Pure ion profiles

In X-HRMS, each scan is an image of the composition of the mixture leaving the separation device. A pure ion profile (PIP) is obtained with centroid dataset and consists in regrouping along every scan all data points that are related to one and only one ion. This is usually done by assuming that consecutive points belong to the same ions if their accurate masses do not differ by more than a set threshold. Pure ion profiles algorithms will try to extract from a dataset the profiles of all ions resulting from the separation of the compounds from the matrix by the separation technique and their ionisation and separation by the mass spectrometer. Once PIP obtained, figures of merits (e.g. peak area, centre, variances...) can easily be calculated and summarised inside a table often known as the peak list.

the PeakList object

In the toolbox, the PeakList object deals with extraction of pure ion profiles, the peak list as well as with various representation associated with it. It is an independent object that will be saved in the .fin folder. To object is initialise using the following command:

myPeakList = PeakList(dtsIn, thrInt, deltmz, minPts)

where dtsIn is a centroid Dataset (i.e. myFinnee.Datasets{4}) and deltmz is the m/z threshold that will determine if two points in successive scans belong to the same PIP. Typically deltmz should be bellow 0.1 m/z. Only PIPs with more than minPts and with, at least, one point of intensity higher than thrInt will be recorded. The value for minPts depends on the MS scan rate and profile peak width, but should not be bellow 5. The value for thrInt should be equal or greater than the threshold used for noise removal. The PIP algorithms developed for PeakList should work for any centroid dataset. However, it was designed for datasets that were corrected for baseline drift and background noise. Best results will be obtained if those steps were performed. For example with the CE-TOFMS dataset, we used

myPeakList = PeakList(myFinnee.Datasets{5}, 100, 0.1, 5);

where Datasets{5} was obtained as follow:

1. Noise reduction. myFinnee = myFinnee.filterDataset(1, 'RemoveNoise:5:5:100');
2. Baseline correction. myFinnee = myFinnee.BaselineCorrection(2);
3. Noise reduction. myFinnee = myFinnee.filterDataset(3, 'RemoveNoise:5:5:100');
4. Centroidisation. myFinnee = myFinnee.doCentroid(4, 'LocalMax:2:0');

PeakList objects contain the list of all PIPS that were constructed from the dataset as well as associated figures of merits (FOM).

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