Evidence map›Paper›PMID 27447636›Full record

ReviewSensors (Basel, Switzerland)2016

Recent Progresses in Nanobiosensing for Food Safety Analysis.

Tao Yang, Huifen Huang, Fang Zhu, Qinlu Lin, Lin Zhang, Junwen Liu

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Nanomaterials in food packaging: state of the art and analysis.Journal of food science and technology · 2018
    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

6 authors.

Tao YangCollege of Food Science & Engineering, Central South University of Forestry & Technology, Changsha 410004, China. yangtao807@163.com.
Huifen HuangCollege of Food Science & Engineering, Central South University of Forestry & Technology, Changsha 410004, China. huanghuifen011@163.com.
Fang ZhuCollege of Food Science & Engineering, Central South University of Forestry & Technology, Changsha 410004, China. lwsw01@163.com.
Qinlu LinCollege of Food Science & Engineering, Central South University of Forestry & Technology, Changsha 410004, China. linql0403@126.com.
Lin ZhangCollege of Food Science & Engineering, Central South University of Forestry & Technology, Changsha 410004, China. wolaitb@163.com.
Junwen LiuDepartment of Histology and Embryology, School of Basic Medical Sciences, Central South University, Changsha 410013, China. liujunwen@csu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With increasing adulteration, food safety analysis has become an important research field. Nanomaterials-based biosensing holds great potential in designing highly sensitive and selective detection strategies necessary for food safety analysis. This review summarizes various function types of nanomaterials, the methods of functionalization of nanomaterials, and recent (2014-present) progress in the design and development of nanobiosensing for the detection of food contaminants including pathogens, toxins, pesticides, antibiotics, metal contaminants, and other analytes, which are sub-classified according to various recognition methods of each analyte. The existing shortcomings and future perspectives of the rapidly growing field of nanobiosensing addressing food safety issues are also discussed briefly.

Indexed as

Anti-Bacterial AgentsBiosensing TechniquesFood AnalysisFood ContaminationFood SafetyNanostructuresPesticidesAnti-Bacterial AgentsPesticidesfood safety analysisfunction of nanomaterialsnanobiosensing

Identifiers

PMID27447636
PMCPMC4970161

What OpenQuestion holds

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LicenceCC BY
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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.