spectral diagnostic
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2019 ◽  
Vol 147 ◽  
pp. 111241 ◽  
Author(s):  
X.L. Zhang ◽  
Z.F. Cheng ◽  
X.D. Lin ◽  
H.S Mo ◽  
W.W. Zhang ◽  
...  

2016 ◽  
Vol 68 (8) ◽  
pp. 994-997 ◽  
Author(s):  
Jian He ◽  
Qingguo Zhang ◽  
Qiuze Li

2011 ◽  
Vol 25 (2) ◽  
pp. 89-102 ◽  
Author(s):  
Ricardo Matias Lopes ◽  
Landulfo Silveira ◽  
Marcos Augusto R. S. Silva ◽  
Kátia Ramos M. Leite ◽  
Carlos Augusto G. Pasqualucci ◽  
...  

This study evaluated the use of Raman spectroscopy to identify the spectral differences between normal (N), benign hyperplasia (BPH) and adenocarcinoma (CaP) in fragments of prostate biopsiesin vitrowith the aim of developing a spectral diagnostic model for tissue classification. A dispersive Raman spectrometer was used with 830 nm wavelength and 80 mW excitation. Following Raman data collection and tissue histopathology (48 fragments diagnosed as N, 43 as BPH and 14 as CaP), two diagnostic models were developed in order to extract diagnostic information: the first using PCA and Mahalanobis analysis techniques and the second one a simplified biochemical model based on spectral features of cholesterol, collagen, smooth muscle cell and adipocyte. Spectral differences between N, BPH and CaP tissues, were observed mainly in the Raman bands associated with proteins, lipids, nucleic and amino acids. The PCA diagnostic model showed a sensitivity and specificity of 100%, which indicates the ability of PCA and Mahalanobis distance techniques to classify tissue changesin vitro. Also, it was found that the relative amount of collagen decreased while the amount of cholesterol and adipocyte increased with severity of the disease. Smooth muscle cell increased in BPH tissue. These characteristics were used for diagnostic purposes.


2010 ◽  
Vol 10 (10) ◽  
pp. 4625-4641 ◽  
Author(s):  
N. L. Ng ◽  
M. R. Canagaratna ◽  
Q. Zhang ◽  
J. L. Jimenez ◽  
J. Tian ◽  
...  

Abstract. In this study we compile and present results from the factor analysis of 43 Aerosol Mass Spectrometer (AMS) datasets (27 of the datasets are reanalyzed in this work). The components from all sites, when taken together, provide a holistic overview of Northern Hemisphere organic aerosol (OA) and its evolution in the atmosphere. At most sites, the OA can be separated into oxygenated OA (OOA), hydrocarbon-like OA (HOA), and sometimes other components such as biomass burning OA (BBOA). We focus on the OOA components in this work. In many analyses, the OOA can be further deconvolved into low-volatility OOA (LV-OOA) and semi-volatile OOA (SV-OOA). Differences in the mass spectra of these components are characterized in terms of the two main ions m/z 44 (CO2+) and m/z 43 (mostly C2H3O+), which are used to develop a new mass spectral diagnostic for following the aging of OA components in the atmosphere. The LV-OOA component spectra have higher f44 (ratio of m/z 44 to total signal in the component mass spectrum) and lower f43 (ratio of m/z 43 to total signal in the component mass spectrum) than SV-OOA. A wide range of f44 and O:C ratios are observed for both LV-OOA (0.17±0.04, 0.73±0.14) and SV-OOA (0.07±0.04, 0.35±0.14) components, reflecting the fact that there is a continuum of OOA properties in ambient aerosol. The OOA components (OOA, LV-OOA, and SV-OOA) from all sites cluster within a well-defined triangular region in the f44 vs. f43 space, which can be used as a standardized means for comparing and characterizing any OOA components (laboratory or ambient) observed with the AMS. Examination of the OOA components in this triangular space indicates that OOA component spectra become increasingly similar to each other and to fulvic acid and HULIS sample spectra as f44 (a surrogate for O:C and an indicator of photochemical aging) increases. This indicates that ambient OA converges towards highly aged LV-OOA with atmospheric oxidation. The common features of the transformation between SV-OOA and LV-OOA at multiple sites potentially enable a simplified description of the oxidation of OA in the atmosphere. Comparison of laboratory SOA data with ambient OOA indicates that laboratory SOA are more similar to SV-OOA and rarely become as oxidized as ambient LV-OOA, likely due to the higher loadings employed in the experiments and/or limited oxidant exposure in most chamber experiments.


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