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Record W2588524863 · doi:10.1111/1750-3841.13654

Advantage of Eu <sup>3+</sup> ‐Doped Polystyrene Microspheres Compared with Colloidal Gold Used in Immunochromatographic Assays for the Detection of Melamine in Milk

2017· article· en· W2588524863 on OpenAlexafffund
Yiwei Tang, Hong Zhang, Xiuying Liu, Evan Trofimchuk, Shaolong Feng, Tao Ma, Xue Gao, Shubing Fang, Xiaonan Lu

Bibliographic record

VenueJournal of Food Science · 2017
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsMelamineDetection limitColloidal goldPolystyreneICTSColloidMaterials scienceChemistryChromatographyNuclear chemistryNanotechnologyNanoparticleComputer scienceComposite materialPolymer

Abstract

fetched live from OpenAlex

-doped fluorescent microspheres were applied as labels to develop the immunochromatographic strips for detecting melamine in milk. Under the optimized condition, the visual detection limit of colloidal gold-immunochromatographic test strip (ICTS) was 150 μg/L of melamine in phosphate-buffered saline (PBS), although the visual detection limit of fluorescent nanoparticles (FN)-ICTS was 75 μg/L in PBS. As thermal acceleration test, FN-ICTS could be stored at 37 °C for at least 11 d before sample testing, but the color of the lines on colloidal gold-ICTS faded away after 7-d storage. The visual result of FN-ICTS was more stable than that of colloidal gold-ICTS, and the fluorescence intensity of the line on FN-ICTS could be maintained up to 30 d at 22 °C after sample testing. Once the immunochromatographic strips were used to detect melamine in milk, no negative effect of milk components on the performance of FN-ICTS was identified, whereas the performance of colloidal gold-ICTS was significantly influenced by milk matrix.

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)0.001
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 score0.151
Threshold uncertainty score0.258

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.013
GPT teacher head0.238
Teacher spread0.225 · 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

Citations13
Published2017
Admission routes2
Has abstractyes

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