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Record W2118546661 · doi:10.1021/jf000157a

A Capillary Electrophoresis Laser-Induced Fluorescence Method for Analysis of Potato Glycoalkaloids Based on a Solution-Phase Immunoassay. 2. Performance Evaluation

2000· article· en· W2118546661 on OpenAlexaff
D. Driedger, Raynald J. LeBlanc, Eileen L. LeBlanc, Peter Sporns

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2000
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPotato Plant Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCapillary electrophoresisChromatographyChemistryImmunoassayCalibration curveLaser-induced fluorescenceFluorescenceElectrophoresisQuantitative analysis (chemistry)Detection limitHigh-performance liquid chromatographyAnalytical Chemistry (journal)Biology

Abstract

fetched live from OpenAlex

Glycoalkaloids (GAs) occur naturally in potatoes and are toxic to humans and animals. The objective of the present study was to evaluate the performance of a solution-phase immunoassay coupled to capillary electrophoresis with laser-induced fluorescence (CE-LIF) detection for the determination of total glycoalkaloids in potatoes. The immunoassay was based on a competition between potato glycoalkaloids and fluorescently labeled solanidine. Reaction products were separated in the capillary zone electrophoresis mode. A calibration curve of signal vs log[GA] was linear from 50 to 400 nM. The CV for duplicate and day-to-day analyses averaged 5.7% and 12%, respectively. Spike recoveries ranged from 85 to 97% for spike levels ranging from 43 to 170 microg/g fresh potato. Potato samples with GA concentrations ranging from <40 to >200 microg/g were successfully analyzed, indicating that immuno-CE-LIF is a rapid alternative to traditional ELISA and HPLC methods.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.024
GPT teacher head0.281
Teacher spread0.257 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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

Citations18
Published2000
Admission routes1
Has abstractyes

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