Untargeted metabolite discovery in kinetic data from multi-dose intervention studies

A new strategy for biomarker discovery is presented that is based on multi-dose kinetic metabolomics data. Gas chromatography-mass spectrometry (GC-MS) data sets recorded in the full scan mode are scanned for compounds showing a meaningful trend following the different doses and sampling time points. From a biological point of view, a meaningful trend denotes a compound that responds similarly at all doses and follows a smooth trend along the time points. This type of information can be used to distinguish relevant metabolites from those compounds not following the expected trends. The method is based on analysing the time and dosage trends of each compound via principal component analysis. As only local information is analysed at a time (meaning no correlation with other metabolites is taken into account), the proposed model flags relevant metabolites even if their trend is different from that of any other compound. The new method is therefore an attractive way to reduce the long list of detected compounds in a metabolomics sample set to include only those having the expected smooth time profile that is common for all doses. The new strategy is tested on a sample set obtained from a gut fermentation study of a polyphenol-rich diet. For this study, the initial list of over 25,000 potentially interesting features was reduced to less than 250, thus significantly reducing the expensive and time-consuming manual examination.     

Comprehensive two-dimensional gas chromatography for fingerprint pattern recognition in olive oils produced by two different techniques in Portuguese olive varieties Galega Vulgar, Cobrançosa e Carrasquenha

For olive oil production a metal hammer-decanter olive processing line was compared to a traditional metal hammer-press line, a discontinuous method which, if properly used, yields high-quality virgin olive oils. Galega, Carrasquenha and Cobrançosa olives (traditional Portuguese varieties) were studied. The analysis of the aroma compounds was performed after headspace-solid phase micro extraction. The analytical results obtained after comprehensive gas chromatography in tandem with time of flight mass spectrometry (GC × GC/ToFMS) for these three different olive oil varieties, from a single year harvest and processed with two different extraction technologies, were compared using statistical image treatment, by means of ImageJ software, for fingerprint recognitions and compared with principal component analysis when the area data of each chromatographic spot of the contour plots were considered. The differences used to classify the olive oils studied under different groups after principal component analysis were observed independently of the treatment used (peak areas or the sum of the pixels counts). When the individual peak areas were considered, more then 75.7% of the total variance is explained by the first two principal components while in the case where the data were subjected to image treatment 84.0% of the total variance is explained by the first two principal components. In both cases the first and second principal components present eigenvalues higher then 1.0. Fingerprint image monitoring of the aroma compounds of the olive oil allowed a rapid differentiation of the three varieties studied as well as the extraction methods used. The volatile compounds responsible for their characterization were tentatively identified in a bi-dimensional polar/non-polar column set in the GC × GC/Tof-MS apparatus. This methodology allowed the reduction of the number of compounds needed for matrices characterization, preserving the efficiency of the discrimination, when compared with the traditional methods where the identification of all peaks is needed.     

Classificatin of chinese tea samples according to origin and quality by principal component techniques

Three categories of tea, black, green and oolong tea, with those varieties in each category were analyzed for their contents of cellulose, hemicellulose, lignin, polyphenols, caffeine and amino acids. The data were subjected to multivarige analysis. Principal component analysis and principal component classification provide discrimination between the different categories and varieties. The quality index, assigned to these tea standards by experts, could be predicted form the principal component scores.     

In vivo determination of the volatile metabolites of saprotroph fungi by comprehensive two‐dimensional gas chromatography

In this work, we discuss the use of multiway principal component analysis combined with comprehensive two‐dimensional gas chromatography to study the volatile metabolites of the saprophytic fungus Memnoniella sp. isolated in vivo by headspace solid‐phase microextraction. This fungus has been identified as having the ability to induce plant resistance against pathogens, possibly through its volatile metabolites. Adequate culture media were inoculated, and its headspace was then sampled with a solid‐phase microextraction fiber and chromatographed every 24 h over seven days. The raw chromatogram processing using multiway principal component analysis allowed the determination of the inoculation period, during which the concentration of volatile metabolites was maximized, as well as the discrimination of the appropriate peaks from the complex culture media background. Several volatile metabolites not previously described in the literature on biocontrol fungi were observed, as well as sesquiterpenes and aliphatic alcohols. These results stress that, due to the complexity of multidimensional chromatographic data, multivariate tools might be mandatory even for apparently trivial tasks, such as the determination of the temporal profile of metabolite production and extinction. However, when compared with conventional gas chromatography, the complex data processing yields a considerable improvement in the information obtained from the samples.     

The use of high performance liquid chromatography-quadrupole time-of-flight mass spectrometry coupled to advanced data mining and chemometric tools for discrimination and classification of red wines according to their variety

In this study, the potential of high performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (HPLC–QTOFMS) for metabolomic profiling of red wine samples was examined. Fifty one wines representing three varieties (Cabernet Sauvignon, Merlot, and Pinot Noir) of various geographical origins were sourced from the European and US retail market. To find compounds detected in analyzed samples, an automated compound (feature) extraction algorithm was employed for processing background subtracted single MS data. Stepwise reduction of the data dimensionality was followed by principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA) which were employed to explore the structure of the data and construct classification models. The validated PLS-DA model based on data recorded in positive ionization mode enabled correct classification of 96% of samples. Determination of molecular formula and tentative identification of marker compound was carried out using accurate mass measurement of full single MS spectra. Additional information was obtained by correlating the fragments obtained by MS/MS accurate mass spectra using the QTOF with collision induced dissociation (CID) of precursor ions.     

Usefulness of chemometrics and mass spectrometry-based electronic nose to classify Australian white wines by their varietal origin

A combination of mass spectrometry-based electronic nose (MS e_nose) and chemometrics was explored to classify two Australian white wines according to their varietal origin namely Riesling and unwooded Chardonnay. The MS e_nose data were analysed using principal components analysis (PCA), discriminant partial least squares (DPLS) and linear discriminant analysis (LDA) applied to principal components scores and validated using full cross validation (leave one out). DPLS gave the highest levels of correct classification for both varieties (>90%). LDA classified correctly 73% of unwooded Chardonnay and 82% of Riesling wines. Even though the conventional analysis provides fundamental information about the volatile compounds present in the wine, the MS e_nose method has a series of advantages over conventional analytical techniques due to simplicity of the sample-preparation and reduced time of analysis and might be considered as a more convenient choice for routine process control in an industrial environment. The work reported here is a feasibility study and requires further development with considerably more commercial samples of different varieties. Further studies are needed in order to improve the calibration specificity, accuracy and robustness, and to extend the discrimination to other wine varieties or blends.     

Insight into molecular characteristics of a Chinese coal via separation,characterization, and data processing

Dayan lignite was subjected to thermal dissolution sequentially with cyclohexane, acetone, and methanol. Each thermal dissolution extract was subjected to further separation/enrichment using column chromatography, which was sequentially eluted with petroleum ether, a mixture of ethyl acetate and petroleum ether (vol:vol = 1:1), and ethyl acetate. The three thermal dissolution extracts and nine enrichment subfractions were characterized by an Orbitrap mass spectrometry equipped with an atmospheric pressure chemical ionization ion source. The mass spectrometry data were also statistically analyzed by principal component analysis, which can reduce the dimensionality of data and classify multiple samples according to principal components. Identified compounds in the extracts and subfractions are classified into eight classes according to the heteroatom distribution. Hydrocarbon class is mainly presented in the petroleum ether fraction, and oxygen class, nitrogen class, and oxygen‐nitrogen class are distributed in both petroleum ether/ethyl acetate and ethyl acetate subfractions. The combination of different analytical methods enhances the understanding of coal at the molecular level and provides important data for downstream refining processes.     

A non-target geographical origin screening of botrytized wines through comprehensive two-dimensional gas chromatography coupled with high-resolution mass spectrometry

One of the most effective methods for gaining insight into the composition of trace-level volatile organic characteristics of wine products is through the use of a comprehensive two-dimensional gas chromatography-high resolution mass spectrometry (GC × GC-HRMS) technique. The vast amount of data generated by this method, however, can often be overwhelming requiring exhaustive and time-consuming analysis to identify significant statistical characteristics. The use of advanced chemometric software can achieve the same or even higher efficiency. This study aimed to identify differences based on geographical locations by analyzing the volatile organic compounds in the composition of botrytized wines from Slovakia, Hungary, France, and Austria. The volatile organic compounds were extracted by solid-phase microextraction and analyzed using GC × GC-HRMS. The data obtained from the analysis underwent Fisher-ratio (F-ratio) tile-based analysis to identify statistically significant differences. Principal component analysis demonstrated a significant distinction between wine samples based on geographical location, using only 10 statistically significant features with the highest F-ratio. In the samples, the following compounds were analyzed: methyl-octadecanoate, 2-cyanophenyl-β-phenylpropionate, α-ionone, n-octanoic acid, 1,2-dihydro-1,1,6-trimethyl-naphthalene, methyl-hexadecanoate, ethyl-pentadecanoate, ethyl-decanoate, and γ-nonalactone. These, all play an important role in cluster pattern observed on principal component analysis results. Additionally, hierarchical cluster analysis confirmed this.     

Principal component directed partial least squares analysis for combining nuclear magnetic resonance and mass spectrometry data in metabolomics: application to the detection of breast cancer

Nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) are the two most commonly used analytical tools in metabolomics, and their complementary nature makes the combination particularly attractive. A combined analytical approach can improve the potential for providing reliable methods to detect metabolic profile alterations in biofluids or tissues caused by disease, toxicity, etc. In this paper, (1)H NMR spectroscopy and direct analysis in real time (DART)-MS were used for the metabolomics analysis of serum samples from breast cancer patients and healthy controls. Principal component analysis (PCA) of the NMR data showed that the first principal component (PC1) scores could be used to separate cancer from normal samples. However, no such obvious clustering could be observed in the PCA score plot of DART-MS data, even though DART-MS can provide a rich and informative metabolic profile. Using a modified multivariate statistical approach, the DART-MS data were then reevaluated by orthogonal signal correction (OSC) pretreated partial least squares (PLS), in which the Y matrix in the regression was set to the PC1 score values from the NMR data analysis. This approach, and a similar one using the first latent variable from PLS-DA of the NMR data resulted in a significant improvement of the separation between the disease samples and normals, and a metabolic profile related to breast cancer could be extracted from DART-MS. The new approach allows the disease classification to be expressed on a continuum as opposed to a binary scale and thus better represents the disease and healthy classifications. An improved metabolic profile obtained by combining MS and NMR by this approach may be useful to achieve more accurate disease detection and gain more insight regarding disease mechanisms and biology.     

Comparison of selected aroma compounds in cultivars of sea buckthorn (<Emphasis Type="Italic">Hippophae rhamnoides</Emphasis> L.)

Thirteen cultivars of sea buckthorn (Hippophae rhamnoides L.) berries: Aromat, Botanicky, Buchlovicky, Hergo, Krasavica, Leicora, Ljubitelna, Pavlovsky, Peterbursky, Sluni?ko, Trofinovsky, Vitaminnaja and Velkoosecky, were tested for the content of volatile aroma compounds using gas chromatography with the solid phase microextraction method during two consequent years (2012–2013). In total, 69 volatile compounds were identified: 26 alcohols, 12 aldehydes, 11 ketones, 9 acids and 11 esters. Based on principal component analysis, 18 most relevant compounds, best representing the variability of the whole system and suitable for the discrimination of the samples, were selected from all compounds identified. These compounds were then compared using the analysis of variance to confirm differences between the samples. Significant (p < 0.05) differences were found in the varieties in both years, Krasavica and Sluni?ko cultivars were found to be quite different from other varieties, being rich in the compounds identified and containing most of the selected compounds. Variability within the cultivars (between picking years) was low or not significant.     

Application of a linear ion trap/orbitrap mass spectrometer in metabolite characterization studies: Examination of the human liver microsomal metabolism of the non-tricyclic anti-depressant nefazodone using data-dependent accurate mass measurements

We report herein, facile metabolite identification workflow on the anti-depressant nefazodone, which is derived from accurate mass measurements based on a single run/experimental analysis. A hybrid LTQ/orbitrap mass spectrometer was used to obtain accurate mass full scan MS and MS/MS in a data-dependent fashion to eliminate the reliance on a parent mass list. Initial screening utilized a high mass tolerance ( approximately 10 ppm) to filter the full scan MS data for previously reported nefazodone metabolites. The tight mass tolerance reduces or eliminates background chemical noise, dramatically increasing sensitivity for confirming or eliminating the presence of metabolites as well as isobaric forms. The full scan accurate mass analysis of suspected metabolites can be confirmed or refuted using three primary tools: (1) predictive chemical formula and corresponding mass error analysis, (2) rings-plus-double bonds, and (3) accurate mass product ion spectra of parent and suspected metabolites. Accurate mass characterization of the parent ion structure provided the basis for assessing structural assignment for metabolites. Metabolites were also characterized using parent product ion m/z values to filter all tandem mass spectra for identification of precursor ions yielding similar product ions. Identified metabolite parent masses were subjected to chemical formula calculator based on accurate mass as well as bond saturation. Further analysis of potential nefazodone metabolites was executed using accurate mass product ion spectra. Reported mass measurement errors for all full scan MS and MS/MS spectra was <3 ppm, regardless of relative ion abundance, which enabled the use of predictive software in determining product ion structure. The ability to conduct biotransformation profiling via tandem mass spectrometry coupled with accurate mass measurements, all in a single experimental run, is clearly one of the most attractive features of this methodology.     

New opportunities in multiplexed optical bioanalyses using quantum dots and donor–acceptor interactions

In this study, we investigated the feasibility of using a novel volatile organic compound (VOC)-based metabolic profiling approach with a newly devised chemometrics methodology which combined rapid multivariate analysis on total ion currents with in-depth peak deconvolution on selected regions to characterise the spoilage progress of pork. We also tested if such approach possessed enough discriminatory information to differentiate natural spoiled pork from pork contaminated with Salmonella typhimurium, a food poisoning pathogen commonly recovered from pork products. Spoilage was monitored in this study over a 72-h period at 0-, 24-, 48- and 72-h time points after the artificial contamination with the salmonellae. At each time point, the VOCs from six individual pork chops were collected for spoiled vs. contaminated meat. Analysis of the VOCs was performed by gas chromatography/mass spectrometry (GC/MS). The data generated by GC/MS analysis were initially subjected to multivariate analysis using principal component analysis (PCA) and multi-block PCA. The loading plots were then used to identify regions in the chromatograms which appeared important to the separation shown in the PCA/multi-block PCA scores plot. Peak deconvolution was then performed only on those regions using a modified hierarchical multivariate curve resolution procedure for curve resolution to generate a concentration profiles matrix C and the corresponding pure spectra matrix S. Following this, the pure mass spectra (S) of the peaks in those region were exported to NIST 02 mass library for chemical identification. A clear separation between the two types of samples was observed from the PCA models, and after deconvolution and univariate analysis using N-way ANOVA, a total of 16 significant metabolites were identified which showed difference between natural spoiled pork and those contaminated with S. typhimurium.     

Multivariate curve resolution applied to in situ X-ray absorption spectroscopy data: An efficient tool for data processing and analysis

Large datasets containing many spectra commonly associated with in situ or operando experiments call for new data treatment strategies as conventional scan by scan data analysis methods have become a time-consuming bottleneck. Several convenient automated data processing procedures like least square fitting of reference spectra exist but are based on assumptions. Here we present the application of multivariate curve resolution (MCR) as a blind-source separation method to efficiently process a large data set of an in situ X-ray absorption spectroscopy experiment where the sample undergoes a periodic concentration perturbation. MCR was applied to data from a reversible reduction–oxidation reaction of a rhenium promoted cobalt Fischer–Tropsch synthesis catalyst. The MCR algorithm was capable of extracting in a highly automated manner the component spectra with a different kinetic evolution together with their respective concentration profiles without the use of reference spectra. The modulative nature of our experiments allows for averaging of a number of identical periods and hence an increase in the signal to noise ratio (S/N) which is efficiently exploited by MCR. The practical and added value of the approach in extracting information from large and complex datasets, typical for in situ and operando studies, is highlighted.     

Brand-dependent volatile fingerprinting of Italian wines from Valpolicella

Head-space solid-phase microextraction (HS-SPME) coupled to gas-chromatography-mass spectrometry was developed and applied to obtain the volatile aromatic fingerprints of three typical Italian wines, Valpolicella, Amarone and Recioto, all produced in the restricted geographical area of Valpolicella (Veneto, Italy) with the same grape cultivars within the regulations of a rigid disciplinary of production. Differences between the three typologies are mainly linked to the different withering times to which grapes are subjected before vinification, which strongly influences the concentration and the development of volatile aroma compounds. A total of 22 different wines (7 Valpolicella, 10 Amarone and 5 Recioto) were characterised in terms of aromatic volatile profile with the aim to distinguish the different products and to evaluate the possibility to differentiate the same product from different brands. For the chemometric evaluation of the data one-way analysis of variance (ANOVA), principal component analysis (PCA) and hierarchical cluster analysis (HCA) were tested. All the chemometric tools employed allow to differentiate between the three products. More intriguing is the ability of the chemometric approach to differentiate between the same product (Amarone, Recioto) from different winery, thus showing the potential of this approach to characterize the brand-dependent typicality of wines, which is usually related to subtle technological differences which nevertheless have strong influences on the organoleptic characteristics of the products.     

Gas chromatography–mass spectroscopy optimization by computer simulation,application to the analysis of 93 volatile organic compounds in workplace ambient air

GC–MS optimization method including both advantages from chromatographic separation and mass spectrometric detection was designed for a set of 93 volatile organic compounds. Only a few experiments were necessary to determine the thermodynamic retention parameters for all compounds on a RTX-VMS column. From these data, computer simulation was used in order to predict the retention times of the compounds in temperature programmed gas chromatography. Then, an automatic selection of ions from the NIST database was performed and compared to the optimum conditions (full separation of VOC). This simulation-selection procedure was used to screen a numerous set of GC and MS conditions in order to quickly design a GC–MS method whatever the set of compounds considered.     

Optimisation of a new headspace mass spectrometry instrument. Discrimination of different geographical origin olive oils

A fast head-space analysis instrument, constituted by an automatic sample introduction system directly coupled to a mass detector without performing any chromatographic separation, was assembled. A suitable and original response was computed to optimise, by experimental design, the measured signals for discrimination purposes. The volatile fractions of 105 extra virgin olive oils coming from five different Mediterranean areas were analysed. The rough information collected by this system was unravelled and explained by well-known chemometrical techniques of display (principal component analysis), feature selection (stepwise linear discriminant analysis) and classification (linear discriminant analysis). The 93.4% of samples resulted to be correctly classified and the 90.5% correctly predicted by cross-validation procedure, whilst the 80.0% of an external test set, created to full validate the classification rule, were correctly assigned.     

Multivariate approaches for efficient detection of potential metabolites from liquid chromatography/mass spectrometry data

This work describes a novel method for rapid screening of unknown metabolites in urine samples that narrows down the list of potential metabolites. Prior to analysis by liquid chromatography/electrospray ionization mass spectrometry (LC/ESI-MS), urine samples were prepared using solid-phase extraction (SPE). Automatic curve resolution was used for deconvolution of the LC/MS data, followed by peak alignment. Preprocessed data were then used for metabolite pattern recognition using principal component analysis (PCA), parallel factor analysis (PARAFAC), and multilinear partial least squares (N-PLS). This approach enabled the rapid detection of metabolites of citalopram in urine by maximizing the information extracted. The metabolites thus identified were compared with earlier studies on the metabolism of citalopram. In addition, new, unreported metabolites were found and characterized by LC/MS/MS and accurate mass measurements. A combination of data from positive and negative ionization enhanced the identification of metabolites.     

On-line coupling of electrokinetic chromatography and mass spectrometry

The use of pseudostationary phases (PSPs) in capillary electrophoresis provides powerful separation systems of high efficiency, selectivity and flexibility. Such electrokinetic chromatographic (EKC) systems are particularly useful for chiral analysis or for the analysis of samples containing a broad range of compounds. As the availability of mass and/or structural data on (unknown) sample constituents is increasingly important, the on-line coupling of EKC and mass spectrometry (MS) has gained attention. However, commonly used PSPs, such as micelles and cyclodextrines, may strongly interfere with electrospray ionization (ESI), making on-line EKC–MS quite a challenging task. This review covers the various approaches that have been proposed and developed to combine EKC and MS. A distinction is made between methodologies that prevent the PSP from entering the MS system, and methodologies that allow introduction of PSPs into the ion source. Various approaches such as partial filling of the separation capillary with PSP, use of reverse-migrating PSPs, employment of volatile PSPs, and alternative ionization modes, are outlined. Specific applications are described and overview tables are provided. It is concluded that there is no general solution for EKC–MS available yet, but new ionization techniques like atmospheric pressure photoionization may offer attractive perspectives for achieving full compatibility.     

Tea Classification by Near Infrared Spectroscopy with Projection Discriminant Analysis and Gene Expression Programming

Traditional gene expression programming for classification is designed for binary decisions. Herein, projection discriminant analysis for direct multiclass categorization using gene expression programming is described. Gene expression programming was first employed to examine new synthetic variables that were built as nonlinear combinations of the original features. The data were projected on planes spanned by these new synthetic variables and the nearest centroid was employed to classify new samples. A new objective function was formulated to determine optimum synthetic variables. Direct multiclass categorization using a gene expression programming algorithm was used to classify six tea varieties analyzed by near infrared spectroscopy. Compared with traditional gene expression programming, principal component analysis, and linear discriminant analysis, direct multiclass categorization with gene expression programming algorithm was more efficient. Visual inspection of high dimensional data by this approach also facilitated classification and comprehension of data.