Using Cross-Correlation with Pattern Recognition Entropy to Obtain Reduced Total Ion Current Chromatograms from Raw Liquid Chromatography-Mass Spectrometry Data

Abstract

Abstract Total ion current chromatograms (TICCs) generated by liquid chromatography-mass spectrometry (LC-MS) are prone to noise from chemical and electronic sources. This noise can severely impact the detection of analytes in a mixture. Recently, we introduced a new variable selection tool based on Pattern Recognition Entropy (PRE) that selects good quality (high signal-to-noise ratio) mass chromatograms from an LC-MS dataset and thereby creates a reduced TICC with low noise and a flat background (J. Chrom. A.2018, 1558, 21–28). PRE, which is based on Shannon’s entropy, was shown to be a straightforward and powerful shape recognition tool for this problem. However, while the chromatographic signals in the reduced TICC from PRE were well resolved, some noise remained in the TICC, which suggested that the algorithm had selected some false positives, i.e., poor quality mass chromatograms. In this paper, we report an improved version of the PRE algorithm that utilizes a second variable selection filter based on cross-correlation (CC). As a check on the ability of PRE and CC to select high quality mass chromatograms, every mass chromatogram in our data set (1451 in total) was individually inspected and rated as either high quality (green), intermediate quality (yellow), or poor quality (red). A color-coded plot of the CC value vs. the PRE value for the mass chromatograms was created, which shows that, as expected, the higher quality mass chromatograms are localized in its upper left quadrant, which corresponds to lower PRE values and higher CC values. In our original paper on this topic, we recommended a threshold of 0.5 σ for PRE, which caused the algorithm to select 151 mass chromatograms out of 1451. Of these, 98 were of high quality, 6 were of intermediate quality, and 47 were of poor quality. Using a second threshold for CC, the algorithm retains all the high and intermediate quality mass chromatograms, while removing all 47 of the poor quality ones. The resulting TICC from the PRE-CC algorithm shows less noise compared to the TICC generated from the PRE approach alone. The PRE-CC algorithm is arguably a faster, simpler and more intuitive approach as compared to the widely used CODA_DW algorithm.