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Record W2103978604 · doi:10.5539/jfr.v2n3p55

Importance of Espresso Coffee Machine Parameters on the Extraction of Chlorogenic Acids in a Certified Italian Espresso by Using SPE-HPLC-DAD

2013· article· en· W2103978604 on OpenAlexvenueno aff
Giovanni Caprioli, Manuela Cortese, Luigi Odello, Massimo Ricciutelli, Gianni Sagratini, Giacomo Tomassoni, Elisabetta Torregiani, Sauro Vittori

Bibliographic record

VenueJournal of Food Research · 2013
Typearticle
Languageen
FieldMedicine
TopicCoffee research and impacts
Canadian institutionsnot available
Fundersnot available
KeywordsChlorogenic acidHigh-performance liquid chromatographyChemistryGreen coffeeChromatographyCoffee groundsFood science

Abstract

fetched live from OpenAlex

<p>Chlorogenic acids (CGA) are a group of phenolic acid derivatives, which are commonly found in coffee at concentrations reaching 0.1-0.2%. A method based on high performance liquid chromatography-diode array detector (HPLC-DAD) is proposed for the simultaneous determination of three chlorogenic acids, i.e. 3-<em>O</em>-caffeoylquinic acid (3-CQA), 5-<em>O</em>-caffeoylquinic acid (5-CQA) and 3,5-di-O-caffeoylquinic acid (3,5-diCQA), in time portions of espresso coffee. Two different espresso coffee machines working with different pressure and temperature curves, and two different blends (i.e. Arabica and Robusta) were used. The method presents good linearities (correlation coefficient greater than 0.99) and recoveries (in the range 67-99%) for the 3 chlorogenic acids. The concentration of total CGAs in a cup of Certified Italian Espresso ranged from 1522.5 to 2223.4 mg kg<sup>-1</sup> and CGA isomer contents were, in decreasing order, 5-CQA > 3-CQA > 3,5-diCQA.</p> <p>The concentration of total chlorogenic acids was higher in Espresso coffee (EC) prepared with Aurelia machine rather than with Leva; Arabica blend possessed higher level ot total chlorogenic acids than Robusta samples. <strong></strong></p>

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)0.003
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 score0.056
Threshold uncertainty score0.647

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.165
GPT teacher head0.415
Teacher spread0.250 · 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

Citations24
Published2013
Admission routes1
Has abstractyes

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