Ordinal analysis applied to the results of positive matrix factorization of chemical ionization mass spectrometry data

As an innovative analytical approach ordinal analysis is applied to positive matrix factorization (PMF) analysis outputs to identify the most important species and factors in chemical ionization mass spectrometry (CIMS) data. The procedure and outcome of the ordinal analysis facilitate further automated data analysis. Prior to standard PMF analysis, CIMS data were normalized to assure equal comparisons and facilitate the analysis process. The ordinal analysis was applied to the Factor Profiles (FPs) results, where mass numbers m/z are ranked by their FP fractions. Such ranking seeks the most influential compounds leading each factor, and the top m/z can be further investigated, e.g. by peak assignments. Rank maps can be plotted based on the ordinal results where the FPs are converted into a different space, which can potentially be used for cluster analysis. The rank maps provide an additional method for factor identification, especially when time series or other forms of the dataset are difficult to recognize. • Ordinal analysis identifies the most important fingerprint species leading each factor. • Rank map visualizes the features of each factors. • The method can be used as an online approach for source appointments of atmospheric pollutants.