Proper Use of Spectral Processing

While those who used the MALDI-TOF MS data had a higher accuracy of positive assignments, this study indicated there are many respondents who are not well versed in the proper use of mass spectrometry data. Respondents can use mass spectra data to confirm sequence assignments within approximately 0.1% accuracy of the measured mass, to estimate the length of a peptide, and to potentially confirm tentative amino acids. The mass spectral data should not be used to estimate the ratios of ions present. The web sites listed in Table II provide a ready resource to aid in this process. 

There are few possible strategies of library compression. Each of them has its own advantages and drawbacks. The most efficient method of data set compression, i.e. Principal Component Analysis (PCA), leads to use of global features. As demonstrated in [15] global features such as PCs (or Fourier coefficients) are not best suited for a calibration or classification purposes. Often, quite small, well-localized differences between objects determine the very possibility of their proper classification. For this reason wavelet transforms seem to be promising tools for compression of data sets which are meant to be further processed. However, even if we limit ourselves only to wavelet transforms, still the problem of an approach optimally selected for a particular purpose remains. There is no single method, which fulfills all requirements associated with a spectral library s compression at once. Here we present comparison of different methods in a systematic way. The approaches A1-A4 above were applied to library compression using 21 filters (9 filters from the Daubechies family, 5 Coiflets and 7 Symmlets, denoted, respectively as filters Nos. 2-10, 11-15 and 16-22). 

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