Identification of sources of diesel oil spills using parallel factor analysis: A bridge between American society for testing and materials and Nordtest methods

Gas chromatography with parallel hard and soft ionization mass spectrometry

RATIONALE

Mass spectrometric identification of compounds in chromatography can be obtained from molecular masses from soft ionization mass spectrometry techniques such as field ionization (FI) and fragmentation patterns from hard ionization techniques such as electron ionization (EI). Simultaneous detection by EI and FI mass spectrometry allows alignment of the different information from each method.

METHODS

We report the construction and characteristics of a combined instrument consisting of a gas chromatograph and two parallel mass spectrometry ionization sources, EI and FI. When considering both ion yield and signal-to-noise it was postulated that good-quality EI and FI mass spectra could be obtained simultaneously using a post-column splitter with a split fraction of 1:10 for EI/FI. This has been realised and we report its application for the analysis of several complex mixtures.

RESULTS

The differences between the full width at half maximum (FWHM) of the EI and FI chromatograms were statistically insignificant, and the retention times of the chromatograms were highly correlated (r(2) =0.9999) with no detectable bias. The applicability and significance of this combined instrument and the attendant methodology are illustrated by the analysis of standard samples of 13 compounds with diverse structures, and the analysis of mixtures of fatty acids, fish oil, hydrocarbons and yeast metabolites.

CONCLUSIONS

This combined dual-source instrument saves time and resources, and more importantly generates equivalent chromatograms aligned in time, in EI and FI (i.e. peaks with similar shapes and identical positions). The identical FWHMs and retention times of the EI and FI chromatograms in this combined instrument enable the accurate assignment of fragment ions from EI to their corresponding molecular ions in FI.

Plant metabolomics: resolution and quantification of elusive peaks in liquid chromatography-mass spectrometry profiles of complex plant extracts using multi-way decomposition methods

Previous studies on LC-MS metabolomic profiling of 127 F2 Barbarea vulgaris plants derived from a cross of parental glabrous (G) and pubescent (P) type, revealed four triterpenoid saponins (hederagenin cellobioside, oleanolic acid cellobioside, epihederagenin cellobioside, and gypsogenin cellobioside) that correlated with resistance of plants against the insect herbivore, Phyllotreta nemorum. In this study, for the first time, we demonstrate the efficiency of the multi-way decomposition method PARAllel FACtor analysis 2 (PARAFAC2) for exploring complex LC-MS data. PARAFAC2 enabled automated resolution and quantification of several elusive chromatographic peaks (e.g. overlapped, elution time shifted and low s/n ratio), which could not be detected and quantified by conventional chromatographic data analysis. Raw LC-MS data of 127 F2 B. vulgaris plants were arranged in a three-way array (elution time point×mass spectra×samples), divided into 17 different chromatographic intervals and each interval were individually modeled by PARAFAC2. Three main outputs of the PARAFAC2 models described: (1) elution time profile, (2) relative abundance, and (3) pure mass spectra of the resolved peaks modeled from each interval of the chromatographic data. PARAFAC2 scores corresponding to relative abundances of the resolved peaks were extracted and further used for correlation and partial least squares (PLS) analysis. A total of 71 PARAFAC2 components (which correspond to actual peaks, baselines and tails of neighboring peaks) were modeled from 17 different chromatographic retention time intervals of the LC-MS data. In addition to four previously known saponins, correlation- and PLS-analysis resolved five unknown saponin-like compounds that were significantly correlated with insect resistance. The method also enabled a good separation between resistant and susceptible F2 plants. PARAFAC2 spectral loadings corresponding to the pure mass spectra of chromatographic peaks matched well with experimentally recorded mass spectra (correlation based similarity >95%). This enabled to extract pure mass spectra of highly overlapped and low s/n ratio peaks.

Pre-processing of ultraviolet resonance Raman spectra

The application of UV excitation sources coupled with resonance Raman have the potential to offer information unavailable with the current inventory of commonly used structural techniques including X-ray, NMR and IR analysis. However, for ultraviolet resonance Raman (UVRR) spectroscopy to become a mainstream method for the determination of protein secondary structure content and monitoring protein dynamics, the application of multivariate data analysis methodologies must be made routine. Typically, the application of higher order data analysis methods requires robust pre-processing methods in order to standardize the data arrays. The application of such methods can be problematic in UVRR datasets due to spectral shifts arising from day-to-day fluctuations in the instrument response. Additionally, the non-linear increases in spectral resolution in wavenumbers (increasing spectral data points for the same spectral region) that results from increasing excitation wavelengths can make the alignment of multi-excitation datasets problematic. Last, a uniform and standardized methodology for the subtraction of the water band has also been a systematic issue for multivariate data analysis as the water band overlaps the amide I mode. Here we present a two-pronged preprocessing approach using correlation optimized warping (COW) to alleviate spectra-to-spectra and day-to-day alignment errors coupled with a method whereby the relative intensity of the water band is determined through a least-squares determination of the signal intensity between 1750 and 1900 cm(-1) to make complex multi-excitation datasets more homogeneous and usable with multivariate analysis methods.