Principal components analysis for the visualisation of multidimensional chemical data acquired by scanning Raman microspectroscopy

The Analyst ◽  
2002 ◽  
Vol 127 (9) ◽  
pp. 1261-1266 ◽  
Author(s):  
Michael Malecha ◽  
Conrad Bessant ◽  
Selwayan Saini
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Raman Microspectroscopy ◽  
Chemical Data ◽  
Components Analysis

scholarly journals Implications of measurement error structure on the visualization of multivariate chemical data: hazards and alternatives

2018 ◽  
Vol 96 (7) ◽  
pp. 738-748 ◽  
Author(s):  
Peter D. Wentzell ◽  
Chelsi C. Wicks ◽  
Jez W.B. Braga ◽  
Liz F. Soares ◽  
Tereza C.M. Pastore ◽  
...  
Keyword(s):  
Data Analysis ◽  
Measurement Error ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Measurement Errors ◽  
Alternative Methods ◽  
Chemical Data ◽  
Multidimensional Data ◽  
Chemical Information ◽  
Components Analysis

The analysis of multivariate chemical data is commonplace in fields ranging from metabolomics to forensic classification. Many of these studies rely on exploratory visualization methods that represent the multidimensional data in spaces of lower dimensionality, such as hierarchical cluster analysis (HCA) or principal components analysis (PCA). However, such methods rely on assumptions of independent measurement errors with uniform variance and can fail to reveal important information when these assumptions are violated, as they often are for chemical data. This work demonstrates how two alternative methods, maximum likelihood principal components analysis (MLPCA) and projection pursuit analysis (PPA), can reveal chemical information hidden from more traditional techniques. Experimental data to compare different methods consists of near-infrared (NIR) reflectance spectra from 108 samples of wood that are derived from four different species of Brazilian trees. The measurement error characteristics of the spectra are examined and it is shown that, by incorporating measurement error information into the data analysis (through MLPCA) or using alternative projection criteria (i.e., PPA), samples can be separated by species. These techniques are proposed as powerful tools for multivariate data analysis in chemistry.

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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