Both for the general performance evaluation of the CNL model and the comparison with conventional methods, potential fragment signals needed to be extracted from all the data files and processed using these fragment deconvolution methods. Therefore, in this section, the general data preprocessing method will be described for all performance evaluations using real samples. The raw data files were converted with MSConvertGUI (64-bit, ProteoWizard ) to mzXML format, using an 100 counts per second absolute intensity threshold. MS1 feature lists were obtained for these files using the self adjusting feature detection (SAFD) algorithm. The following settings were used: 10,000 maximum number of iterations, 20,000 resolution, minimum intensity of 500 counts, minimum mass domain window size 0.02 Da, 0.75 minimum regression coefficient, a maximum signal increment of 5 percent, a signal to background ratio of 2, and an allowed peak width in the time domain between 3 and 200 scans, respectively.