Evidence map›Paper›PMID 36351813›Full record

ArticleSe pu = Chinese journal of chromatography2022

[Surface-enhanced Raman detection of deoxynivalenol allenol in agricultural products].

Mingming Chen, Bihang Su, Jianli Huang, Fengfu Fu, Yongqiang Dong

Open access · greenAbstract readEnglish Abstract
In one paragraph

Article in Se pu = Chinese journal of chromatography, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.6field-weighted citation impact, top 23% 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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. Review
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

5 authors at 1 institution in 1 country.

Mingming ChenMOE Key Laboratory for Analytical Science of Food Safety and Biology, Fujian Provincial Key Laboratory of Analysis and Detection Technology for Food Safety, College of Chemistry, Fuzhou University, Fuzhou 350108, China.
Bihang SuMOE Key Laboratory for Analytical Science of Food Safety and Biology, Fujian Provincial Key Laboratory of Analysis and Detection Technology for Food Safety, College of Chemistry, Fuzhou University, Fuzhou 350108, China.
Jianli HuangFujian Grain and Oil Quality Monitoring Institute, Fuzhou 350108, China.
Fengfu FuMOE Key Laboratory for Analytical Science of Food Safety and Biology, Fujian Provincial Key Laboratory of Analysis and Detection Technology for Food Safety, College of Chemistry, Fuzhou University, Fuzhou 350108, China.
Yongqiang DongMOE Key Laboratory for Analytical Science of Food Safety and Biology, Fujian Provincial Key Laboratory of Analysis and Detection Technology for Food Safety, College of Chemistry, Fuzhou University, Fuzhou 350108, China.
Fuzhou University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Fungal toxins are secondary metabolites of fungi. Food is highly susceptible to contamination by various fungal species that produce fungal toxins during production and storage. Fungal toxins can cause either acute or chronic poisoning from long-term, low-dose ingestion. Therefore, fungal toxins have become a topic of international interest as a food safety issue. Deoxynivalenol (DON) is a single-terminal sporam toxin produced predominantly by Fusarium graminae and Fusarium pinkosa. DON is globally one of the most common fungal toxins contaminating grain, food, and feed. Various methods have been applied for screening and detecting DON; however, these methods utilize expensive instruments and entail complex operations, poor repeatability, and low sensitivity. Therefore, the development of a simpler, more rapid, and sensitive sensing technology for DON detection is important for applications within the agriculture and food industry. Recently, surface-enhanced Raman scattering (SERS) has become a rapidly developing spectral analysis technology with unique advantages, including high sensitivity, high throughput, and rapid response rates. Therefore, attempts have been made to apply the SERS technique to detecting DON. However, due to the limitations concerning SERS substrates, the currently established SERS method exhibits serious problems, including low sensitivity and weak anti-interference ability, and cannot meet the requirements of sample detection. Recently, our group has prepared aggregated silver nanoparticles (a-AgNPs/CDs) with high SERS activity by using single-layer carbon-based dots (CDs) as a capping agent. Moreover, the obtained materials (a-AgNPs/CDs) were combined with hydrogel technology to prepare novel hydrogel SERS chips. The obtained SERS chips exhibited several advantages over traditional SERS substrates, such as high sensitivity, long-term stability, improved uniformity, and strong anti-interference capabilities. Herein, a novel SERS method for rapid screening and detection of DON in grains was established using a portable Raman spectrometer based on the developed hydrogel SERS chips. The main experimental conditions were optimized before the SERS detection of DON; this included the optimization of the hydrogel SERS chip soaking temperature and time in the DON solution. It was found that the optimal soaking temperature and time were 40 ℃ and 5 min, respectively. Under the optimal SERS detection conditions, the linear response range of DON was 1-10000 μg/kg (correlation coefficient (

Indexed as

Metal NanoparticlesMycotoxinsAgricultureFlourHydrogelsSilverTrichothecenesTriticumdeoxynivalenolHydrogelsMycotoxinsSilverTrichothecenesaggregated silver nanoparticles (a-AgNPs)deoxynivalenol (DON)hydrogelsingle-layer carbon-based dots (CDs)surface-enhanced Raman scattering (SERS)

Identifiers

PMID36351813
PMCPMC9654612
OpenAlexW4308771296

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.