Evidence map›Paper›PMID 38005225›Full record

ReviewMolecules (Basel, Switzerland)2023

Advance in Detection Technique of Lean Meat Powder Residues in Meat Using SERS: A Review.

Qinghui Guo, Yankun Peng, Jianwei Qin, Kuanglin Chao, Xinlong Zhao, Tianzhen Yin

Abstract readReview
In one paragraph

Review in Molecules (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Qinghui GuoCollege of Engineering, China Agricultural University, Beijing 100083, China.ORCID 0000-0002-9505-9755
Yankun PengCollege of Engineering, China Agricultural University, Beijing 100083, China.ORCID 0000-0003-4124-0492
Jianwei QinUSDA/ARS Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center, 10300 Baltimore Ave., Beltsville, MD 20705, USA.ORCID 0000-0002-8061-4373
Kuanglin ChaoUSDA/ARS Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center, 10300 Baltimore Ave., Beltsville, MD 20705, USA.
Xinlong ZhaoCollege of Engineering, China Agricultural University, Beijing 100083, China.
Tianzhen YinCollege of Engineering, China Agricultural University, Beijing 100083, China.

Funding

the Major International (Regional) Joint Research Project of the National Natural Science Foundation of China 32020103016
6 · The paper itself

Abstract

Food that contains lean meat powder (LMP) can cause human health issues, such as nausea, headaches, and even death for consumers. Traditional methods for detecting LMP residues in meat are often time-consuming and complex and lack sensitivity. This article provides a review of the research progress on the use of surface-enhanced Raman spectroscopy (SERS) technology for detecting residues of LMP in meat. The review also discusses several applications of SERS technology for detecting residues of LMP in meat, including the enhanced detection of LMP residues in meat based on single metal nanoparticles, combining metal nanoparticles with adsorbent materials, combining metal nanoparticles with immunizing and other chemicals, and combining the SERS technology with related techniques. As SERS technology continues to develop and improve, it is expected to become an even more widely used and effective tool for detecting residues of LMP in meat.

Indexed as

Metal NanoparticlesSpectrum Analysis, RamanHumansMeatPowdersPowderslean meat powderlivestock and poultry meatnanomaterialsresiduessurface–enhanced Raman spectroscopy

Identifiers

PMID38005225
PMCPMC10673115

What OpenQuestion holds

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Registered trials

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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.