Application of the principal components analysis technique to optical fiber sensors for acetone detection

2021 ◽  
Vol 143 ◽  
pp. 107314
Author(s):  
J.L. Rodríguez-Garciapiña ◽  
G. Beltrán-Pérez ◽  
J. Castillo-Mixcóatl ◽  
S. Muñoz-Aguirre
Keyword(s):  
Optical Fiber ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Optical Fiber Sensors ◽  
Fiber Sensors ◽  
Analysis Technique ◽  
Components Analysis ◽  
Acetone Detection

scholarly journals Collagen IV and laminin-1 expression in embryonic mouse lens using principal components analysis technique

2018 ◽  
Vol 271 (2) ◽  
pp. 207-221 ◽  
Author(s):  
O. SIJILMASSI ◽  
J.M. LÓPEZ ALONSO ◽  
M.C. BARRIO ASENSIO ◽  
A. DEL RÍO SEVILLA
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Collagen Iv ◽  
Embryonic Mouse ◽  
Analysis Technique ◽  
Components Analysis ◽  
Mouse Lens ◽  
Laminin 1

Diagnoses Generated by Numerical Taxonomic Methods Applied to Standard Blood Variables

1980 ◽  
Vol 19 (04) ◽  
pp. 205-209
Author(s):  
L. A. Abbott ◽  
J. B. Mitton
Keyword(s):  
Liver Cirrhosis ◽  
Renal Disease ◽  
Principal Components Analysis ◽  
Discriminant Function ◽  
Principal Components ◽  
Discriminant Function Analysis ◽  
Chronic Renal Disease ◽  
Function Analysis ◽  
Infectious Hepatitis ◽  
Components Analysis

Data taken from the blood of 262 patients diagnosed for malabsorption, elective cholecystectomy, acute cholecystitis, infectious hepatitis, liver cirrhosis, or chronic renal disease were analyzed with three numerical taxonomy (NT) methods : cluster analysis, principal components analysis, and discriminant function analysis. Principal components analysis revealed discrete clusters of patients suffering from chronic renal disease, liver cirrhosis, and infectious hepatitis, which could be displayed by NT clustering as well as by plotting, but other disease groups were poorly defined. Sharper resolution of the same disease groups was attained by discriminant function analysis.

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