Discriminant Analysis PCA-LDA Assisted Surface-Enhanced Raman Spectroscopy for Direct Identification of Malaria-Infected Red Blood Cells
Various methods for detecting malaria have been developed in recent years, each with its own set of advantages. These methods include microscopic, antigen-based, and molecular-based analysis of blood samples. This study aimed to develop a new, alternative procedure for clinical use by using a large...
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MDPI AG
2022-06-01
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| Series: | Methods and Protocols |
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| Online Access: | https://www.mdpi.com/2409-9279/5/3/49 |
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| author | Gunganist Kongklad Ratchapak Chitaree Tana Taechalertpaisarn Nathinee Panvisavas Noppadon Nuntawong |
| author_facet | Gunganist Kongklad Ratchapak Chitaree Tana Taechalertpaisarn Nathinee Panvisavas Noppadon Nuntawong |
| author_sort | Gunganist Kongklad |
| collection | DOAJ |
| description | Various methods for detecting malaria have been developed in recent years, each with its own set of advantages. These methods include microscopic, antigen-based, and molecular-based analysis of blood samples. This study aimed to develop a new, alternative procedure for clinical use by using a large data set of surface-enhanced Raman spectra to distinguish normal and infected red blood cells. PCA-LDA algorithms were used to produce models for separating <i>P. falciparum</i> (3D7)-infected red blood cells and normal red blood cells based on their Raman spectra. Both average normalized spectra and spectral imaging were considered. However, these initial spectra could hardly differentiate normal cells from the infected cells. Then, discrimination analysis was applied to assist in the classification and visualization of the different spectral data sets. The results showed a clear separation in the PCA-LDA coordinate. A blind test was also carried out to evaluate the efficiency of the PCA-LDA separation model and achieved a prediction accuracy of up to 80%. Considering that the PCA-LDA separation accuracy will improve when a larger set of training data is incorporated into the existing database, the proposed method could be highly effective for the identification of malaria-infected red blood cells. |
| first_indexed | 2024-03-09T22:54:14Z |
| format | Article |
| id | doaj.art-74b290d91ba645f3b630cb2f788a2365 |
| institution | Directory Open Access Journal |
| issn | 2409-9279 |
| language | English |
| last_indexed | 2024-03-09T22:54:14Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Methods and Protocols |
| spelling | doaj.art-74b290d91ba645f3b630cb2f788a23652023-11-23T18:14:47ZengMDPI AGMethods and Protocols2409-92792022-06-01534910.3390/mps5030049Discriminant Analysis PCA-LDA Assisted Surface-Enhanced Raman Spectroscopy for Direct Identification of Malaria-Infected Red Blood CellsGunganist Kongklad0Ratchapak Chitaree1Tana Taechalertpaisarn2Nathinee Panvisavas3Noppadon Nuntawong4Department of Physics, Faculty of Science, Mahidol University, Bangkok 10400, ThailandDepartment of Physics, Faculty of Science, Mahidol University, Bangkok 10400, ThailandDepartment of Microbiology, Faculty of Science, Mahidol University, Bangkok 10400, ThailandDepartment of Plant, Faculty of Science, Mahidol University, Bangkok 10400, ThailandNational Electronics and Computer Technology Center (NECTEC), 112 Thailand Science Park, Pathum Thani 12120, ThailandVarious methods for detecting malaria have been developed in recent years, each with its own set of advantages. These methods include microscopic, antigen-based, and molecular-based analysis of blood samples. This study aimed to develop a new, alternative procedure for clinical use by using a large data set of surface-enhanced Raman spectra to distinguish normal and infected red blood cells. PCA-LDA algorithms were used to produce models for separating <i>P. falciparum</i> (3D7)-infected red blood cells and normal red blood cells based on their Raman spectra. Both average normalized spectra and spectral imaging were considered. However, these initial spectra could hardly differentiate normal cells from the infected cells. Then, discrimination analysis was applied to assist in the classification and visualization of the different spectral data sets. The results showed a clear separation in the PCA-LDA coordinate. A blind test was also carried out to evaluate the efficiency of the PCA-LDA separation model and achieved a prediction accuracy of up to 80%. Considering that the PCA-LDA separation accuracy will improve when a larger set of training data is incorporated into the existing database, the proposed method could be highly effective for the identification of malaria-infected red blood cells.https://www.mdpi.com/2409-9279/5/3/49malaria-infected red blood cells<i>P. falciparum</i> (3D7)surfaced-enhanced Raman spectraPCA-LDA |
| spellingShingle | Gunganist Kongklad Ratchapak Chitaree Tana Taechalertpaisarn Nathinee Panvisavas Noppadon Nuntawong Discriminant Analysis PCA-LDA Assisted Surface-Enhanced Raman Spectroscopy for Direct Identification of Malaria-Infected Red Blood Cells Methods and Protocols malaria-infected red blood cells <i>P. falciparum</i> (3D7) surfaced-enhanced Raman spectra PCA-LDA |
| title | Discriminant Analysis PCA-LDA Assisted Surface-Enhanced Raman Spectroscopy for Direct Identification of Malaria-Infected Red Blood Cells |
| title_full | Discriminant Analysis PCA-LDA Assisted Surface-Enhanced Raman Spectroscopy for Direct Identification of Malaria-Infected Red Blood Cells |
| title_fullStr | Discriminant Analysis PCA-LDA Assisted Surface-Enhanced Raman Spectroscopy for Direct Identification of Malaria-Infected Red Blood Cells |
| title_full_unstemmed | Discriminant Analysis PCA-LDA Assisted Surface-Enhanced Raman Spectroscopy for Direct Identification of Malaria-Infected Red Blood Cells |
| title_short | Discriminant Analysis PCA-LDA Assisted Surface-Enhanced Raman Spectroscopy for Direct Identification of Malaria-Infected Red Blood Cells |
| title_sort | discriminant analysis pca lda assisted surface enhanced raman spectroscopy for direct identification of malaria infected red blood cells |
| topic | malaria-infected red blood cells <i>P. falciparum</i> (3D7) surfaced-enhanced Raman spectra PCA-LDA |
| url | https://www.mdpi.com/2409-9279/5/3/49 |
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