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Record W4391757440 · doi:10.1021/acssensors.3c02512

Method for Glucose and Fructose Quantitation in Beverages Using an Off-the-Shelf Glucose Test Strip

2024· article· en· W4391757440 on OpenAlexafffund
Faisal Hossain, S. A. Doyle, Michael J. Serpe

Bibliographic record

VenueACS Sensors · 2024
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Chemometric Analyses
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta InnovatesMinistry of Advanced Education, Government of AlbertaGrand Challenges CanadaCanada Foundation for InnovationUniversity of Alberta
KeywordsFructoseChemistryFructolysisChromatographyMonosaccharideBiochemistryFood science

Abstract

fetched live from OpenAlex

A method was developed for quantifying both glucose and fructose in solutions and grape juice using commercially available glucose test strips connected to a mini-potentiostat. The first step of this sensing approach involved exposing the sample solution to an Accu Chek Aviva glucose test strip, which allowed for the direct quantitation of glucose. To quantify fructose, the solution was exposed to glucose isomerase, which led to the conversion of glucose to fructose and vice versa until an equilibrium was reached. Once equilibrium was reached, the solution was exposed to another glucose test strip; the signal obtained was shown to be related to the total amounts of glucose and fructose in solution. Finally, fructose was quantified by subtracting the glucose concentration (from the initial measurement) from the total concentration of glucose and fructose (from the second measurement after the reaction with glucose isomerase). The method yielded a limit of detection of 0.047 g L –1 for glucose and 0.49 g L –1 for fructose. Importantly, this method was shown to work well for analyzing glucose and fructose concentration in grape juice, which contains >60 g L –1 glucose and fructose. Since the ratio of glucose and fructose concentration in ripe grapes is close to 1, this method can be used to aid in the determination of grape ripeness to guide harvesting times.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.608

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.039
GPT teacher head0.353
Teacher spread0.314 · 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

Citations4
Published2024
Admission routes2
Has abstractyes

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