Abstract
Raman spectroscopy was used to distinguish the serums from lung cancer patients and healthy people, through spectral pretreatment method combined with pattern recognition methods including principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), un-correlated linear discriminant analysis (ULDA), etc. Through the comparisons of the results, it can be found that ULDA and LDA combined with multiple scatter correction (MSC) pretreatment method successfully distinguish the patients of lung cancer and healthy people. The method has academic significance and promising clinical application value.
Highlights
Cancer is one of a variety of diseases with high mortality rate, and because of the increasing of environmental pollution, the incidence of cancer is increasing [1] [2]
Raman spectroscopy was used to distinguish the serums from lung cancer patients and healthy people, through spectral pretreatment method combined with pattern recognition methods including principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), uncorrelated linear discriminant analysis (ULDA), etc
The results show that the method of ULDA or LDA combined with multiple scatter correction (MSC) could accurately distinguish the patients of lung cancer and healthy people
Summary
Cancer is one of a variety of diseases with high mortality rate, and because of the increasing of environmental pollution, the incidence of cancer is increasing [1] [2]. Because the Raman scattering of water is very weak, and other biological substances have wealthy Raman information, so Raman spectroscopy is very suitable for the detection of serum containing a large amount of moisture. Raman spectroscopy combined with chemometrics methods to study cancer, mostly on human tissue research [11] [12]. There is little research on serum by Raman spectroscopy combined with chemometrics. This study is based on serum Raman spectroscopy data of lung cancer patients and healthy people. By pattern recognition methods of chemometrics including principal component analysis (PCA), non-negative matrix factorization (NMF), partial least squares-discriminant analysis (PLS-DA), linear correlation analysis (LDA) and uncorrelated linear discriminant analysis (ULDA), lung cancer patients and healthy people were distinguished. The results show that the method of ULDA or LDA combined with multiple scatter correction (MSC) could accurately distinguish the patients of lung cancer and healthy people. This work provides a new way on the identification of lung cancer, which has academic significance and promising clinical application value
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
