MétaCan
Menu
Back to cohort

Evaluation of the thermal stability of bioactive compounds in coffee beans and their fractions modified in the roasting process

2022· article· en· W4223455068 on OpenAlex
Joanna Grzelczyk, Petr Fiurasek, Ashok Kakkar, Grażyna Budryn

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFood Chemistry · 2022
Typearticle
Languageen
FieldMedicine
TopicCoffee research and impacts
Canadian institutionsMcGill UniversityRegroupement Québécois sur les Matériaux de Pointe
FundersPolitechnika Lódzka
KeywordsRoastingCaffeineFood scienceChemistryFlavorGreen coffeeCoffee groundsDegradation (telecommunications)AntioxidantThermal stabilityOrganic chemistryBiology

Abstract

fetched live from OpenAlex

Coffee is used as flavor or health-promoting additive in thermally processed food. In this study, ground coffee and freeze-dried coffee extracts were evaluated in terms of their thermal stabilities, and for the first time heat resistance of fractions (mono-, dichlorogenic acids and caffeine) with different roasting levels was evaluated. It observed that the degradation of green coffee bean ingredients began at 150 °C, and for the re-heated light and dark roasted, in the range of 171-188 °C. The lyophilized extracts were more stable and their degradation began around 160 °C. However, with the re-treatment (cooking, baking, frying) of the coffee extract fractions, the degradation of the monochlorogenic acids commenced at 114 °C, while for dichlorogenics at 108 °C and caffeine at 146 °C. Monochlorogenic acids in Robusta coffee showed high antioxidant activity (55-70%) and the highest content of fiber (13-17%). Coffee could be used to fortify food.

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.

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)0.001
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 score0.196
Threshold uncertainty score0.149

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.086
GPT teacher head0.338
Teacher spread0.252 · 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