Evidence map›Paper›PMID 40584312›Full record

ReviewACS omega2025

Recent Developments and Applications of Surface-Enhanced Raman Scattering Spectroscopy in Pesticides Detection: From Single Pesticides to Mixed Pesticides.

Pengpeng Yu, Lixin Ma, Xiaonan Yang, Shanshan Xue, Zhepeng Zhang, Li Sun, Jianrong Cai

Abstract readReview
In one paragraph

Review in ACS omega, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
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  6. 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

7 authors.

Pengpeng YuSchool of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China.
Lixin MaSchool of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China.
Xiaonan YangSchool of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China.
Shanshan XueSchool of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China.
Zhepeng ZhangSchool of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China.
Li SunSchool of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China.
Jianrong CaiSchool of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China.ORCID https://orcid.org/0000-0002-7509-3067

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pesticides directly pollute the environment and food, eventually being absorbed into the human body. Their residues are highly toxic and pose serious health risks. Chromatography and gas liquid chromatography tandem mass spectrometry are widely employed for pesticide residue detection. However, these methods are often labor-intensive due to complex pretreatment processes, time-consuming procedures, and high energy consumption. Surface-enhanced Raman spectroscopy is regarded as a new detection method for agricultural residues due to its advantages of high sensitivity, excellent specificity, comprehensive fingerprint information and nondestructive to samples. This technique enables efficient detection of trace pesticide residues in both liquid and solid samples via simple extraction. In this review, the classification of SERS substrates, peak attribution of Raman reporter molecules, label-free detection of single and mixed pesticide residues, and the application of labeled detection have been comprehensively reviewed. The study aims to provide a valuable reference for pesticide residue detection, particularly for the simultaneous detection of multiple pesticide residues. Additionally, the review explores the challenges and future prospects of SERS technology in pesticide residue detection, including the difficulties of achieving synchronous detection, advancements in intelligent algorithm development, and the construction of portable on-site detection platforms. These innovations are designed to simplify the detection process and offer insights into selecting optimal sensing methods for on-site detection of multiple pesticide residues.

Identifiers

PMID40584312
PMCPMC12199038

What OpenQuestion holds

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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.