I kept human bias to a minimum by handling and preparing all samples in the same way throughout. We also had a few problems with contaminants, particularly for poor-quality samples which had low detections for 'real' compounds - in this instance I used targeted mass spectra searches with a minimum peak size and mass spectra quality threshold to look for particular compounds. If we'd left it up to an automated program, many of the peaks would have been incorrectly assigned. I was working on scent samples collected from wild echidnas the results of GC-MS analyses were complex and we identified several homologous series' of different compounds (many of which were not in our search databases). Hi, I'm getting in a little late here, but I agree with Jeremy - I aligned chromatogram peaks manually. However, because the peaks are discrete, one probably cannot do things like gradient search to get the optimum. I am quite sure this is not the most efficient apprach, though. In a brute force approach this would mean a little bit more than 100*100 = 10000 chromatogram similarity calculations for every combination of a and b, to find the best combination of a and b among them. If it is, let's say 0.1% of the chromatogram width, then a and b could be incremented or decremented by a number slightly smaller than 1/1000th (or 1/1000th of tmax for parameter b). The step by which these values could then be changed in a numerical optimization depends on typical peak width. Say if the drift was not expected to be more than 10%, parameter a would fall between 0.9-1.1 and b between 0.9*tmax - 1.1*tmax. Otherwise they could be limited by reasonable ranges. If there was a peak or a number of peaks that were always present in both samples, an initial estimate for a and b could be obtained by linear regression.
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