Lung cancer is the leading cause of cancer-related death in the world. Early diagnosis has great significance for the survival of patients with lung cancer. In this paper, attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy combined with chemometrics was used to study the serum samples from patients with lung cancer and healthy people. The results of spectral band area comparison showed that the concentrations of protein, lipid and nucleic acids molecules in serum of patients with lung cancer were increased compared with those in healthy people. The original spectra were preprocessed to improve the accuracy of principal component regression (PCR) and partial least squares-discriminant analysis (PLS-DA) models. PLS-DA results for first derivative spectral data in nucleic acids (1250-1000cm-1) band showed 80% sensitivity, 91.89% specificity and 87.10% accuracy with high Rc2 of 0.8949 and Rv2 of 0.8153, low RMSEC of 0.3136 and RMSEV of 0.4180. It is shown that ATR-FTIR spectroscopy combined with chemometrics might be developed as a simple method for clinical screening and diagnosis of lung cancer.
Storage years affected the quality of Pu'er raw tea. It is intensely difficult to distinguish quality through their appearance and odor. In this work, the Fourier transform infrared spectroscopy (FTIR) combined with two-dimensional correlation spectroscopy (2D-COS), hierarchical cluster analysis (HCA), and principal components analysis (PCA) were employed to distinguish Pu'er raw tea with different storage years.2D-COS results revealed the changes of the various components in Pu'er raw tea sample corresponding to the temperature perturbation and the interconnection between these changes were obtained. PCA results showed that the scattered points of PC1 and PC2 can be well distributed in different regions. HCA helped to carry out the clustering result of Pu'er raw tea samples in different storage years. Therefore, FTIR combined with chemometrics might be a facile, fast, and nondestructive strategy for identifying Pu'er raw tea with different storage years. Novelty impact statement• The components of Pu'er raw tea and its variation with temperature were acquired by Fourier transform infrared spectroscopy (FTIR) and two-dimensional correlation spectroscopy (2D-COS).• The effective use of spectral means to identify Pu'er raw tea in different storage years. Provide an effective reference for the quality appraisal of Pu'er raw tea.• FTIR combined with chemometrics can be developed into a facile, fast, and nondestructive strategy for identifying Pu'er raw tea with different storage years.
Lung cancer is a fatal tumor threatening human health. It is of great significance to explore a diagnostic method with wide application range, high specificity, and high sensitivity for the detection of lung cancer. In this study, data fusion and wavelet transform were used in combination with Fourier transform infrared (FTIR) spectroscopy and Raman spectroscopy to study the serum samples of patients with lung cancer and healthy people. The Raman spectra of serum samples can provide more biological information than the FTIR spectra of serum samples. After selecting the optimal wavelet parameters for wavelet threshold denoising (WTD) of spectral data, the partial least squares–discriminant analysis (PLS-DA) model showed 93.41% accuracy, 96.08% specificity, and 90% sensitivity for the fusion data processed by WTD in the prediction set. The results showed that the combination of FTIR spectroscopy and Raman spectroscopy based on data fusion and wavelet transform can effectively diagnose patients with lung cancer, and it is expected to be applied to clinical screening and diagnosis in the future.
Background: Raman and fluorescence spectra techniques are potential tools for disease diagnosis. In recent years, the application of Raman and fluorescence spectra techniques in biological studies has increased a great deal, and clinical investigations relevant to cancer detection by spectroscopic means have attracted particularly attention from both clinical and non-clinical researchers. Methods: In this article, Raman and fluorescence spectra were employed for the detection of liver cancer and healthy individuals using their serum samples. These serum samples were compared with their spectral features acquired by Raman and fluorescence spectroscopy to initially establish spectral features that can be considered spectral markers of liver cancer diagnosis. Resuits: The intensity differences from characteristic peaks of carotene, protein and lipid associated Raman spectra were clearly observed in liver cancer patient serum samples versus normal human serum. The changes in the serum fluorescence profiles of liver cancer patients were also analyzed. To probe the capacity and contrast of Raman spectroscopy as an analytical implement for the early diagnosis of liver cancer, principal component analysis (PCA) was used to analyze the Raman spectra of controls , liver cancer patients and healthy individuals. Furthermore, the Partial Least Squares-Discriminant Analysis (PLS-DA) was performed to compare the diagnostic performance of Raman spectroscopy for the classification of disease samples and healthy samples.Conclusion: Compare with the existing diagnostic techniques, the Raman spectroscopy technique has an excellent advantage in extremely low sample requirements, ease of use and ideal screening procedures. Thus, Raman spectroscopy has great potential to be developed as a powerful tool for distinguishing between healthy and liver cancer serum samples.
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