simplified prior to detection by separating some analytes from others. separation achieves various goals: analytes which cannot be resolved by the detector may be separated in this step; in ms analysis, ion suppression is reduced; the retention time of the analyte serves as information regarding its identity. this separation step is not mandatory and is often omitted in nmr and "shotgun" based approaches such as shotgun lipidomics. gas chromatography (gc), especially when interfaced with mass spectrometry (gc-ms), is a widely used separation technique for metabolomic analysis. gc offers very high chromatographic resolution, and can be used in conjunction with a flame ionization detector (gc/fid) or a mass spectrometer (gc-ms). the method is especially useful for identification and quantification of small and volatile molecules. however, a practical limitation of gc is the requirement of chemical derivatization for many biomolecules as only volatile chemicals can be analysed without derivatization. in cases where greater resolving power is required, two-dimensional chromatography (gcxgc) can be applied. high performance liquid chromatography (hplc) has emerged as the most common separation technique for metabolomic analysis. with the advent of electrospray ionization, hplc was coupled to ms. in contrast with gc, hplc has lower chromatographic resolution, but requires no derivatization for polar molecules, and separates molecules in the liquid phase. additionally hplc has the advantage that a much wider range of analytes can be measured with a higher sensitivity than gc methods. capillary electrophoresis (ce) has a higher theoretical separation efficiency than hplc (although requiring much more time per separation), and is suitable for use with a wider range of metabolite classes than is gc. as for all electrophoretic techniques, it is most appropriate for charged analytes. detection methods mass spectrometry (ms) is used to identify and quantify metabolites after optional separation by gc, hplc, or ce. gc-ms was the first hyphenated technique to be developed. identification leverages the distinct patterns in which analytes fragment. these patterns can be thought of as a mass spectral fingerprint. libraries exist that allow identification of a metabolite according to this fragmentation pattern . ms is both sensitive and can be very specific. there are also a number of techniques which use ms as a stand-alone technology: the sample is infused directly into the mass spectrometer with no prior separation, and the ms provides sufficient selectivity to both separate and to detect metabolites. for analysis by mass spectrometry, the analytes must be imparted with a charge and transferred to the gas phase. electron ionization (ei) is the most common ionization technique applied to gc separations as it is amenable to low pressures. ei also produces fragmentation of the analyte, both providing structural information while increasing the complexity of the data and possibly obscuring the molecular ion. atmospheric-pressure chemical ionization (apci) is an atmospheric pressure technique that can be applied to all the above separation techniques. apci is a gas phase ionization method, which provides slightly more aggressive ionization than esi which is suitable for less polar compounds. electrospray ionization (esi) is the most common ionization technique applied in lc/ms. this soft ionization is most successful for polar molecules with ionizable functional groups. another commonly used soft ionization technique is secondary electrospray ionization (sesi). in the 2000s, surface-based mass analysis has seen a resurgence, with new ms technologies focused on increasing sensitivity, minimizing background, and reducing sample preparation. the ability to analyze metabolites directly from biofluids and tissues continues to challenge current ms technology, largely because of the limits imposed by the complexity of these samples, which contain thousands to tens of thousands of metabolites. among the technologies being developed to address this challenge is nanostructure-initiator ms (nims), a desorption/ ionization approach that does not require the application of matrix and thereby facilitates small-molecule (i.e., metabolite) identification. maldi is also used; however, the application of a maldi matrix can add significant background at <1000 da that complicates analysis of the low-mass range (i.e., metabolites). in addition, the size of the resulting matrix crystals limits the spatial resolution that can be achieved in tissue imaging. because of these limitations, several other matrix-free desorption/ionization approaches have been applied to the analysis of biofluids and tissues. secondary ion mass spectrometry (sims) was one of the first matrix-free desorption/ionization approaches used to analyze metabolites from biological samples. sims uses a high-energy primary ion beam to desorb and generate secondary ions from a surface. the primary advantage of sims is its high spatial resolution (as small as 50 nm), a powerful characteristic for tissue imaging with ms. however, sims has yet to be readily applied to the analysis of biofluids and tissues because of its limited sensitivity at >500 da and analyte fragmentation generated by the high-energy primary ion beam. desorption electrospray ionization (desi) is a matrix-free technique for analyzing biological samples that uses a charged solvent spray to desorb ions from a surface. advantages of desi are that no special surface is required and the analysis is performed at ambient pressure with full access to the sample during acquisition. a limitation of desi is spatial resolution because "focusing" the charged solvent spray is difficult. however, a recent development termed laser ablation esi (laesi) is a promising approach to circumvent this limitation. most recently, ion trap techniques such as orbitrap mass spectrometry are also applied to metabolomics research. nuclear magnetic resonance (nmr) spectroscopy is the only detection technique which does not rely on separation of the analytes, and the sample can thus be recovered for further analyses. all kinds of small molecule metabolites can be measured simultaneously - in this sense, nmr is close to being a universal detector. the main advantages of nmr are high analytical reproducibility and simplicity of sample preparation. practically, however, it is relatively insensitive compared to mass spectrometry-based techniques. although nmr and ms are the most widely used, modern day techniques other methods of detection that have been used. these include fourier-transform ion cyclotron resonance, ion-mobility spectrometry, electrochemical detection (coupled to hplc), raman spectroscopy and radiolabel (when combined with thin-layer chromatography). table 1. comparison of most common used metabolomics methods technology sensitivity (lod) sample volume compatible with gases compatible with liquids compatible with solids start-up cost can be used in metabolite imaging (maldi or desi) advantages disadvantages gc-ms 0.5 µm 0.1-0.2 ml yes yes no <$300,000 no quantitative (with calibration) large body of software and databases for metabolite identification detects most organic and some inorganic molecules excellent separation reproductibility destructive (not recoverable) requires sample separation slow (20—40 min per sample) lc-ms 0.5 nm 10—100 µl no yes yes >$300,000 yes very flexible technology detects most organic and some inorganic molecules destructive (not recoverable) not very quantitative slow (15—40 min per sample) usually requires separation nmr spectroscopy 5 µm 10—100 µl no yes yes >us$1 million yes very flexible technology detects most organic and some inorganic molecules large instrument footprint cannot detect or identify salts and inorganic ions cannot detect non-protonated compounds requires large sample volumes (0.1—0.5 ml) statistical methods the data generated in metabolomics usually consist of measurements performed on subjects under various conditions. these measurements may be digitized spectra, or a list of metabolite features. in its simplest form, this generates a matrix with rows corresponding to subjects and columns corresponding with metabolite features (or vice versa). several statistical programs are currently available for analysis of both nmr and mass spectrometry data. a great number of free software are already available for the analysis of metabolomics data shown in the table. some statistical tools listed in the table were designed for nmr data analyses were also useful for ms data. for mass spectrometry data, software is available that identifies molecules that vary in subject groups on the basis of mass-over-charge value and sometimes retention time depending on the experimental design. once metabolite data matrix is determined, unsupervised data reduction techniques (e.g. pca) can be used to elucidate patterns and connections. in many studies, including those evaluating drug-toxicity and some disease models, the metabolites of interest are not known a priori. this makes unsupervised methods, those with no prior assumptions of class membership, a popular first choice. the most common of these methods includes principal component analysis (pca) which can efficiently reduce the dimensions of a dataset to a few which explain the greatest variation. when analyzed in the lower-dimensional pca space, clustering of samples with similar metabolic fingerprints can be detected. pca algorithms aim to replace all correlated variables with a much smaller number of uncorrelated variables (referred to as principal components (pcs)) and retain most of the information in the original dataset. this clustering can elucidate patterns and assist in the determination of disease biomarkers - metabolites that correlate most with class membership. linear models are commonly used for metabolomics data, but are affected by multicollinearity. on the other hand, multivariate statistics are thriving methods for high-dimensional correlated metabolomics data, of which the most popular one is projection to latent structures (pls) regression and its classification version pls-da. other data mining methods, such as random forest, support-vector machines, etc. are received increasing attention for untargeted metabolomics data analysis. in the case of univariate methods, variables are analyzed one by one using classical statistics tools (such as student's t-test, anova or mixed models) and only these with sufficient small p-values are considered relevant. however, correction strategies should be used to reduce false discoveries when multiple comparisons are conducted since there is no standard method for measuring the total amount of metabolites directly in untargeted metabolomics. for multivariate analysis, models should always be validated to ensure that the results can be generalized. machine learning and data mining machine learning is a powerful tool that can be used in metabolomics analysis. recently, scientists have developed retention time prediction software. these tools allow researchers to apply artificial intelligence to the retention time prediction of small molecules in complex mixture, such as human plasma, plant extracts, foods, or microbial cultures. retention time prediction increases the identification rate in liquid chromatography and can lead to an improved biological interpretation of metabolomics data. key applications toxicity assessment/toxicology by metabolic profiling (especially of urine or blood plasma samples) detects the physiological changes caused by toxic insult of a chemical (or mixture of chemicals). in many cases, the observed changes can be related to specific syndromes, e.g. a specific lesion in liver or kidney. this is of particular relevance to pharmaceutical companies wanting to test the toxicity of potential drug candidates: if a compound can be eliminated before it reaches clinical trials on the grounds of adverse toxicity, it saves the enormous expense of the trials. for functional genomics, metabolomics can be an excellent tool for determining the phenotype caused by a genetic manipulation, such as gene deletion or insertion. sometimes this can be a sufficient goal in itself—for instance, to detect any phenotypic changes in a genetically modified plant intended for human or animal consumption. more exciting is the prospect of predicting the function of unknown genes by comparison with the metabolic perturbations caused by deletion/insertion of known genes. such advances are most likely to come from model organisms such as saccharomyces cerevisiae and arabidopsis thaliana. the cravatt laboratory at the scripps research institute has recently applied this technology to mammalian systems, identifying the n-acyltaurines as previously uncharacterized endogenous substrates for the enzyme fatty acid amide hydrolase (faah) and the monoalkylglycerol ethers (mages) as endogenous substrates for the uncharacterized hydrolase kiaa1363. metabologenomics is a novel approach to integrate metabolomics and genomics data by correlating microbial-exported metabolites with predicted biosynthetic genes. this bioinformatics-based pairing method enables natural product discovery at a larger-scale by refining non-targeted metabolomic analyses to identify small molecules with related biosynthesis and to focus on those that may not have previously well known structures. fluxomics is a further development of metabolomics. the disadvantage of metabolomics is that it only provides the user with abundances or concentrations of metabolites, while fluxomics determines the reaction rates of metabolic reactions and can trace metabolites in a biological system over time. nutrigenomics is a generalised term which links genomics, transcriptomics, proteomics and metabolomics to human nutrition. in general, in a given body fluid, a metabolome is influenced by endogenous factors such as age, sex, body composition and genetics as well as underlying pathologies. the large bowel microflora are also a very significant potential confounder of metabolic profiles and could be