Raman spectral analysis of nasopharyngeal carcinoma cell line CNE2 after microwave radiation

2013 ◽  
Vol 91 (2) ◽  
pp. 67-71 ◽  
Author(s):  
Yuhuang Ye ◽  
Yang Chen ◽  
Ying Su ◽  
Changyan Zou ◽  
Yangwen Huang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Raman Spectroscopy ◽  
Nasopharyngeal Carcinoma ◽  
Microwave Radiation ◽  
Principal Components ◽  
Carcinoma Cell ◽  
Support Vector ◽  
Components Analysis ◽  
Raman Spectral ◽  
Radiation Time

This study aimed to study the effects of microwave radiation on the nasopharyngeal carcinoma cell line CNE2 by Raman spectroscopy. The cells were separated into a control group and radiated groups with radiation times of 2, 5, 10, and 25 min, respectively. Both principal components analysis and support vector machine were employed for statistical analysis of Raman spectra. The results show that the relative content of C-H deformation and amide I begin to change when the radiation time is over 10 min, and principal components analysis further confirms there are significant differences after 10 min of radiation. Moreover, support vector machine is simultaneously used to classify radiated samples from control samples. The classification accuracy is low until the radiation time reaches over 10 min. In conclusion, this study reveals the Raman spectral characteristics of CNE2 under different microwave radiation exposure timesand demonstrates Raman spectroscopy can be a potential method to explore cellular characterization after radiation. The final results may help in elucidating the mechanism by which microwave radiation interacts with tumor cells.

scholarly journals A hybrid data-driven fault detection strategy with application to navigation sensors

2020 ◽  
Vol 53 (7-8) ◽  
pp. 1404-1415
Author(s):  
Huahui Yang ◽  
Chen Meng ◽  
Cheng Wang
Keyword(s):  
Support Vector Machine ◽  
Fault Detection ◽  
Principal Components ◽  
Navigation System ◽  
Data Driven ◽  
Support Vector ◽  
Detection Strategy ◽  
Hybrid Data ◽  
Components Analysis ◽  
The One

The integrated navigation system highly relies on the accuracy of measurements of sensors that are susceptible to unknown disturbances. In order to improve the reliability and safety of the navigation system, there is an increasing need for the fault detection of the sensors. In the present study, a hybrid data-driven fault detection strategy is proposed, which is based on residual sequence analysis. Currently, the one-class support vector machine is one of the most popular fault detection methods for navigation systems with many successful cases. Therefore, the one-class support vector machine is combined with time-series similarity measure and modified principal components analysis approaches. The similarity measurement of multi-sequence residuals between a real-time sample and normal condition samples is computed to construct learning features for one-class support vector machine. Similarly, the modified principal components analysis scheme is applied to project residuals onto subspaces and obtain learning features. Moreover, the one-class support vector machine model is applied for abnormal detection if unexpected sensor faults exhibit in measurements and residuals. Finally, experiments are carried out to evaluate the performance of the proposed strategy for abrupt faults and soft faults on navigation sensors. Experimental results show that the hybrid data-driven fault detection strategy can effectively detect these faults with short time delay and high accuracy.

Raman spectroscopy-based label-free cell identification using wavelet transform and support vector machine

RSC Advances ◽  
2016 ◽  
Vol 6 (55) ◽  
pp. 50027-50033 ◽  
Author(s):  
S. Bakhtiaridoost ◽  
H. Habibiyan ◽  
S. Muhammadnejad ◽  
M. Haddadi ◽  
H. Ghafoorifard ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Raman Spectroscopy ◽  
Wavelet Transform ◽  
Raman Spectra ◽  
Free Cell ◽  
Support Vector ◽  
Label Free ◽  
Cell Identification ◽  
Rare Cells

Wavelet transform and SVM applied to Raman spectra makes a powerful and accurate tool for identification of rare cells such as CTCs.

scholarly journals Raman investigation of corrosion products on Roman copper-based artefacts

ACTA IMEKO ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 129
Author(s):  
Leila Es Sebar ◽  
Leonardo Iannucci ◽  
Yuval Goren ◽  
Peter Fabian ◽  
Emma Angelini ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Roman Period ◽  
Corrosion Products ◽  
Beer Sheva ◽  
Chemometric Technique ◽  
Components Analysis ◽  
Main Components

<p class="Abstract">This paper illustrates a case study related to the characterisation of corrosion products present on recently excavated artefacts. The archaeological findings, from the Rakafot 54 site (Beer-Sheva, Israel), consist of 23 coins and a pendant, all dating back to the Roman period. Raman spectroscopy was used to identify the corrosion products that compose the patina covering the objects. To facilitate and support their identification, spectra were then processed using principal components analysis. This chemometric technique allowed the identification of two main compounds, classified as atacamite and clinoatacamite, which formed the main components of the patinas. The results of this investigation can help in assessing the conservation state of artefacts and defining the correct restoration strategy.</p>

Diagnosis of atherosclerosis in human carotid artery by FT-Raman spectroscopy: Principal Components Analysis algorithm

2004 ◽  
Author(s):  
Grazielle V. Nogueira ◽  
Landulfo Silveira, Jr. ◽  
Airton A. Martin ◽  
Renato A. Zangaro ◽  
Marcos T. Pacheco ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Carotid Artery ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Analysis Algorithm ◽  
Ft Raman Spectroscopy ◽  
Components Analysis ◽  
Human Carotid ◽  
Ft Raman
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