Evidence map›Paper›PMID 40862938›Full record

ArticleBiosensors2025

Quantitative Detection of Pyrene in Edible Oil via Plasmonic TLC-SERS Combined with Machine Learning Analysis.

Jiahui Tian, Xianhe Jiao, Jiaqi Guo, Qian Yu, Shuqin Zhang, Guizhou Gu, Kundan Sivashanmugan, Xianming Kong

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biosensors, 2025. 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
–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

1 citing paper in PubMed.

  1. Article
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

8 authors.

Jiahui TianSchool of Petrochemical Engineering, Liaoning Petrochemical University, Fushun 113001, China.
Xianhe JiaoSchool of Petrochemical Engineering, Liaoning Petrochemical University, Fushun 113001, China.
Jiaqi GuoJiangsu Co-Innovation Center for Efficient Processing, Utilization of Forest Resources and Joint International Research Lab of Lignocellulosic Functional Materials, Nanjing Forestry University, Nanjing 210037, China.
Qian YuSchool of Petrochemical Engineering, Liaoning Petrochemical University, Fushun 113001, China.
Shuqin ZhangFushun Ecological Environment Monitoring Center of Liaoning Province, Fushun 113001, China.
Guizhou GuSchool of Petrochemical Engineering, Liaoning Petrochemical University, Fushun 113001, China.
Kundan SivashanmuganCenter for Fluorescence Spectroscopy, Department of Biochemistry and Molecular Biology, University of Maryland School of Medicine, 721 West Lombard St., Baltimore, MD 21201, USA.ORCID 0009-0000-1767-7462
Xianming KongSchool of Petrochemical Engineering, Liaoning Petrochemical University, Fushun 113001, China.ORCID 0000-0001-7622-7744

Funding

International Science and Technology Cooperation Project of Liaoning Province 2024JH2/101900019
6 · The paper itself

Abstract

The presence of polycyclic aromatic hydrocarbons (PAHs) in edible oil has a serious effect on human health and may potentially induce cancer. This study combined thin-layer chromatography and surface-enhanced Raman spectroscopy (TLC-SERS) to rapidly and quantitatively detect PAHs in culinary oil. Machine learning using the principle component analysis-back propagation neural network (PCA-BP) was integrated with TLC-SERS for the detection of PAHs. Ag nanoparticles on diatomite (diatomite/Ag) TLC-SERS substrate were prepared via an in situ growth process and employed as a stationary phase in the TLC channel. The analyte sample was dropped onto the TLC channel for separation and detection. The diatomite/Ag TLC channel demonstrated excellent separation capability and superior SERS performance and successfully detected PAHs from edible oil at a sensitivity of 0.1 ppm. The PCA-BP quantitative analysis model demonstrated outstanding prediction performance. This work demonstrates that the combination of TLC-SERS technology with PCA-BP is an efficient and accurate method for quantitatively detecting PAHs in edible oil, which can effectively improve the quality of food.

Indexed as

Chromatography, Thin LayerMachine LearningPlant OilsPyrenesSpectrum Analysis, RamanColloidsMetal NanoparticlesPolycyclic Aromatic HydrocarbonsPrincipal Component AnalysisSilverSpectroscopy, Fourier Transform InfraredColloidsPlant OilsPolycyclic Aromatic HydrocarbonsPyrenesSilveredible oilmachine learningpyrenequantitativeTLC-SERS

Identifiers

PMID40862938
PMCPMC12384367

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.