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Record W2795918216 · doi:10.5740/jaoacint.17-0491

Direct Comparison of Cavity Ring Down Spectrometry and Isotope Ratio Mass Spectrometry for Detection of Sugar Adulteration in Honey Samples

2018· article· en· W2795918216 on OpenAlexaff
Madhavi Mantha, John R Urban, William A. Mark, Anatoly Chernyshev, Kevin M. Kubachka

Bibliographic record

VenueJournal of AOAC International · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBee Products Chemical Analysis
Canadian institutionsUniversity of Waterloo
FundersNational Institute of Standards and Technology
KeywordsIsotope-ratio mass spectrometryHigh-fructose corn syrupChemistryMass spectrometrySugarChromatographyFood science

Abstract

fetched live from OpenAlex

In the last several years, economically motivated adulteration (EMA) of foods including honey has received increased attention. The addition of inexpensive sweeteners such as high fructose corn syrup or cane sugar to honey is still encountered despite scientific methods that can routinely detect this type of adulteration. The standard method for detection of these adulterants utilizes isotope ratio mass spectrometry (IRMS); however, this technique requires an elevated degree of technical knowledge for operation as well as a high cost for purchase and maintenance. Cavity ring down spectroscopy (CRDS) has demonstrated potential for this type of analysis and is less expensive with simpler operation. This study evaluates CRDS for the detection of low-cost sweeteners added to honey and compares the performance of CRDS to IRMS. Several honey samples were analyzed, and the advantages and limitations specific to CRDS were evaluated. Overall, the results indicate that CRDS provides a performance comparable to the benchmark technique IRMS for EMA honey analysis.

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 score0.079
Threshold uncertainty score0.151

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 head0.021
GPT teacher head0.268
Teacher spread0.246 · 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

Citations19
Published2018
Admission routes1
Has abstractyes

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