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Development of quantum dot-linked immunosorbent assay (QLISA) and ELISA for the detection of sunset yellow in foods and beverages

2022· article· en· W4221115288 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFood Chemistry · 2022
Typearticle
Languageen
FieldChemistry
TopicDye analysis and toxicity
Canadian institutionsUniversity of Guelph
FundersNational Natural Science Foundation of China
KeywordsPhotobleachingChemistryMonoclonal antibodyQuantum dotIC50Detection limitChromatographyFood scienceFood productsMolecular biologyFluorescenceAntibodyBiochemistryNanotechnologyBiologyIn vitroMaterials sciencePhysicsImmunology

Abstract

fetched live from OpenAlex

Sunset yellow (SY) is widely used as food colorant. Excess and illegal use of SY could pose potential health risk. Quantum dots have been successfully used in biological research due to the high photoluminescence and high resistance to photobleaching. To analyze SY efficiently, quantum dot-linked immunosorbent assay (QLISA) and enzyme-linked immunosorbent assay (ic-ELISA) were developed on the basis of generated monoclonal antibody. A carboxyl group was introduced to SY and coupled with carrier proteins to synthesize artificial antigen. Under the optimal conditions, inhibitory concentrations (IC50) of SY were 1.9 ng/mL (ic-ELISA) and 3.4 ng/mL (QLISA); the limits of detection (LODs) were 0.2 ng/mL (ic-ELISA) and 1.0 ng/mL (QLISA), respectively. Cross-reactivities of the mAb toward eight kinds of analogues were<0.01%. The recovery rates in spiked foods and beverages were 75.6%∼120.1% (ic-ELISA) and 74.0%∼114.1% (QLISA), respectively.

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.

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 score0.004
Threshold uncertainty score0.455

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 head0.017
GPT teacher head0.233
Teacher spread0.216 · 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