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Record W2897687447 · doi:10.3390/mi9100517

Pseudo-Continuous Flow FTIR System for Glucose, Fructose and Sucrose Identification in Mid-IR Range

2018· article· en· W2897687447 on OpenAlexafffund
Hamza Landari, Mourad Roudjane, Younès Messaddeq, Amine Miled

Bibliographic record

VenueMicromachines · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSpectroscopy Techniques in Biomedical and Chemical Research
Canadian institutionsUniversité Laval
FundersCanada Excellence Research Chairs, Government of CanadaNatural Sciences and Engineering Research Council of CanadaGovernment of CanadaCMC Microsystems
KeywordsFourier transform infrared spectroscopyFructoseSucroseAnalytical Chemistry (journal)Materials scienceAbsorption (acoustics)SugarMicrofluidicsFourier transformChemistryChromatographyOpticsMathematicsNanotechnologyBiochemistryPhysicsComposite material

Abstract

fetched live from OpenAlex

In this paper, we present a new FTIR-based microfluidic system for Glucose, Fructose and Sucrose detection. The proposed microfluidic system is based on a pseudo-continuous flow coupled to a microscope-FTIR instrument. The detection and characterization of sugar samples were performed by recording their absorption spectrum in the wavelength range 700⁻1000 cm - 1 of the Mid-IR region. The proposed pseudo-continuous flow system is designed to improve the uniformity of the sample distribution in the analyzed area versus conventional systems. The obtained results for different sugars concentrations, show a very low measurement error of 4.35% in the absorption peak intensity, which is ten times lower than the error obtained using the conventional measurements.

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 score0.010
Threshold uncertainty score0.515

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 head0.007
GPT teacher head0.294
Teacher spread0.288 · 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

Citations26
Published2018
Admission routes2
Has abstractyes

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