Identification and Quantification of Homologous Series of Compound in Complex Mixtures: Autocovariance Study of GC/MS Chromatograms

Nontargeted homologue series extraction from hyphenated high resolution mass spectrometry data

BACKGROUND

A large proportion of polar anthropogenic compounds routinely released into the environment comprises homologue series, i.e., sets of chemicals differing in a repeating chemical unit. Using analytical techniques such as liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS), these compounds are readily measurable as signal sets with characteristic differences in mass and typically retention time. However, and despite such distinct characteristics, no computational approach for the direct, simultaneous and untargeted detection of all such signal sets comprising both LC and HRMS information has to date been presented.

RESULTS

A fast two-staged approach has been developed to extract LC-HRMS signal patterns which can be indicative of homologous analytes. In a first stage, a k-d tree representation of picked LC-HRMS peaks is used to extract all feasible 3-tuples of peaks with restrictions in, e.g., mass defect differences. A second stage then recombines these 3-tuples to larger series tuples while ensuring smooth changes in their retention time characteristics. This unsupervised approach was evaluated for ten effluent samples from Swiss sewage treatment plants (STPs), in both positive and negative electrospray-ionization.

CONCLUSIONS

Beside recovering all continuous series of previously identified homologues, substantial fractions of nontargeted peaks could subsequently be assigned into very diverse peak series, although assignments were often not unique. The latter ambiguities were resolved by a self-organizing map technique and revealed both distinctive series meshing and rivaling combinatorial solutions in the presence of isobaric or gapped series peaks. When comparing STPs, several ubiquitous yet partially low-frequent series mass differences emerged and may prioritize future identification efforts. The presented algorithm is freely available as part of the R package nontarget and as a user-friendly web-interface at www.envihomolog.eawag.ch.Graphical AbstractSearch for systematic series indicative of homologous compounds is based on a partitioned representation of LC-HRMS signal characteristics. This nontargeted search first extracts series triplets in a nearest-neighbour walk and then recombines them to larger ones. For illustration, the two dimensions involving mass defect characteristics are represented by one only.

2D autocovariance function for comprehensive analysis of two-way GC-MS data matrix: application to environmental samples

This paper describes a signal processing method for comprehensive analysis of the large data set generated by hyphenated GC-MS technique. It is based on the study of the 2D autocovariance function (2D-EACVF) computed on the raw GC-MS data matrix, extending the procedure previously developed for 1D to 2D signals. It appears specifically promising for GC-MS investigation, in particular to single out ordered patterns in complex data: such patterns can be simply identified by visual inspection from deterministic peaks in the 2D-EACVF plot. A case of order along the retention time axis (x=t(R)) is represented by a horizontal sequence of peaks, located at the same interdistance Δt(R)=b(x), e.g., b(x) is the CH(2) retention time increment between subsequent terms of an homologous series. The order along the fragment mass axis (y=m/z) contains information on analyte fragmentation patterns. Deterministic peaks appear in the 2D-EACVF plot at Δm/z values corresponding to the most abundant ion fragments - dominating fragments in MS spectrum - or to ions generated by repetitive loss of the same ion fragment, i.e., Δm/z=14 amu produced by the [CH(2)]() group loss in n-alkanes. Method applicability was tested by processing GC-MS data of organic extracts of atmospheric aerosol samples: attention is focused on identifying and characterizing homologous series of organics, i.e., n-alkanes and n-alkanoic acids, since they are considered molecular tracers able to track the origin and fate of different organics in the environment.

Distribution of n-alkanes in the Northern Italy aerosols: data handling of GC-MS signals for homologous series characterization

The paper describes the characterization of n-alkane homologous series present in PM samples performed by gas chromatography-mass spectrometry analysis. The PM samples were collected in three locations in northern Italy: Milan, a large urban area, Oasi Bine, a rural site far from big city centers, and Alpe San Colombano, a remote, high altitude site in the Alps. They represent different particle sizes (PM(1), PM(2.5), PM(10)) and seasons (summer, fall, and winter). The analyzed samples were characterized in terms of PM total mass, total concentration of C(20)-C(32) n-alkanes and carbon preference index, CPI, to quantify the relative abundance of odd versus even n-alkanes. As alternative to the conventional method based on peak integration, a chemometric approach based on autocovariance function (EACVF) computation was found reliable to characterize the homologous series. In particular two parameters have proven useful chemical markers for tracking the biogenic and anthropogenic origins of n-alkanes: CPI(EACVF) and series %, estimating the % n-alkanes abundance relative to total alkane concentration. The investigated samples display a large variation in the n-alkanes relative abundance: the lowest values (series % = 1-14%) were found in summer and the highest (series % = 24-48%) in winter. In addition, a considerable seasonal variation of CPI(EACVF) values can be identified for all the sampling sites: the CPI(EACVF) values are close to 1 (CPI(EACVF) = 0.8-1.2) in the cold seasons, revealing a strong contribution from anthropogenic emissions, while spreader values (CPI(EACVF) = 0.9-3) were found in the warm season, that is, reflecting a variable contribution from biogenic sources in combination with anthropogenic emissions.

Signal processing of GC–MS data of complex environmental samples: Characterization of homologous series

Identification and characterization of homologous series by GC-MS analysis provide very relevant information on organic compounds in complex mixtures. A chemometric approach, based on the study of the autocovariance function, EACVF(tot), is described as a suitable tool for extracting molecular-structural information from the GC signal, in particular for identifying the presence of homologous series and quantifying the number of their terms. A data pre-processing procedure is introduced to transform the time axis in order to display a strictly homogenous retention pattern: n-alkanes are used as external standard to stretch or shrink the original chromatogram in order to build up a linear GC retention scale. This addition can be regarded as a further step in the direction of a signal processing procedure for achieving a systematic characterization of complex mixture from experimental chromatograms. The EACVF(tot) was computed on the linearized chromatogram: if the sample presents terms of homologous series, the EACVF(tot) plot shows well-defined deterministic peaks at repeated constant interdistances. By comparison with standard references, the presence of such peaks is diagnostic for the presence of the ordered series, their position can be related to the chemical structure of the compounds, their height is the basis for estimating the number of terms in the series. The power of the procedure can be magnified by studying SIM chromatograms acquired at specific m/z values characteristic of the compounds of interest: the EACVF(tot) on these selective signals makes it possible to confirm the results obtained from an unknown mixture and check their reliability. The procedure was validated on standard mixtures of known composition and applied to an unknown gas oil sample. In particular, the paper focuses on the study of two specific classes of compounds: n-alkanes and oxygen-containing compounds, since their identification provides information useful for characterizing the chemical composition of many samples of different origin. The robustness of the method was tested in experimental chromatograms obtained under unfavorable conditions: chromatograms acquired in non-optimal temperature program conditions and chromatographic data affected by signal noise.

Current literature in mass spectrometry

In order to keep subscribers up‐to‐date with the latest developments in their field, John Wiley & Sons are providing a current awareness service in each issue of the journal. The bibliography contains newly published material in the field of mass spectrometry. Each bibliography is divided into 11 sections: 1 Books, Reviews & Symposia; 2 Instrumental Techniques & Methods; 3 Gas Phase Ion Chemistry; 4 Biology/Biochemistry: Amino Acids, Peptides & Proteins; Carbohydrates; Lipids; Nucleic Acids; 5 Pharmacology/Toxicology; 6 Natural Products; 7 Analysis of Organic Compounds; 8 Analysis of Inorganics/Organometallics; 9 Surface Analysis; 10 Environmental Analysis; 11 Elemental Analysis. Within each section, articles are listed in alphabetical order with respect to author (6 Weeks journals ‐ Search completed at 4th. Oct. 2006)