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Record W3011273160 · doi:10.1111/ajgw.12425

The colorimetric determination of copper in wine: total copper

2020· article· en· W3011273160 on OpenAlexfundno aff
Nikolaos Kontoudakis, Mark E. Smith, Paul A. Smith, Eric Wilkes, Andrew C. Clark

Bibliographic record

VenueAustralian Journal of Grape and Wine Research · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFermentation and Sensory Analysis
Canadian institutionsnot available
FundersCharles Sturt UniversityNSW Wine Industry AssociationWine AustraliaAustralian GovernmentAlberta Water Research Institute
KeywordsWineCopperChemistryRepeatabilityReagentColorimetryWhite WineChromatographyDetection limitColorimetric analysisFood science

Abstract

fetched live from OpenAlex

Background and Aims The total concentration of copper (Cu) in wine cannot be routinely measured without relatively expensive equipment, reagents and/or without labour-intensive sample pretreatment. The following research describes an approach for the colorimetric measurement of total Cu in wine utilising a spectrophotometer and relatively cheap reagents. Method and Results The proposed method is based on using bicinchoninic acid as the complexing chromophore for Cu. Copper was measured directly and rapidly in white wine, while a digestion of red wine was required. A key step for the analysis in the non-digested white wine matrix was the addition of silver(I) to aid the dissociation of Cu in the wine from sulfide. For red and white wines, the limit of detection was 0.02 mg/L Cu, and the repeatability was 3 ± 1%. The accuracy of the method was assessed in terms of recovery experiments (102 ± 3%, n = 4), and also by comparing total Cu concentration in 84 wines determined by the colorimetric method with that obtained by inductively coupled plasma with optical emission spectroscopy (R2 = 0.95). Conclusions The colorimetric determination of total Cu was successfully optimised and validated in white and red wines. Significance of the Study The methodology will allow wineries and researchers to routinely measure total Cu concentration in wine with a spectrophotometer.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.873
Threshold uncertainty score0.118

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.121
GPT teacher head0.354
Teacher spread0.233 · 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

Citations20
Published2020
Admission routes1
Has abstractyes

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