Giorgio Tomasi

icoshift: A versatile tool for the rapid alignment of 1D NMR spectra

The increasing scientific and industrial interest towards metabonomics takes advantage from the high qualitative and quantitative information level of nuclear magnetic resonance (NMR) spectroscopy. However, several chemical and physical factors can affect the absolute and the relative position of an NMR signal and it is not always possible or desirable to eliminate these effects a priori. To remove misalignment of NMR signals a posteriori, several algorithms have been proposed in the literature. The icoshift program presented here is an open source and highly efficient program designed for solving signal alignment problems in metabonomic NMR data analysis. The icoshift algorithm is based on correlation shifting of spectral intervals and employs an FFT engine that aligns all spectra simultaneously. The algorithm is demonstrated to be faster than similar methods found in the literature making full-resolution alignment of large datasets feasible and thus avoiding down-sampling steps such as binning. The algorithm uses missing values as a filling alternative in order to avoid spectral artifacts at the segment boundaries. The algorithm is made open source and the Matlab code including documentation can be downloaded from www.models.life.ku.dk.

A comparison of algorithms for fitting the PARAFAC model

A multitude of algorithms have been developed to fit a trilinear PARAFAC model to a three-way array. Limits and advantages of some of the available methods (i.e. GRAM-DTLD, PARAFAC-ALS, ASD, SWATLD, PMF3 and dGN) are compared. The algorithms are explained in general terms together with two approaches to accelerate them: line search and compression. In order to compare the different methods, 720 sets of artificial data were generated with varying level and type of noise, collinearity of the factors and rank. Two PARAFAC models were fitted on each data set: the first having the correct number of factors F and the second with F+1 components (the objective being to assess the sensitivity of the different approaches to the over-factoring problem, i.e. when the number of extracted components exceeds the rank of the array). The algorithms have also been tested on two real data sets of fluorescence measurements, again by extracting both the right and an exceeding number of factors. The evaluations are based on: number of iterations necessary to reach convergence, time consumption, quality of the solution and amount of resources required for the calculations (primarily memory).

icoshift: An effective tool for the alignment of chromatographic data.

The Interval Correlation Optimised Shifting algorithm (icoshift) has recently been introduced for the alignment of nuclear magnetic resonance spectra. The method is based on an insertion/deletion model to shift intervals of spectra/chromatograms and relies on an efficient Fast Fourier Transform based computation core that allows the alignment of large data sets in a few seconds on a standard personal computer. The potential of this programme for the alignment of chromatographic data is outlined with focus on the model used for the correction function. The efficacy of the algorithm is demonstrated on a chromatographic data set with 45 chromatograms of 64,000 data points. Computation time is significantly reduced compared to the Correlation Optimised Warping (COW) algorithm, which is widely used for the alignment of chromatographic signals. Moreover, icoshift proved to perform better than COW in terms of quality of the alignment (viz. of simplicity and peak factor), but without the need for computationally expensive optimisations of the warping meta-parameters required by COW. Principal component analysis (PCA) is used to show how a significant reduction on data complexity was achieved, improving the ability to highlight chemical differences amongst the samples.

A novel approach for characterization of polycyclic aromatic hydrocarbon (PAH) pollution patterns in sediments from Guanabara Bay, Rio de Janeiro, Brazil.

A novel multivariate method based on principal component analysis of pre-processed sections of chromatograms is used to characterize the complex PAH pollution patterns in sediments from Guanabara Bay, Brazil. Five distinct sources of 3- to 6-ring PAHs could be revealed. The harbour is the most contaminated site in the bay, its plume stretches in a South West to North East direction and the chemical profile indicates mainly pyrogenic sources mixed with a fraction of high-molecular-weight petrogenic PAHs. Rio São João de Meriti is the second largest source of PAHs, and introduces mainly a fraction of low-molecular-weight petrogenic PAHs from the western region of Rio de Janeiro. The sites close to the ruptured pipeline at the Duque de Caxias Refinery show a distinctive pollution pattern indicating a heavy petroleum fraction. The method also led to the identification of new potential indicator ratios also involving coeluting peaks (e.g., triphenylene and chrysene).

Chemometric analysis of gas chromatography with flame ionisation detection chromatograms: A novel method for classification of petroleum products

Most oil characterisation procedures are time consuming, labour intensive and utilise only part of the acquired chemical information. Oil spill fingerprinting with multivariate data processing represents a fast and objective evaluation procedure, where the entire chromatographic profile is used. Methods for oil classification should be robust towards changes imposed on the spill fingerprint by short-term weathering, i.e. dissolution and evaporation processes in the hours following a spill. We propose a methodology for the classification of petroleum products. The method consists of: chemical analysis; data clean-up by baseline removal, retention time alignment and normalisation; recognition of oil type by classification followed by initial source characterisation. A classification model based on principal components and quadratic discrimination robust towards the effect of short-term weathering was established. The method was tested successfully on real spill and source samples.

The use of environmental metabolomics to determine glyphosate level of exposure in rapeseed (Brassica napus L.) seedlings.

Metabolic profiling in plants can be used to differentiate between treatments and to search for biomarkers for exposure. A methodology for processing Ultra-High-Performance Liquid Chromatography-Diode-Array-Detection data is devised. This methodology includes a scheme for selecting informative wavelengths, baseline removal, retention time alignment, selection of relevant retention times, and principal component analysis (PCA). Plant crude extracts from rapeseed seedling exposed to sublethal concentrations of glyphosate are used as a study case. Through this approach, plants exposed to concentrations down to 5 μM could be distinguished from the controls. The compounds responsible for this differentiation were partially identified and were different from those specific for high exposure samples, which suggests that two different responses to glyphosate are elicited in rapeseed depending on the level of exposure. The PCA loadings indicate that a combination of other metabolites could be more sensitive than the response of shikimate to detect glyphosate exposure.

Multilinear Models: Iterative Methods

Abstract In this section, multilinear models for multi-way arrays requiring iterative fitting algorithms are outlined. Among them: the PARAFAC (PARAllel FACtor analysis) model and one of its variants (the PARAFAC2 model); Tucker models in which one or more modes are reduced (viz., the N-way Tucker-N and Tucker-m models); hybrid models having intermediate properties between PARAFAC and Tucker ones; and coupled matrix and tensor decompositions (CMTF) which simultaneously decomposes multiple tensors. Five examples are included as to illustrate some practical aspects concerning the use of these models on analytical data.

A Tucker model based approach for analysis of complex oil biodegradation data.

A novel method based on gas chromatography-mass spectrometry in selected ion monitoring mode (GC-MS/SIM) and Tucker models is developed to evaluate the effects of oil type, microbial treatments and incubation time on the biodegradation of petroleum hydrocarbons. The data set consists of sections of the m/z 180, 192 and 198 GC-MS/SIM chromatograms of oil extracts from a biodegradation experiment where four oil types were exposed to four microbial treatments over a period of one year. The chosen sections, which are specific to methylfluorenes, phenanthrenes and dibenzothiophenes, were combined in a 4-way array (incubation timexoil typextreatmentxcombined chromatographic retention times) that was analyzed using both principal component analysis and the Tucker model. Several conclusions could be reached: the light fuel oil was the least degradable of those tested, 2- and 3-methyl isomers were more easily degraded compared to the 4-methyl isomers, the mixture of surfactant producers and PAC degraders provided the most effective degradation and the largest part of the degradation occurred between 54 and 132 days.

Source apportionment of polycyclic aromatic hydrocarbons (PAHs) in sediments from Khuzestan province, Iran

Abstract Khuzestan, Iran is heavily industrialised with petrochemical and refinery companies. Herein, sediment and soil samples were collected from Hendijan coast, Khore Mosa and Arvandroud River. The CHEMSIC (CHEmometric analysis of Selected Ion Chromatograms) method was used to assign the main sources of polycyclic aromatic hydrocarbon (PAH) pollution. A four-component principal component analysis (PCA) model was obtained. While principal component 1 (PC1) was related to the total concentration of PAHs, the remaining PCs described three distinct sources: PC2 and PC3 collectively differentiate between weathered petrogenic and pyrogenic, and PC4 is indicative for a diagenetic input. The sources of PAHs in the Arvandroud River were mainly relatively fresh oil with some samples corresponding to a weathered oil input. Further, perylene (indicator for diagenetic source) was identified. Samples from Khore Mosa revealed a mixture with high proportions of high-molecular-weight PAHs, indicating a pyrogenic/weathered petrogenic source. Samples from Hendijan coast contained low relative concentrations of PAHs, thus only little information on pollution sources.

An untargeted gas chromatography mass spectrometry metabolomics platform for marine polychaetes

The development of an appropriate extraction method for untargeted environmental metabolomic analysis of marine polychaetes could promote their use for environmental monitoring purposes. To this end, we compared four extraction methods on the marine polychaete Nereis virens both exposed to crude oil and non-exposed. XCMS was used for feature detection and preprocessing; different normalization and scaling approaches were tested; and principal component analysis (PCA) was used together with basic statistical tests to ascertain common metabolic patterns and determine the most suitable extraction method. We conclude that a two-step extraction procedure with 80:20 (v/v) methanol:water on freeze dried polychaete tissue provides the best trade-off between analysis time, and extraction efficiency and intermediate reproducibility. No definitive conclusions could be drawn about the ability of the method to discriminate controls and crude oils in actual biological replicates because the experiment was carried out by design on analytical replicates only. We show that the normalization to the sum of all the common features, and the use of a weighted least squares criterion to fit the PCA by means of scaling to the median absolute deviation (MAD) of the pooled quality control samples significantly improved the clustering of controls and crude oil exposed samples. The scaling alone led to an increase of 19% in explained variance compared to ordinary PCA.