Ohio University Center for Intelligent Chemical Instrumentation Libo Cao, Guoxiang Chen 1 , and Peter de B. Harrington 2 [email protected][email protected][email protected]Ohio University Center for Intelligent Chemical Instrumentation Department of Chemistry & Biochemistry, Athens, OH 45701-2979 1 Research & Development Scientist, Metara Inc., 1225 E Arques Ave, Sunnyvale, CA 94085-4701 2 Faculty Fellow, Idaho National Engineering and Environmental Lab, Idaho Falls, ID 83401-2208 Histogram Mass Spectra Lose Key Chemical Information: Are Wavelet Compressed Mass Spectral Profiles a Viable Alternative? The 50 th ASMS Conference, 2002
Transcript
Ohio University Center for Intelligent Chemical Instrumentation
Libo Cao, Guoxiang Chen1, and Peter de B. Harrington2
Ohio University Center for Intelligent Chemical Instrumentation
Abstract
An overlooked computational preprocessing step is the integration of mass spectral peaks and rendering a spectrum in the form of a histogram. With technological advances, mass spectral profiles can be processed thereby retaining information in the ion peak shapes. Although the size of the mass spectral profile can be quite larger than a histogram, nonlinear wavelet compression can reduce the spectra to a manageable size. This approach is important to other areas such as the mass measurement of large ions for which baseline resolution is unobtainable and integrated peak areas do convey the accurate abundance.
Principal component analysis (PCA) in many ways forms the basis for multivariate data analysis [1-2]. PCA provides an approximation of a data matrix, A as a product of two smaller matrices T and P that have orthogonal columns. Also the columns maximize the variance of A. Recently, there have been efforts to introduce data compression to chemometrics.
Data compression is able to reduce data size without losing important chemical information. Data noise can be lowered and implementation of algorithms can be greatly speeded up. Wavelet transform (WT) has gained a position as a popular compressing and denoising technique in the field of analytical chemistry due to its fast implementation, large number of available basis functions, and
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multiresolution ability. The WT technique has been exploited to processing absorbance spectra [3-4], chromatograms [5], and electrochemical signals [6].
Wavelet transform has been used with the other chemometric approaches such as PCA [7], partial least squares analysis [8], and artificial neural network [9]. Reviews and tutorials about wavelet transform are available [10].
Mass spectra were collected on a Hewlett Packard 5988 gas chromatograph-mass spectrometer (GC/MS). The data acquisition was acquired with a personal computer and the OS/2 Warp 4.0 operating system. The mass spectrometer was controlled using Prolab Vector/Two GC-LC/MS software, version 3.02.00. The GC/MS data were processed using a LabVIEW virtual instrument (VI) that performed histogram calculations at a resolution of 0.1 m/z and generated mass spectral profiles with a data point interval of 0.12 m/z. The same spectra were used for comparisons at different resolutions and formats. Difficult to distinguish isomers were used to demonstrate the benefits of using profiles instead of histograms. Three xylene isomers were run individually on the GC/MS and their spectra were collected as mass profiles. The spectra were converted to histograms with
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integrated peak areas using 0.1 mass resolutions. The spectra were then normalized, mean-centered and displayed on the first two or three principal components. The same spectra were treated as mass profiles. Resolution values were calculated by measuring the distance between each pair of averages for each isomer and dividing this value by the average of the two standard deviations about the averages multiplied by four.
References
[1] F. Malinowski and D. Howery, Factor Analysis in Chemistry; Wiley: New York, 1980
[2] L.S. Ramos, K.R. Beebe, W.P. Carey, E. Sanchez, B.C.Erickson, B.E. Wilson, L.E. Wangen and B.R. Kowalski, Anal. Chem.,
58 (1986) 294R-315R.
[3] B. Walczak and D. L. Massart, Chemomet. Intell. Lab. Syst., 36 (1997) 81.
[4] F. Ehrentreich and L. Sümmmchen, Anal. Chem. 73 (2001) 4364.
[5] J. Lasa, I. Sliwka, J. Rosiek and K. Wal, Chemia Analityczna, 46 (2001) 529.
[6] H. Chen, Anal. Chim. Acta 346 (1997) 319.
[7] B. Walczak and D. L. Massart, Chemomet. Intell. Lab. Syst. 38 (1997) 39.
[8] S. Ren and L. Gao, Talanta 50 (2000) 1163
[9] C. Cai and P. B. Harrington, J. Chem. Inf. Comput. Sci. 39 (1999) 874.
[10] B. Walczak, Wavelets in chemistry; Elsevier: Amsterdam, 2000.
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Principal Component Analysis (PCA)
PCA provides an approximation of a data matrix, A as a product of two smaller matrices T and P that have orthogonal columns. Also the columns maximize the variance of A.
A = TPT
A – A data matrix containing intensities from m/z 51 to m/z 150
measured from 30 spectra for the same chemical compound T – A 30 × n matrix that describes the mass spectra at different
retention times; n is number of components. PT – An n × 100 matrix that describes the mass spectral intensities
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PCA Algorithm
Var1
Var2
Var3 P1
ti
A data matrix X is represented as a cluster of N points in a K-dimensional space. This figure shows a three-dimensional space with a straight line fitted to the points: the line is a one-component PC model. The PC score of an object (ti) is its orthogonal projection onto the PC. The PC is also referred to as the variable loadings.
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Xylene Resolutions from Different Data Treatments
Histogram Binned (1.00) Binned (0.16)
m and o-xylene 2.15 2.08 2.77
m and p-xylene 0.98 1.25 3.42
o and p-xylene 1.50 1.34 2.11
Resolution
Isomers
Resolution values were calculated by measuring the distance between each pair of averages for each isomer and dividing this value by the average of the two standard deviations about the averages multiplied by four.
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Future Work
Wavelet compression for MS spectra
Data compression in analytical chemistry aims at major savings of
storage space and speeding up calculations. Using the wavelet
compressed profiles improved the resolution and allowed the
identification of the three isomers from their mass spectra.
Applications of automated pattern recognition
Because wavelet compressed profiles include useful information that is lost during integration of the mass spectral peaks. The integrated peak areas are typically reported as histograms for mass spectral data. More applications of automated pattern recognition should be explored.
Applications of automated pattern recognition
Because wavelet compressed profiles include useful information that is lost during integration of the mass spectral peaks. The integrated peak areas are typically reported as histograms for mass spectral data. More applications of automated pattern recognition should be explored.
