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- W3049242578 abstract "Autofluorescence spectroscopy, together with multivariate analysis methods, has been applied for blood plasma analysis to develop a powerful non-invasive and label-free technique for pulmonary tuberculosis detection. The fluorescence spectra of blood plasma samples from 12 diagnosed active pulmonary tuberculosis patients as well as 9 healthy (control) volunteers are recorded, and the distinct intensity difference of the original and second derivative fluorescence spectra of the two groups are observed. The Principal component analysis (PCA) algorithm is applied on the fluorescence spectra to reduce the dimensionality and highlight the spectral differences, it can separate the two groups into two discrete clusters in the principal component scatter plot. Then the Linear discriminant analysis (LDA) algorithm is applied on the principal component data set to classify the blood plasma of the two groups. It shows that the PCA-LDA diagnostic model can differentiate the tuberculosis plasma from normal plasma with diagnosis accuracy of 95.2 %, sensitivity of 91.7 % and specificity of 100 %, respectively. The diagnosis performance was evaluated and confirmed using leave-one-out cross validation procedure. To the best of our knowledge, it is the first time that autofluorescence spectroscopy of blood plasma is demonstrated as a potential tool for identification and screening of pulmonary tuberculosis disease." @default.
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- W3049242578 date "2020-12-01" @default.
- W3049242578 modified "2023-10-17" @default.
- W3049242578 title "Autofluorescence spectroscopy of blood plasma with multivariate analysis methods for the diagnosis of pulmonary tuberculosis" @default.
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- W3049242578 doi "https://doi.org/10.1016/j.ijleo.2020.165446" @default.
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