Evidence map›Paper›PMID 39661318›Full record

ArticleAnalytical sciences : the international journal of the Japan Society for Analytical Chemistry2025

Antibiotic SERS spectral analysis based on data augmentation and attention mechanism strategy.

Hang Zhao, Min Zhou, Chunlin Liu, Hongheng Sun, Panshuo Zhang, Jun Ma, Xiaofeng Shi

Abstract read
PubMed Publisher
In one paragraph

Article in Analytical sciences : the international journal of the Japan Society for Analytical Chemistry, 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. A conjugated pyridine diimine fluorene-based polymer for the detection of furaltadone.Analytical sciences : the international journal of the Japan Society for Analytical Chemistry · 2025
    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

7 authors.

Hang ZhaoOptics and Optoelectronics Laboratory, Ocean University of China, Qingdao, 266100, People's Republic of China. zhaohang@ouc.edu.cn.ORCID http://orcid.org/0000-0002-9903-0183
Min ZhouOptics and Optoelectronics Laboratory, Ocean University of China, Qingdao, 266100, People's Republic of China.
Chunlin LiuOptics and Optoelectronics Laboratory, Ocean University of China, Qingdao, 266100, People's Republic of China.
Hongheng SunOptics and Optoelectronics Laboratory, Ocean University of China, Qingdao, 266100, People's Republic of China.
Panshuo ZhangOptics and Optoelectronics Laboratory, Ocean University of China, Qingdao, 266100, People's Republic of China.
Jun MaOptics and Optoelectronics Laboratory, Ocean University of China, Qingdao, 266100, People's Republic of China.
Xiaofeng ShiOptics and Optoelectronics Laboratory, Ocean University of China, Qingdao, 266100, People's Republic of China.

Funding

Laoshan Laboratory LSKJ202203901National Natural Science Foundation of China 422061191Natural Science Foundation of Shandong Province ZR2021QF047
6 · The paper itself

Abstract

The analysis of Raman spectrum data has gradually transitioned into the era of machine learning. However, it is still constrained by the challenge of acquiring large volumes of raw data and the issue of losing characteristic information from spectral data. In this paper, we propose a strategy that combines data amplification and attention mechanisms for analyzing antibiotic spectral data. Firstly, a Generative Adversarial Network was employed to amplify the SERS spectrum of eight antibiotics by 10 times, to augment the dataset to fulfill the requirements of the neural network. Then, the amplified data is input into a one-dimensional convolutional neural network with an attentional mechanism module, which enables a more accurate capture of spectral feature information. The one-dimensional convolutional neural network achieved a 97.5% accuracy in classifying eight antibiotics. The accuracy of the four mixtures within the same class was 89.4%.

Indexed as

Anti-Bacterial AgentsNeural Networks, ComputerSpectrum Analysis, RamanMachine LearningAnti-Bacterial AgentsAntibioticsAttention mechanismConvolutional neural networkData augmentationSurface-enhanced raman spectroscopy

Identifiers

PMID39661318

What OpenQuestion holds

Textmetadata
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.