Surface-enhanced Raman spectroscopy with machine learning in non-invasive detection of dengue-NS1 fingerprint / Afaf Rozan Mohd Radzol ... [et al.]
The surface-enhanced Raman spectroscopy (SERS) method exploits the plasmonic effect of nano-sized metallic materials to intensify the Raman scattering of the monochromatic light of analyte molecules. This promotes the sensitivity and specificity of the Raman spectroscopy analysis method. This study...
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| Language: | en |
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Universiti Teknologi MARA Cawangan Pulau Pinang
2024
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| Online Access: | https://ir.uitm.edu.my/id/eprint/93873/2/93873.pdf https://doi.org/10.24191/esteem.v20iMarch.616.g534 https://ir.uitm.edu.my/id/eprint/93873/ http://uppp.uitm.edu.my/online-issues.html |
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| author | Mohd Radzol, Afaf Rozan Khuan, Y Lee Peng, Shyan Wong Looi, Irene Mansor, Wahidah |
| author_facet | Mohd Radzol, Afaf Rozan Khuan, Y Lee Peng, Shyan Wong Looi, Irene Mansor, Wahidah |
| author_sort | Mohd Radzol, Afaf Rozan |
| building | Tun Abdul Razak Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknologi Mara |
| content_source | UiTM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | The surface-enhanced Raman spectroscopy (SERS) method exploits the plasmonic effect of nano-sized metallic materials to intensify the Raman scattering of the monochromatic light of analyte molecules. This promotes the sensitivity and specificity of the Raman spectroscopy analysis method. This study integrated SERS with machine learning (ML) to detect dengue fever, a disease infecting more than 40% of the world’s population. Non-structural protein 1 (NS1), detected in the sera of infected dengue patients during the early infection stage, is currently recognised as a biomarker for the early diagnosis of DF. However, no attempts have been made to detect NS1 in the salivary Raman spectra. Given this situation, this study delves into the potential of SERS as an early, non-invasive DF detection technique using salivary NS1. The SERS spectra of saliva samples (n=289) were collected and subsequently classified as positive and negative for DF, using principal component analysis (PCA) integrated with support vector machine (SVM) models. The PCA-SVM model's performance was benchmarked against two clinical diagnostic NS1-enzyme-linked immunosorbent assay (ELISA) tests recommended by the World Health Organization (WHO). The PCA-SVM model outperformed both tests regarding radial basis function kernel (RBF) and cumulative percent variance (CPV; 83.22% accuracy, 88.27% sensitivity, 78.13% specificity). |
| format | Article |
| id | my.uitm.ir-93873 |
| institution | Universiti Teknologi Mara |
| language | en |
| publishDate | 2024 |
| publisher | Universiti Teknologi MARA Cawangan Pulau Pinang |
| record_format | eprints |
| spelling | my.uitm.ir-938732025-07-10T06:24:23Z https://ir.uitm.edu.my/id/eprint/93873/ Surface-enhanced Raman spectroscopy with machine learning in non-invasive detection of dengue-NS1 fingerprint / Afaf Rozan Mohd Radzol ... [et al.] esteem Mohd Radzol, Afaf Rozan Khuan, Y Lee Peng, Shyan Wong Looi, Irene Mansor, Wahidah Pulau Pinang Applications of electronics The surface-enhanced Raman spectroscopy (SERS) method exploits the plasmonic effect of nano-sized metallic materials to intensify the Raman scattering of the monochromatic light of analyte molecules. This promotes the sensitivity and specificity of the Raman spectroscopy analysis method. This study integrated SERS with machine learning (ML) to detect dengue fever, a disease infecting more than 40% of the world’s population. Non-structural protein 1 (NS1), detected in the sera of infected dengue patients during the early infection stage, is currently recognised as a biomarker for the early diagnosis of DF. However, no attempts have been made to detect NS1 in the salivary Raman spectra. Given this situation, this study delves into the potential of SERS as an early, non-invasive DF detection technique using salivary NS1. The SERS spectra of saliva samples (n=289) were collected and subsequently classified as positive and negative for DF, using principal component analysis (PCA) integrated with support vector machine (SVM) models. The PCA-SVM model's performance was benchmarked against two clinical diagnostic NS1-enzyme-linked immunosorbent assay (ELISA) tests recommended by the World Health Organization (WHO). The PCA-SVM model outperformed both tests regarding radial basis function kernel (RBF) and cumulative percent variance (CPV; 83.22% accuracy, 88.27% sensitivity, 78.13% specificity). Universiti Teknologi MARA Cawangan Pulau Pinang 2024-03 Article PeerReviewed text en https://ir.uitm.edu.my/id/eprint/93873/2/93873.pdf Mohd Radzol, Afaf Rozan and Khuan, Y Lee and Peng, Shyan Wong and Looi, Irene and Mansor, Wahidah (2024) Surface-enhanced Raman spectroscopy with machine learning in non-invasive detection of dengue-NS1 fingerprint / Afaf Rozan Mohd Radzol ... [et al.]. (2024) ESTEEM Academic Journal <https://ir.uitm.edu.my/view/publication/ESTEEM_Academic_Journal.html>, 20 (March). pp. 65-81. ISSN 2289-4934 http://uppp.uitm.edu.my/online-issues.html https://doi.org/10.24191/esteem.v20iMarch.616.g534 https://doi.org/10.24191/esteem.v20iMarch.616.g534 |
| spellingShingle | Pulau Pinang Applications of electronics Mohd Radzol, Afaf Rozan Khuan, Y Lee Peng, Shyan Wong Looi, Irene Mansor, Wahidah Surface-enhanced Raman spectroscopy with machine learning in non-invasive detection of dengue-NS1 fingerprint / Afaf Rozan Mohd Radzol ... [et al.] |
| title | Surface-enhanced Raman spectroscopy with machine learning in non-invasive detection of dengue-NS1 fingerprint / Afaf Rozan Mohd Radzol ... [et al.] |
| title_full | Surface-enhanced Raman spectroscopy with machine learning in non-invasive detection of dengue-NS1 fingerprint / Afaf Rozan Mohd Radzol ... [et al.] |
| title_fullStr | Surface-enhanced Raman spectroscopy with machine learning in non-invasive detection of dengue-NS1 fingerprint / Afaf Rozan Mohd Radzol ... [et al.] |
| title_full_unstemmed | Surface-enhanced Raman spectroscopy with machine learning in non-invasive detection of dengue-NS1 fingerprint / Afaf Rozan Mohd Radzol ... [et al.] |
| title_short | Surface-enhanced Raman spectroscopy with machine learning in non-invasive detection of dengue-NS1 fingerprint / Afaf Rozan Mohd Radzol ... [et al.] |
| title_sort | surface-enhanced raman spectroscopy with machine learning in non-invasive detection of dengue-ns1 fingerprint / afaf rozan mohd radzol ... [et al.] |
| topic | Pulau Pinang Applications of electronics |
| url | https://ir.uitm.edu.my/id/eprint/93873/2/93873.pdf https://doi.org/10.24191/esteem.v20iMarch.616.g534 https://ir.uitm.edu.my/id/eprint/93873/ http://uppp.uitm.edu.my/online-issues.html https://doi.org/10.24191/esteem.v20iMarch.616.g534 |
| url_provider | http://ir.uitm.edu.my/ |