Decomposition of multi‐component fluorescence spectra by narrow peak method based on principal component analysis

2021 ◽  
Author(s):  
Elena Tsibulskaya ◽  
Nikolay Maslov
Keyword(s):  
Principal Component Analysis ◽  
Fluorescence Spectra ◽  
Principal Component ◽  
Component Analysis ◽  
Narrow Peak

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.

Application of principal component analysis and self-organizing map to the analysis of 2D fluorescence spectra and the monitoring of fermentation processes

2006 ◽  
Vol 11 (5) ◽  
pp. 432-441 ◽  
Author(s):  
Jong Il Rhee ◽  
Tae-Hyoung Kang ◽  
Kum-Il Lee ◽  
Ok-Jae Sohn ◽  
Sun-Yong Kim ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Fluorescence Spectra ◽  
Principal Component ◽  
Component Analysis ◽  
Self Organizing Map ◽  
Fermentation Processes ◽  
Self Organizing
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