Procrustes analysis for the determination of number of significant masses in gas chromatography–mass spectrometry

In GC–MS, typically, the majority of masses are of no significance. The aim of this paper is to show how the influence of increasing numbers of masses affect the information content of GC–MS data. Two closely eluting peaks arising from salbutamol and clenbuterol are analysed. Principal component analysis is performed using 10 and 50 masses. The patterns formed using two principal components are compared by procrustes analysis, involving scaling, rotating and reflecting the data. The influence of increasing the number of masses is discussed. The change in pattern is quantified using the root mean square difference between the scores using 50 masses and a smaller number of masses.

[1]  Frode Brakstad,et al.  The feasibility of latent variables applied to GC-MS data , 1995 .

[2]  Javier Andrade,et al.  Procrustes Rotation as a Way To Compare Different Sampling Seasons in Soils , 1995 .

[3]  Stephen P. Gurden,et al.  Use of eigenvalues for determining the number of components in window factor analysis of spectroscopic and chromatographic data , 1995 .

[4]  R. Brereton Tutorial review. Deconvolution of mixtures by factor analysis , 1995 .

[5]  Javier Andrade,et al.  Selection of analytical variables to optimize laboratory efforts in future groundwater studies , 1994 .

[6]  Bruce R. Kowalski,et al.  Comments on the DATa ANalysis (DATAN) algorithm and rank annihilation factor analysis for the analysis of correlated spectral data , 1994 .

[7]  H. Lohninger,et al.  Classification of mass spectra: A comparison of yes/no classification methods for the recognition of simple structural properties , 1994 .

[8]  J. Méndez,et al.  Chemometric study of organic pollution in the aerosol of Madrid , 1993 .

[9]  Mikael Kubista,et al.  Analysis of Correlated Spectral Data , 1993 .

[10]  R. Brereton,et al.  Resolution of strongly overlapping two‐way multicomponent data by means of heuristic evolving latent projections , 1993 .

[11]  Mikael Kubista,et al.  A new method for the analysis of correlated data using procrustes rotation which is suitable for spectral analysis , 1990 .

[12]  Donald R. Scott,et al.  Preprocessing, variable selection, and classification rules in the application of SIMCA pattern recognition to mass-spectral data , 1989 .

[13]  Bruce R. Kowalski,et al.  An improved algorithm for the generalized rank annihilation method , 1989 .

[14]  H. P. Dupuy,et al.  Chemical and Instrumental Analyses of Warmed‐Over Flavor in Beef , 1987 .

[15]  L. Hwang,et al.  Reconstruction of mass spectra of components of unknown mixtures based on factor analysis , 1981 .

[16]  F. J. Knorr,et al.  Multichannel detection and numerical resolution of overlapping chromatographic peaks , 1981 .

[17]  B. Kowalski,et al.  Extraction of individual mass spectra from gas chromatography-mass spectrometry data of unseparated mixtures , 1981 .

[18]  Edmund R. Malinowski,et al.  Factor Analysis in Chemistry , 1980 .

[19]  J. Futrell,et al.  Separation of mass spectra of mixtures by factor analysis , 1979 .

[20]  G. L. Ritter,et al.  Factor analysis of the mass spectra of mixtures , 1976 .

[21]  R. W. Rozett,et al.  Methods of factor analysis of mass spectra , 1975 .

[22]  L. B. Rogers,et al.  Principal-component analysis applied to combined gas chromatographic-mass spectrometric data , 1974 .

[23]  W. H. Elliott,et al.  Mass spectrometric determination of unresolved components in gas chromatographic effluents. , 1966, Analytical chemistry.