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Record W4205452095 · doi:10.1093/annweh/wxab101

Measurement of Diacetyl and Related Compounds in Coffee Roasteries and Breweries

2021· article· en· W4205452095 on OpenAlexafffund
Nicholas G. Davey, Larissa C. Richards, Jonathan Davidson, Trevor Michalchuk, Christopher G. Gill, Erik T. Krogh, Christopher D. Simpson

Bibliographic record

VenueAnnals of Work Exposures and Health · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Chemical Sensor Technologies
Canadian institutionsSimon Fraser UniversityUniversity of VictoriaVancouver Island University
FundersNational Institute of Environmental Health SciencesBritish Columbia Knowledge Development FundCanada Foundation for Innovation
KeywordsDiacetylFood scienceEnvironmental healthChemistryMedicine

Abstract

fetched live from OpenAlex

α-Diketones such as diacetyl (2,3-butanedione) and 2,3-pentanedione are generated during the roasting and fermentation of foods and are also used as flavoring compounds. Exposure to these compounds has been associated with obliterative bronchiolitis in workers. We report indoor air concentrations of diacetyl and 2,3-pentanedione, as well as acetoin (3-hydroxy-2-butanone), in several small coffee roasteries and breweries using standard integrated air sampling sorbent tubes followed by gas chromatography tandem mass spectrometry as well as the first use of on-site continuous real-time proton-transfer reaction time-of-flight mass spectrometry (PTR-ToF-MS). Diacetyl and 2,3-pentanedione were detected in most of the sorbent samples at concentrations between 0.02 and 8 ppbv, and in general were higher in coffee roasteries compared with breweries. Three integrated air samples, all from the barista area at one facility, exceeded the NIOSH recommended exposure limit (REL) of 5 ppbv for diacetyl. 2,3-Pentanedione concentrations in these three samples were greater than 50% of its REL, but did not exceed it. Acetoin, a precursor to diacetyl, was also detected at concentrations between 0.03 and 5 ppbv in most sorbent tube samples, with concentrations generally higher in breweries. PTR-ToF-MS measurements exhibited similar trends and provided continuous real-time volatile organic compound data that showed episodic excursions with peak concentrations of diacetyl and 2,3-pentanedione between 15 and 20 ppbv. Examination of the time series data identified specific activities associated with peak diketone emissions, including transfer of freshly roasted coffee beans to the cooling tray, or the opening of a brew kettle. Additional indoor air quality parameters including CO2, NO2, and PM2.5 were also assessed on-site. Airway inflammation was assessed in 19 workers before and after each work shift using online measurements of fractional exhaled nitric oxide (FENO). The pre-shift mean FENO was 3.7 (95% confidence interval: -3.6, 11.0) ppbv higher and the post-shift FENO was 7.1 (-1.9, 16.1) ppbv higher for workers at coffee roasteries compared with breweries. The cross-shift change in FENO was 3.4 (-2.8, 9.6) ppbv higher for workers at coffee roasteries compared with breweries. However, none of these differences were statistically significant, and the cross-shift change in FENO was not statistically different from zero for either group of workers. The findings from this pilot study demonstrate that α-diketones and related compounds are present in the indoor air of both breweries and coffee roasteries and may exceed health protective guidelines in coffee roasteries. Additional studies are required to fully characterize worker exposures in these settings and to identify specific work activities and processes associated with high exposures. Engineering controls, including targeted exhaust ventilation and the use of low-cost sensors, are recommended as an approach to protect workers from exposure to hazardous levels of α-diketones.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.246
Threshold uncertainty score0.350

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.057
GPT teacher head0.288
Teacher spread0.231 · 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

Citations3
Published2021
Admission routes2
Has abstractyes

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