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Record W4406098375 · doi:10.3390/mps8010005

Fluorescence Analysis of Quinine in Commercial Tonic Waters

2025· article· en· W4406098375 on OpenAlex
Artturi Harcher, Isabel Gibbs, J. Ryan Shaw, Julia Perschbacher, Lindsay Replogle, Michaela Eide, Morgan Grissom, Oliver O’Neal, Quan Nguyen, M. L. Hunnicutt, Roaa Mahmoud, Soma Dhakal

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueMethods and Protocols · 2025
Typearticle
Languageen
FieldChemistry
TopicVarious Chemistry Research Topics
Canadian institutionsnot available
FundersVirginia Commonwealth University
KeywordsQuinineTonic (physiology)ChemistryReproducibilityFood scienceMedicineChromatographyMalariaInternal medicine

Abstract

fetched live from OpenAlex

Quinine is known for treating malaria, muscle cramps, and, more recently, has been used as an additive in tonic water due to its bitter taste. However, it was shown that excessive consumption of quinine can have severe side effects on health. In this work, we utilized fluorescence spectroscopy to measure the concentration of quinine in commercial tonic water samples. An external standard method was used to calculate the concentrations of quinine in two commercially available tonic water brands, namely Canada Dry and Schweppes, and compare them to the maximum allowable concentration of quinine in beverages. Upon analysis of the data collected by five different groups, the levels of quinine were found to be above the average concentration in most commercial tonic water samples, but below the maximum permitted concentration. Moreover, the five replicate sets of data demonstrated high reproducibility of the method employed in this study. The simple yet instructive protocol that we developed can be adapted to determine the concentration of other fluorescent compounds in foods and beverages. Further, the presented method and detailed protocol can be easily adopted for undergraduate labs and in chemical education.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.440
Threshold uncertainty score0.358

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 head0.050
GPT teacher head0.479
Teacher spread0.429 · 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