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Record W1985165150 · doi:10.1021/jf034118l

Comparison of MALDI-TOF Mass Spectrometric to Enzyme Colorimetric Quantification of Glucose from Enzyme-Hydrolyzed Starch

2003· article· en· W1985165150 on OpenAlexaff
G. A. Grant, Suzanne Frison, Judy Yeung, Thava Vasanthan, Peter Sporns

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2003
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsStarchChemistryChromatographyMass spectrometryHydrolysisMonosaccharideEnzymatic hydrolysisSample preparationFood scienceBiochemistry

Abstract

fetched live from OpenAlex

Successful quantification of the glucose produced by enzyme hydrolysis of starch was achieved by a matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) protocol, using sorbitol as an internal standard. The starch contents measured by MALDI-TOF MS of corn starch, fiber-enriched oat flour derivatives, oat and barley flours, and barley flour/corn starch composites were evaluated in comparison to a widely accepted and validated method of starch determination, which relies on enzyme colorimetry (EC). The average starch content measured in a series of corn starch samples of different masses was 93 and 101% for EC and MALDI-TOF MS, respectively, values that represent the estimated purity of the sample. There was an agreement of 99% between the starch contents determined by the two analytical methods for complex flour-derived samples. Starch values estimated by MALDI-TOF MS consistently showed a greater degree of variability than those determined by EC, but this limitation was readily compensated by rapid acquisition of multiple mass spectra. This study is the first to report the quantification of glucose by MALDI-TOF MS, and it offers new perspectives into the potential utility of MALDI-TOF MS as a definitive tool for monosaccharide analysis and rapid starch determination in complex samples.

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 score0.006
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.021
GPT teacher head0.269
Teacher spread0.248 · 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

Citations28
Published2003
Admission routes1
Has abstractyes

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