Abstract
Polymers are a common component of chemical background which complicates data analysis and can impair interpretation. Undesired chemical background cannot always be addressed via pre-analytical methods, chromatography, or existing data processing methods. The Kendrick mass filter (KMF) is presented for the computational removal of undesired signals present in MS(1) spectra. The KMF is analogous to mass defect filtering but utilizes homology information via Kendrick mass scaling in combination with chromatographic retention time and the number of observed signals. The KMF is intended to assist in situations in which current data processing methods to remove background, e.g., blank subtraction, are either not possible or effective. The major parameters affecting KMF were investigated using PEG 400 and NIST standard reference material 1950 (metabolites in human plasma). Further exploration of the KMF performance was tested using an extract of a swab known to contain polymers. An illustrative real-world example of skin analysis with polymeric signal is discussed. The KMF is also able to provide a high-level view of the compositionality of data regarding the presence of signals with repeat units and indicate the presence of different polymers. Graphical Abstract ᅟ.