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
GenreMethods

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

Citations4
Published2024
Admission routes2
Has abstractyes

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