scholarly journals Forensic examination of textile fibres using Raman imaging and multivariate analysis

Author(s):  
Félix Zapata ◽  
Fernando E. Ortega-Ojeda ◽  
Carmen García-Ruiz
2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Phiranuphon Meksiarun ◽  
Mika Ishigaki ◽  
Verena A.C. Huck-Pezzei ◽  
Christian W. Huck ◽  
Kanet Wongravee ◽  
...  

2016 ◽  
Vol 47 (10) ◽  
pp. 1239-1246 ◽  
Author(s):  
Sara Fateixa ◽  
Manon Wilhelm ◽  
Helena I. S. Nogueira ◽  
Tito Trindade
Keyword(s):  

2015 ◽  
Vol 821-823 ◽  
pp. 241-244
Author(s):  
Olga Milikofu ◽  
Tomomi Kozu ◽  
Tim Batten

New ultrafast Raman imaging methods allow high definition data to be collected from a whole wafer scale down to individual defects in 2D and 3D, on a time scale suitable for routine quality control. On the other hand, just one hour of data collection can result in datasets containing 105~106 spectra, and attempts to manually analyze such big data with traditional univariate methods can take days, without guarantee that all important information is revealed. Such problem can be easily overcome with fast and automated multivariate analysis methods. Here we introduce the techniques and demonstrate applications to SiC.


2008 ◽  
Vol 392 (7-8) ◽  
pp. 1277-1282 ◽  
Author(s):  
Alois Bonifacio ◽  
Sara Finaurini ◽  
Christoph Krafft ◽  
Silvia Parapini ◽  
Donatella Taramelli ◽  
...  

2019 ◽  
Vol 103 (16) ◽  
pp. 6759-6769 ◽  
Author(s):  
Jie Li ◽  
Jie Qin ◽  
Xu Zhang ◽  
Rui Wang ◽  
Zhuowen Liang ◽  
...  

Author(s):  
Diego Quintero Balbas ◽  
Giancarlo Lanterna ◽  
Claudia Cirrincione ◽  
Marilena Ricci ◽  
Maurizio Becucci ◽  
...  

1966 ◽  
Vol 24 ◽  
pp. 188-189
Author(s):  
T. J. Deeming

If we make a set of measurements, such as narrow-band or multicolour photo-electric measurements, which are designed to improve a scheme of classification, and in particular if they are designed to extend the number of dimensions of classification, i.e. the number of classification parameters, then some important problems of analytical procedure arise. First, it is important not to reproduce the errors of the classification scheme which we are trying to improve. Second, when trying to extend the number of dimensions of classification we have little or nothing with which to test the validity of the new parameters.Problems similar to these have occurred in other areas of scientific research (notably psychology and education) and the branch of Statistics called Multivariate Analysis has been developed to deal with them. The techniques of this subject are largely unknown to astronomers, but, if carefully applied, they should at the very least ensure that the astronomer gets the maximum amount of information out of his data and does not waste his time looking for information which is not there. More optimistically, these techniques are potentially capable of indicating the number of classification parameters necessary and giving specific formulas for computing them, as well as pinpointing those particular measurements which are most crucial for determining the classification parameters.


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