Analysis of complex mixtures recovered from space missions statistical approach to the study of Titan atmosphere analogues (tholins)

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.

Gas chromatography–mass spectrometry analysis of amino acid enantiomers as methyl chloroformate derivatives: Application to space analysis

This work describes a GC-MS method for enantioselective separation of amino acids. The method is based on a derivatization reaction which employs a mixture of alkyl chloroformate-alcohol-pyridine, as reagents to obtain the N(O,S)-alkyl alkoxy carbonyl esters of amino acids. Various reaction parameters are investigated and optimized to achieve a reproducible derivatization procedure suitable for separation of amino acid enantiomers on Chirasil-L-Val chiral stationary phase. In particular, the following topics are investigated for 20 proteinogenic amino acids: (i) the proper reagent and reaction conditions to obtain the highest derivative yield; (ii) the amino acid reactivity and the MS properties of the obtained derivatives; (iii) the linearity and sensitivity of the analytical method; (iv) the retention behavior of the derivatives and their enantiomeric separation on the Chirasil-L-Val chiral stationary phase. By combining the resolution power of the Chirasil-L-Val column and the high selectivity of the SIM MS detection mode, the described procedure enables the enantiomeric separation and quantification of 16 enantiomeric pairs of amino acids. The procedure is simple and fast and reproducible. It displays a wide linearity range at ppb detection limits for quantitative determinations: these properties make this derivatization method a suitable candidate for amino acid GC-MS analysis on board of the spacecrafts in space exploration missions of solar system body environments.

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

The paper describes a method for determining homologous classes of compounds in a multicomponent complex chromatogram obtained under programming elution conditions. The method is based on the computation of the autocovariance function of the experimental chromatogram (EACVF). The EACVF plot, if properly interpreted, can be regarded as a "class chromatogram" i.e., a virtual chromatogram formed by peaks whose positions and heights allow identification and quantification of the different homologous series, even if they are embedded in a random complex chromatogram. Theoretical models were developed to describe complex chromatograms displaying random retention pattern, ordered sequences or a combination of them. On the basis of theoretical autocovariance function, the properties of the chromatogram can be experimentally evaluated, under well-defined conditions: in particular, the two components of the chromatogram, ordered and random, can be identified. Moreover, the total number of single components (SCs) and the separated number of the SCs belonging to the random and ordered components can be determined, when the two components display the same concentration. If the mixture contains several homologous series with common frequency and different phase values, the number and identity of the different homologous series as well as the number of SCs belonging to each of them can be evaluated. Moreover, the power of the EACVF method can be magnified by applying it to the single ion monitoring (SIM) signals to selectively detect specific compound classes in order to identify the different homologous series. By this way, a full "decoding" of the complex multicomponent chromatogram is achieved. The method was validated on synthetic mixtures containing known amount of SCs belonging to homologous series of hydrocarbon, alcohols, ketones, and aromatic compounds in addition to other not structurally related SCs. The method was applied to both the total ion monitoring (TIC) and the SIM signals, to describe step by step the essence of the procedure. Moreover, the systematic use of both SIM and TIC can simplify the decoding procedure of complex chromatograms by singling out only specific compound classes or by confirming the identification of the different homologous series. The method was further applied to a sample containing unknown number of compounds and homologous series (a petroleum benzin, bp 140-160 degrees C): the results obtained were meaningful in terms of both the identified number of components and identified homologous series.

In situ analysis of the Martian soil by gas chromatography: Decoding of complex chromatograms of organic molecules of exobiological interest

Gas chromatography-mass spectrometry (GC-MS) will be used in future space exploration missions, in order to seek organic molecules at the surface of Mars, and especially potential chemical indicators of life. Carboxylic acids are among the most expected organic species at the surface of Mars, and they could be numerous in the analysed samples. For this reason, a chemometric method was applied to support the interpretation of chromatograms of carboxylic acid mixtures. The method is based on AutoCovariance Function (ACVF) in order to extract information on the sample--number and chemical structure of the components--and on separation performance. The procedure was applied to standard samples containing targeted compounds which are among the most expected to be present in the Martian soil: n-alkanoic and benzene dicarboxylic acids. ACVF was computed on the obtained chromatograms and plotted versus retention time: peaks of the ACVF plot can be related to specific molecular structures and are diagnostic for chemical identification of compounds.

GC?MS in Space Research: Decoding Complex Isothermal Chromatograms Recovered from Space Missions

An analytical procedure is described to study GC-MS isothermal chromatograms simulating those recovered from space missions: in fact GC plays a predominant role in space missions devoted to characterizing the chemical composition of extra-terrestrial atmospheres. SIM (selected ion monitoring) detection was used for monitoring selected chemical classes: a simplified chromatogram can be obtained giving information on the chemical composition of the complex mixture. Since only isothermal GC chromatograms are allowed by flight constraints, a time axis transformation is required to make them homogeneous: i.e., constant retention increments for CH2 additions in terms of a homologous series. The order in the linearized chromatogram can be simply singled out with a chemometric approach based on the study of the Autocovariance Function (ACVF) computed on the digitized chromatogram: the plot of the experimental autocorrelation function (EACF) shows well-shaped peaks if constant interdistances are repeated in different regions of the chromatogram. The method was applied to standard mixtures representative of planetary atmospheres--hydrocarbons, nitriles and oxygenated compounds with between 3 and 12 carbon atoms--analyzed in flight simulating conditions. The coupling of the selectivity of SIM detection with the interpretation power of the EACF procedure proves to be a powerful tool for interpreting data recovered from space missions: the chemical composition of the mixture can be identified by handling the raw SIM chromatograms.

Decoding of complex isothermal chromatograms recovered from space missions. Identification of molecular structure

A chemometric approach, based on the study of the autocovariance function, is described to study isothermal GC chromatograms of multicomponent mixtures: isothermal GC analysis is the method of choice in space missions since it is, to date, the only method compatible with flight constraints. Isothermal GC chromatograms look inhomogeneous and disordered with peak density decreasing at higher retention times: a time axis transformation is proposed to make retention an homogeneous process so that CH2 addition in terms of an homologous series yields a constant retention increment. The time axis is transformed into a new scale based on the retention times of n-alkanes, as they are the basis of the universal Kovats indices procedure. The order introduced into the chromatogram by retention time linearization can be simply singled out by the experimental autocorrelation function (EACF) plot: if constant inter-distances are repeated in different regions of the chromatogram, well-shaped peaks are evident in the EACF plot. By comparison, with a standard mixture it is possible to identify peaks diagnostic of specific molecular structures: study of the EACF plot provides information on sample chemical composition. The procedure was applied to standard mixtures containing compounds representative of the planetary atmospheres that will be investigated in the near future: in particular, those related to Titan's atmosphere (Cassini-Huygens mission) and cometary's nucleus (Rosetta mission). The employed experimental conditions simulated those applied to GC instruments installed on space probes and landers in space missions. The method was applied to two specific investigations related to space research, i.e., a comparison of retention selectivity of different GC columns and identification of the chemical composition of an unknown mixture.

Statistical-overlap theory of column switching in gas chromatography: applications to flavor and fragrance compounds

First-column gas chromatograms (GCs) of hundreds of flavor and fragrance compounds, and second-column GCs of specific regions of these GCs, are predicted using thermodynamic databases in commercial software. A statistical-overlap theory of column switching with cryogenic focusing then is developed by mimicking the predicted GCs by two kinds of Monte Carlo simulations. In the first kind, a probability distribution is calculated for the number of compounds in a region of the first-column GC, based on the number of observed peaks in the region, the number of observed peaks in the second-column GC, and the retention-time distributions and breadths of single-component peaks in both GCs. In the second kind, criteria are established for the theory's application. The theory is applied to 12 regions of first-column GCs. The theory predicts the number of compounds in all of them and shows that separation rarely is complete in second-column GCs, when 10 or more compounds are transferred between columns. The theory also rationalizes the tedious search required to find good separation conditions by showing that column-switching gas chromatography with cryogenic focusing is inherently statistical. The number of peaks in the second-column GC can be greater than, less than, or equal to the number of peaks in the relevant region of the first-column GC, and the good conditions sought by researchers to substantially improve separation correspond to favorable "rolls of the dice" found only by trial and error.