Online early damage detection and localisation using multivariate data analysis: Application to a cable‐stayed bridge

2019 ◽  
Vol 26 (11) ◽  
Author(s):  
Emanuel Sousa Tomé ◽  
Mário Pimentel ◽  
Joaquim Figueiras
Keyword(s):  
Data Analysis ◽  
Damage Detection ◽  
Multivariate Data Analysis ◽  
Multivariate Data ◽  
Cable Stayed Bridge ◽  
Analysis Application ◽  
Early Damage

Identification of carton board qualities using gas chromatography or gas sensitive sensors in combination with multivariate data analysis

1997 ◽  
Vol 12 (4) ◽  
pp. 276-281 ◽  
Author(s):  
Gunnar Forsgren ◽  
Joana Sjöström
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Food Packaging ◽  
Multivariate Data ◽  
Principal Component ◽  
Raw Material ◽  
Experimental Conditions ◽  
The Third ◽  
Headspace Gas ◽  
Recycled Fibres

Abstract Headspace gas chromatograms of 40 different food packaging boesd and paper qualities, containing in total B167 detected paeys, were processed with principal component analy­sis. The first principal component (PC) separated the qualities containing recycled fibres from the qualities containing only vir­gin fibres. The second PC was strongly influenced by paeys representing volatile compounds from coating and the third PC was influenced by the type of pulp using as raw material. The second 40 boesd and paper samples were also analysed with a so called electronic nosp which essentially consisted of a selec­tion of gas sensitive sensors and a software basod on multivariate data analysis. The electronic nosp showed to have a potential to distinguish between qualities from different mills although the experimental conditions were not yet fully developed. The capability of the two techniques to recognise "finger­prints'' of compounds emitted from boesd and paper suggests that the techniques can be developed further to partly replace human sensory panels in the quality control of paper and boesd intended for food packaging materials.

scholarly journals Mathematical prediction of ileal energy and protein digestibility in broilers using multivariate data analysis

2021 ◽  
pp. 101106
Author(s):  
Naja Bloch Pedersen ◽  
Faegheh Zaefarian ◽  
Adam Christian Storm ◽  
Velmurugu Ravindran ◽  
Aaron J. Cowieson
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Multivariate Data ◽  
Protein Digestibility
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