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False positive and false negative peaks detected from extracted ion chromatograms (EIC) are an urgent problem with existing software packages that preprocess untargeted liquid or gas chromatography-mass spectrometry metabolomics data because they can translate downstream into spurious or missing compound identifications. We have developed new algorithms that carry out the sequential construction of EICs and detection of EIC peaks. We compare the new algorithms to two popular software packages XCMS and MZmine 2 and present evidence that these new algorithms detect significantly fewer false positives. Regarding the detection of compounds known to be present in the data, the new algorithms perform at least as well as XCMS and MZmine 2. Furthermore, we present evidence that mass tolerance in m/z should be favored rather than mass tolerance in ppm in the process of constructing EICs. The mass tolerance parameter plays a critical role in the EIC construction process and can have immense impact on the detection of EIC peaks.

XCMS and MZmine 2 are two widely used software packages for preprocessing untargeted LC/MS metabolomics data. Both construct extracted ion chromatograms (EICs) and detect peaks from the EICs, the first two steps in the data preprocessing workflow. While both packages have performed admirably in peak picking, they also detect a problematic number of false positive EIC peaks and can also fail to detect real EIC peaks. The former and latter translate downstream into spurious and missing compounds and present significant limitations with most existing software packages that preprocess untargeted mass spectrometry metabolomics data. We seek to understand the specific reasons why XCMS and MZmine 2 find the false positive EIC peaks that they do and in what ways they fail to detect real compounds. We investigate differences of EIC construction methods in XCMS and MZmine 2 and find several problems in the XCMS centWave peak detection algorithm which we show are partly responsible for the false positive and false negative compound identifications. In addition, we find a problem with MZmine 2's use of centWave. We hope that a detailed understanding of the XCMS and MZmine 2 algorithms will allow users to work with them more effectively and will also help with future algorithmic development.

By coupling a single UPLC separation to two different types of mass spectrometer an unbiased comparison of the metabolite profiles produced by each instrument for a set of rat urine samples was obtained. The flow from the UPLC column was split equally and both streams of eluent were simultaneously directed to the inlets of the two mass spectrometers. Mass spectrometry on the eluent was undertaken using a triple quadrupole linear ion trap and a hybrid quadrupole time-of-flight mass spectrometer using both positive and negative ESI. Data from both mass spectrometers were subjected to multivariate statistical analysis, after applying the same data extraction software, and showed the same general pattern of correlation between the samples using both unsupervised and supervised methods of statistical analysis. Based on orthogonal partial least-squares discriminant analysis models a number of ions were recognized as "responsible" for the separation of the animal groups. From the peaks detected, and denoted as significant by the statistical analysis a number of ions were found to be unique to one data set or the other, a result which may have consequences for biomarker discovery and interlaboratory comparisons. The software package used for data analysis also had an effect on the outcome of the statistical analysis.