ArticleScientific reports2023
Raman spectroscopy combined with a support vector machine algorithm as a diagnostic technique for primary Sjögren's syndrome.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 20 citations in OpenAlex.
- AI/ML-Assisted SERS Biosensing for Biomolecular Detection: From Direct Spectral Response to Integrated Diagnostic Systems.Biosensors · 2026Review
- Research on a rapid detection method for systemic lupus erythematosus based on Raman spectroscopy.Frontiers in immunology · 2026Article
- ATR-FTIR Spectroscopy of Saliva and Machine Learning as a Screening Test for Sjögren Disease.Analytical chemistry · 2025Article
- Gradient Nanostructures and Machine Learning Synergy for Robust Quantitative Surface-Enhanced Raman Scattering.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- 3D Hyperspectral Data Analysis with Spatially Aware Deep Learning for Diagnostic Applications.Analytical chemistry · 2025Article
- Diagnosis and activity prediction of SLE based on serum Raman spectroscopy combined with a two-branch Bayesian network.Frontiers in immunology · 2025Article
- Detection and classification of microplastics in green tea using SERS with gold nanoparticle substrates integrating chemometrics and deep learning.Current research in food science · 2025Article
- Label-free plasmonic spectral profiling of serum DNA.Biosensors & bioelectronics · 2024Article
- Raman hyperspectroscopy of saliva and machine learning for Sjögren's disease diagnostics.Scientific reports · 2024Article
- Early Diagnosis of Fibromyalgia Using Surface-Enhanced Raman Spectroscopy Combined with Chemometrics.Biomedicines · 2024Article
- Surface-Enhanced Raman Spectroscopy-Based Detection of Micro-RNA Biomarkers for Biomedical Diagnosis Using a Comparative Study of Interpretable Machine Learning Algorithms.Applied spectroscopy · 2024Article
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of this study was to explore the feasibility of Raman spectroscopy combined with computer algorithms in the diagnosis of primary Sjögren syndrome (pSS). In this study, Raman spectra of 60 serum samples were acquired from 30 patients with pSS and 30 healthy controls (HCs). The means and standard deviations of the raw spectra of patients with pSS and HCs were calculated. Spectral features were assigned based on the literature. Principal component analysis (PCA) was used to extract the spectral features. Then, a particle swarm optimization (PSO)-support vector machine (SVM) was selected as the method of parameter optimization to rapidly classify patients with pSS and HCs. In this study, the SVM algorithm was used as the classification model, and the radial basis kernel function was selected as the kernel function. In addition, the PSO algorithm was used to establish a model for the parameter optimization method. The training set and test set were randomly divided at a ratio of 7:3. After PCA dimension reduction, the specificity, sensitivity and accuracy of the PSO-SVM model were obtained, and the results were 88.89%, 100% and 94.44%, respectively. This study showed that the combination of Raman spectroscopy and a support vector machine algorithm could be used as an effective pSS diagnosis method with broad application value.
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