MétaCan
Menu
Back to cohort
Record W2917803946 · doi:10.5539/jfr.v8n2p48

A New Spectrophometric Method For The Analysis Of Fructose, Glucose And Sucrose, Using 2-Thiobarbituric Acid And Zero-Valent Iron Powder (ZVIP)

2019· article· en· W2917803946 on OpenAlexvenueno aff
Karine Assaker, Jamil Rima

Bibliographic record

VenueJournal of Food Research · 2019
Typearticle
Languageen
FieldEngineering
TopicElectrochemical sensors and biosensors
Canadian institutionsnot available
FundersUniversité Libanaise
KeywordsFructoseSucroseChemistryThiobarbituric acidDetection limitAqueous solutionChromatographyCalibration curveCarbohydrateOrganic chemistryAntioxidant

Abstract

fetched live from OpenAlex

A new spectrophotometric method has been developed for the determination of fructose, glucose and sucrose in fruit juices. The method is based on the oxidation of sugars with zero valent iron powder in acidic pH aqueous solution and bubbled with air. Oxidized by-products were complexed with 2-thiobarbituric and monitored by UV-Vis spectroscopy. The recorded spectra are specifically characteristic for fructose, glucose and sucrose respectively. Calibration curves were studied for each compound and the quantitative determination of carbohydrate concentrations was carried out using an internal standard addition method. The average relative standard deviation is 4% and recovery rate range from 98% to 103% for spiked concentrations of 0.05 – 4 mM carbohydrates. The detection limit are 13, 15 and 12 µM for fructose, glucose and sucrose respectively. The oxidation of carbohydrates gives rise to certain by-products, presents a new method of indirect measurement of carbohydrates. This method is applied in the determination of carbohydrates in juices and other foods in a precise, easy and inexpensive way.

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)0.001
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: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.339

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.034
GPT teacher head0.328
Teacher spread0.295 · 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

Citations7
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Food ResearchSame topicElectrochemical sensors and biosensorsFrench-language works237,207