Maria A. van Agthoven

Near-Infrared Compositional Analysis of Gas and Condensate Reservoir Fluids at Elevated Pressures and Temperatures

The near-infrared spectroscopic (NIR) analysis of several fluid mixtures approximating natural gases or condensates is reported. Spectra were measured under wide variations of pressure and temperature in accord with conditions found in various gas or condensate reservoirs. Some restrictions simulating currently feasible hardware specifications were placed on spectral data before they were used for analysis. We employed principal components regression (PCR) on inverted Beers Law for compositional analysis. The result shows that it is feasible to conduct an in situ compositional analysis in the reservoir environment. In fact, this algorithm is currently being utilized successfully with an optical spectrometer operating down-hole in oil wells.

Near-Infrared Spectral Analysis of Gas Mixtures

The analysis by near-infrared spectroscopy (NIR) of a series of gas mixtures approximating natural gases is reported. Wide variations of gas pressure and temperature are used in accord with conditions found in various utilitarian gas flow streams. The NIR analysis of CH4 and CO2 composition is found to be straightforward and depends only on compound mass density, but not explicitly on temperature, pressure, or composition. Linearity of the spectra of more complex mixtures is maintained, but the NIR analysis is more complex. Principal component analysis is shown to resolve composition for those gas mixtures.

Efficient denoising algorithms for large experimental datasets and their applications in Fourier transform ion cyclotron resonance mass spectrometry

Significance Every measurement is corrupted due to random fluctuations in the sample and the apparatus. Current efficient denoising algorithms require large matrix analysis, and become untractable even for moderately large datasets. Any series can be considered as an operator that modifies any input vector. By applying this operator on a series of random vectors and thus reducing the dimension of the data, it is possible, using simple algebra, to reduce noise in a robust manner. Furthermore, the structure of the underlying matrices allows a very fast and memory-efficient implementation. Counterintuitively, randomness is used here to reduce noise. This procedure, called urQRd (uncoiled random QR denoising), allows denoising to be applied to data of virtually unlimited size. Modern scientific research produces datasets of increasing size and complexity that require dedicated numerical methods to be processed. In many cases, the analysis of spectroscopic data involves the denoising of raw data before any further processing. Current efficient denoising algorithms require the singular value decomposition of a matrix with a size that scales up as the square of the data length, preventing their use on very large datasets. Taking advantage of recent progress on random projection and probabilistic algorithms, we developed a simple and efficient method for the denoising of very large datasets. Based on the QR decomposition of a matrix randomly sampled from the data, this approach allows a gain of nearly three orders of magnitude in processing time compared with classical singular value decomposition denoising. This procedure, called urQRd (uncoiled random QR denoising), strongly reduces the computer memory footprint and allows the denoising algorithm to be applied to virtually unlimited data size. The efficiency of these numerical tools is demonstrated on experimental data from high-resolution broadband Fourier transform ion cyclotron resonance mass spectrometry, which has applications in proteomics and metabolomics. We show that robust denoising is achieved in 2D spectra whose interpretation is severely impaired by scintillation noise. These denoising procedures can be adapted to many other data analysis domains where the size and/or the processing time are crucial.

Towards analytically useful two-dimensional Fourier transform ion cyclotron resonance mass spectrometry

Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometry (MS) achieves high resolution and mass accuracy, allowing the identification of the raw chemical formulae of ions in complex samples. Using ion isolation and fragmentation (MS/MS), we can obtain more structural information, but MS/MS is time- and sample-consuming because each ion must be isolated before fragmentation. In 1987, Pfändler et al. proposed an experiment for 2D FT-ICR MS in order to fragment ions without isolating them and to visualize the fragmentations of complex samples in a single 2D mass spectrum, like 2D NMR spectroscopy. Because of limitations of electronics and computers, few studies have been conducted with this technique. The improvement of modern computers and the use of digital electronics for FT-ICR hardware now make it possible to acquire 2D mass spectra over a broad mass range. The original experiments used in-cell collision-induced dissociation, which caused a loss of resolution. Gas-free fragmentation modes such as infrared multiphoton dissociation and electron capture dissociation allow one to measure high-resolution 2D mass spectra. Consequently, there is renewed interest to develop 2D FT-ICR MS into an efficient analytical method. Improvements introduced in 2D NMR spectroscopy can also be transposed to 2D FT-ICR MS. We describe the history of 2D FT-ICR MS, introduce recent improvements, and present analytical applications to map the fragmentation of peptides. Finally, we provide a glossary which defines a few keywords for the 2D FT-ICR MS field.

Two-dimensional ECD FT-ICR mass spectrometry of peptides and glycopeptides.

2D FT-ICR MS allows the correlation between precursor and fragment ions by modulating ion cyclotron radii for fragmentation modes with radius-dependent efficiency in the ICR cell without the need for prior ion isolation. This technique has been successfully applied to ion-molecule reactions, Collision-induced dissociation and infrared multiphoton dissociation. In this study, we used electron capture dissociation for 2D FT-ICR MS for the first time, and we recorded two-dimensional mass spectra of peptides and a mixture of glycopeptides that showed fragments that are characteristic of ECD for each of the precursor ions in the sample. We compare the sequence coverage obtained with 2D ECD FT-ICR MS with the sequence coverage obtained with ECD MS/MS and compare the sensitivities of both techniques. We demonstrate how 2D ECD FT-ICR MS can be implemented to identify peptides and glycopeptides for proteomics analysis.

Differentiating Fragmentation Pathways of Cholesterol by Two-Dimensional Fourier Transform Ion Cyclotron Resonance Mass Spectrometry

AbstractTwo-dimensional Fourier transform ion cyclotron resonance mass spectrometry is a data-independent analytical method that records the fragmentation patterns of all the compounds in a sample. This study shows the implementation of atmospheric pressure photoionization with two-dimensional (2D) Fourier transform ion cyclotron resonance mass spectrometry. In the resulting 2D mass spectrum, the fragmentation patterns of the radical and protonated species from cholesterol are differentiated. This study shows the use of fragment ion lines, precursor ion lines, and neutral loss lines in the 2D mass spectrum to determine fragmentation mechanisms of known compounds and to gain information on unknown ion species in the spectrum. In concert with high resolution mass spectrometry, 2D Fourier transform ion cyclotron resonance mass spectrometry can be a useful tool for the structural analysis of small molecules. Graphical Abstractᅟ

Two-dimensional Fourier transform ion cyclotron resonance mass spectrometry: reduction of scintillation noise using Cadzow data processing

In two-dimensional Fourier transform ion cyclotron resonance mass spectrometry (2D FTICR-MS), scintillation noise, caused mostly by fluctuations in the number of ions in the ICR cell, is the leading cause for errors in spectrum interpretation. In this study, we adapted an algorithm based on singular value decomposition and first introduced by Cadzow et al. (IEE Proceedings Pt. F 1987, 134, 69) to 2D FTICR-MS and we measured its performance in terms of noise reduction without losing signal information in the 2D mass spectrum.

2D FT-ICR MS of Calmodulin: A Top-Down and Bottom-Up Approach

AbstractTwo-dimensional Fourier transform ion cyclotron resonance mass spectrometry (2D FT-ICR MS) allows data-independent fragmentation of all ions in a sample and correlation of fragment ions to their precursors through the modulation of precursor ion cyclotron radii prior to fragmentation. Previous results show that implementation of 2D FT-ICR MS with infrared multi-photon dissociation (IRMPD) and electron capture dissociation (ECD) has turned this method into a useful analytical tool. In this work, IRMPD tandem mass spectrometry of calmodulin (CaM) has been performed both in one-dimensional and two-dimensional FT-ICR MS using a top-down and bottom-up approach. 2D IRMPD FT-ICR MS is used to achieve extensive inter-residue bond cleavage and assignment for CaM, using its unique features for fragment identification in a less time- and sample-consuming experiment than doing the same thing using sequential MS/MS experiments. Graphical Abstractᅟ

Two-Dimensional Mass Spectrometry for Proteomics, a Comparative Study with Cytochrome c

Two-dimensional Fourier transform ion cyclotron resonance mass spectrometry (2D FT-ICR MS) allows the correlation between precursor and fragment ions in tandem mass spectrometry without the need to isolate the precursor ion beforehand. 2D FT-ICR MS has been optimized as a data-independent method for the structural analysis of compounds in complex samples. Data processing methods and denoising algorithms have been developed to use it as an analytical tool. In the present study, the capabilities of 2D FT-ICR MS are explored with a tryptic digest of cytochrome c with both ECD and IRMPD as fragmentation modes. The 2D mass spectra showed useful fragmentation patterns of peptides over a dynamic range of almost 400. By using a quadratic calibration, fragment ion peaks could be successfully assigned. The correlation between precursor and fragment ions in the 2D mass spectra was more accurate than in MS/MS spectra after quadrupole isolation, due to the limitations of quadrupole isolation. The use of the second dimension allowed for successful fragment assignment from precursors that were separated by only m/z 0.0156. The resulting cleavage coverage of cytochrome c almost matched data provided by high-resolution FT-ICR MS/MS analysis, but the 2D FT-ICR MS method required only one experimental scan.

Bottom-Up Two-Dimensional Electron-Capture Dissociation Mass Spectrometry of Calmodulin

AbstractTwo-dimensional mass spectrometry (2D MS) is a tandem mass spectrometry technique that allows data-independent fragmentation of all precursors in a mixture without previous isolation, through modulation of the ion cyclotron frequency in the ICR-cell prior to fragmentation. Its power as an analytical technique has been proven particularly for proteomics. Recently, a comparison study between 1D and 2D MS has been performed using infrared multiphoton dissociation (IRMPD) on calmodulin (CaM), highlighting the capabilities of the technique in both top-down (TDP) and bottom-up proteomics (BUP). The goal of this work is to expand this study on CaM using electron-capture dissociation (ECD) 2D MS as a single complementary BUP experiment in order to enhance the cleavage coverage of the protein under analysis. By adding the results of the BUP 2D ECD MS to the 2D IRMPD MS analysis of CaM, the total cleavage coverage increased from ~40% to ~68%. Graphical abstractᅟ