scholarly journals Adaptation of principal component analysis for continuous laser-induced fluorescence spectra decomposition. Krf excimer laser excitation examples

2018 ◽  
Author(s):  
N. A. Maslov
Keyword(s):  
Principal Component Analysis ◽  
Excimer Laser ◽  
Fluorescence Spectra ◽  
Laser Excitation ◽  
Principal Component ◽  
Component Analysis ◽  
Laser Induced Fluorescence ◽  
Continuous Laser ◽  
Krf Excimer Laser

Fluorescence Spectra Recognition of Hypertriglyceridemia Serum Using Principal Component Analysis and Probabilistic Neural Networks

2011 ◽  
Vol 40 (11) ◽  
pp. 1641-1645
Author(s):  
李鹏 LI Peng ◽  
周建民 ZHOU Jianmin ◽  
赵志敏 ZHAO Zhimin
Keyword(s):  
Neural Networks ◽  
Principal Component Analysis ◽  
Fluorescence Spectra ◽  
Principal Component ◽  
Component Analysis ◽  
Probabilistic Neural Networks

scholarly journals Organic colorants based on lac dye and brazilwood as markers for a chronology and geography of medieval scriptoria: a chemometrics approach

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Paula Nabais ◽  
Maria J. Melo ◽  
João A. Lopes ◽  
Márcia Vieira ◽  
Rita Castro ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Hierarchical Cluster Analysis ◽  
Fluorescence Spectra ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Component Analysis ◽  
Excitation Spectra ◽  
Molecular Fluorescence ◽  
Lac Dye

AbstractThis work presents the first proof of concept for the use of molecular fluorescence signatures in medieval colours based on lac dye and brazilwood lake pigments. These two important medieval dyes were tested as markers using their UV–Visible emission and excitation spectra. These medieval paints had been previously fully characterized through a multi-analytical approach. In this work, molecular fluorescence spectra were acquired in manuscripts dating from 12th to 15th c., which were produced in monastic scriptoria or workshops. First, the spectral distribution and relative intensity of the emission and excitation spectra were discussed in detail by comparison with reference compounds, including reproductions of paints based on medieval technical texts. It was possible to group the spectra according to recipe specificities. Then, statistical methods (principal component analysis and hierarchical cluster analysis) were applied to the same fluorescence spectra and the generated clusters were compared with the previous ones. Principal component analysis was initially employed to eliminate redundancy in fluorescence data, so minimizing bias on the hierarchical cluster analysis results. Except for some misplaced spectra, the placement of samples per group was confirmed. The outliers resulted from either a poor signal to noise ratio or occurred because certain paints were unique, such as the colour produced by mixing lac dye and brazilwood, which was found in manuscripts from the Alcobaça monastic scriptorium. Previously, by using infrared or Raman spectroscopies, only lac dye could be detected. Notably, these paints compare well with a recipe that was reproduced from the text by Jean Le Begue, in which both dyes were required.

scholarly journals Fungus Covered Insulator Materials Studied with Laser-Induced Fluorescence and Principal Component Analysis

2005 ◽  
Vol 59 (8) ◽  
pp. 1037-1041 ◽  
Author(s):  
M. Bengtsson ◽  
S. Wallström ◽  
M. Sjöholm ◽  
R. Grönlund ◽  
B. Anderson ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Fiber Optic ◽  
Fungal Growth ◽  
Principal Component ◽  
Component Analysis ◽  
Laser Induced Fluorescence ◽  
Nitrogen Laser ◽  
Clean Surface ◽  
Principal Component Analyses ◽  
Channel Analyzer

A method combining laser-induced fluorescence and principal component analysis to detect and discriminate between algal and fungal growth on insulator materials has been studied. Eight fungal cultures and four insulator materials have been analyzed. Multivariate classifications were utilized to characterize the insulator material, and fungal growth could readily be distinguished from a clean surface. The results of the principal component analyses make it possible to distinguish between algae infected, fungi infected, and clean silicone rubber materials. The experiments were performed in the laboratory using a fiber-optic fluorosensor that consisted of a nitrogen laser and an optical multi-channel analyzer system.

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