Evidence map›Paper›PMID 30126248›Full record

ArticleSensors (Basel, Switzerland)2018

Direct Detection of Toxic Contaminants in Minimally Processed Food Products Using Dendritic Surface-Enhanced Raman Scattering Substrates.

Hannah Dies, Maria Siampani, Carlos Escobedo, Aristides Docoslis

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
5.5field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 53 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 2 countries.

Hannah DiesDepartment of Chemical Engineering, Queen's University, Kingston, ON K7L 3N6, Canada. 9hed@queensu.ca.
Maria SiampaniDepartment of Chemistry, University of Ioannina, GR-54110 Ioannina, Greece. mariasiampani16@gmail.com.
Carlos EscobedoDepartment of Chemical Engineering, Queen's University, Kingston, ON K7L 3N6, Canada. ce32@queensu.ca.ORCID 0000-0002-7832-166X
Aristides DocoslisDepartment of Chemical Engineering, Queen's University, Kingston, ON K7L 3N6, Canada. docoslis@queensu.ca.ORCID 0000-0001-5284-0181
Queen's University · CAUniversity of Ioannina · GR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We present a method for the surface-enhanced Raman scattering (SERS)-based detection of toxic contaminants in minimally processed liquid food products, through the use of a dendritic silver nanostructure, produced through electrokinetic assembly of nanoparticles from solution. The dendritic nanostructure is produced on the surface of a microelectrode chip, connected to an AC field with an imposed DC bias. We apply this chip for the detection of thiram, a toxic fruit pesticide, in apple juice, to a limit of detection of 115 ppb, with no sample preprocessing. We also apply the chip for the detection of melamine, a toxic contaminant/food additive, to a limit of detection of 1.5 ppm in milk and 105 ppb in infant formula. All the reported limits of detection are below the recommended safe limits in food products, rendering this technique useful as a screening method to identify liquid food with hazardous amounts of toxic contaminants.

Indexed as

MalusAnimalsFood ContaminationFruit and Vegetable JuicesHumansLimit of DetectionMetal NanoparticlesMilkSilverSpectrum Analysis, RamanThiramTriazinesmelamineSilverThiramTriazinesfood quality monitoringmelaminenanoparticlespesticidessurface-enhanced Raman scatteringthiram

Identifiers

PMID30126248
PMCPMC6111781
OpenAlexW2888679949

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.