Isotopic C-13 NMR spectrometry, which is able to measure intra-molecular C-13 composition, is of emerging demand because of the new information provided by the C-13 site-specific content of a given molecule. A systematic evaluation of instrumental behaviour is of importance to envisage isotopic C-13 NMR as a routine tool. This paper describes the first collaborative study of intra-molecular C-13 composition by NMR. The main goals of the ring test were to establish intra-and inter-variability of the spectrometer response. Eight instruments with different configuration were retained for the exercise on the basis of a qualification test. Reproducibility at the natural abundance of isotopic C-13 NMR was then assessed on vanillin from three different origins associated with specific delta C-13(i) profiles. The standard deviation was, on average, between 0.9 and 1.2 parts per thousand for intra-variability. The highest standard deviation for inter-variability was 2.1%. This is significantly higher than the internal precision but could be considered good in respect of a first ring test on a new analytical method. The standard deviation of delta C-13(i) in vanillin was not homogeneous over the eight carbons, with no trend either for the carbon position or for the configuration of the spectrometer. However, since the repeatability for each instrument was satisfactory, correction factors for each carbon in vanillin could be calculated to harmonize the results.
(C) 2013 Elsevier B.V. All rights reserved.
Analytical capabilities of Nanoscopic Secondary Ion Mass Spectrometry (nano-SIMS) and Synchrotron Radiation based X-ray Fluorescence (SR nano-XRF) techniques were compared for nanochemical imaging of polymorphonuclear human neutrophils (PMNs). PMNs were high pressure frozen (HPF), cryosubstituted, embedded in Spurr's resin and cut in thin sections (500 nm and 2 mu m for both techniques resp.) Nano-SIMS enabled nanoscale mapping of isotopes of C, N, O, P and S, while SR based nano-XRF enabled trace level imaging of metals like Ca, Mn, Fe, Ni, Cu and Zn at a resolution of approx. 50 nm. The obtained elemental distributions were compared with those of whole, cryofrozen PMNs measured at the newly developed ID16A nano-imaging beamline at the European Synchrotron Radiation Facility (ESRF) in Grenoble, France. Similarities were observed for elements more tightly bound to the cell structure such as phosphorus and sulphur, while differences for mobile ions such as chlorine and potassium were more pronounced. Due to the observed elemental redistribution of mobile ions such as potassium and chlorine, elemental analysis of high pressure frozen (HPF), cryo-substituted and imbedded cells should be interpreted critically. Although decreasing analytical sensitivity occurs due to the presence of ice, analysis of cryofrozen cells - close to their native state - remains the golden standard. In general, we found nanoscale secondary ion mass spectrometry (nano-SIMS) and synchrotron radiation based nanoscopic X-ray fluorescence (SR nano-XRF) to be two supplementary alternatives for nanochemical imaging of single cells at the nanoscale.
Experimental human exposure studies are an effective tool to study adverse health effects from acute inhalation of particulate matter and other constituents of air pollution. In this randomized and double-blinded crossover study, we investigated the systemic effect on bioactive lipid metabolite levels after controlled biodiesel exhaust exposure of healthy humans and compared it to filtered air at a separate exposure occasion. Eicosanoids and other oxylipins, as well as endocannabinoids and related lipids, were quantified in plasma from 14 healthy volunteers at baseline and at three subsequent time points (2, 6, and 24 h) after 1 h exposure sessions. Protocols based on liquid chromatography (LC) coupled to tandem mass spectrometry (MS/MS) methods were developed to detect temporal changes in circulating levels after biodiesel exhaust exposure. The exhaust was generated by a diesel engine fed with an undiluted rapeseed methyl ester fuel. Among the 51 analyzed lipid metabolites, PGF(2 alpha), 9,10-DiHOME, 9-HODE, 5-HETE, 11-HETE, 12-HETE, and DEA displayed significant responsiveness to the biodiesel exhaust exposure as opposed to filtered air. Of these, 9-HODE and 5-HETE at 24 h survived the 10% false discovery rate cutoff (p < 0.003). Hence, the majority of the responsive lipid metabolites were monohydroxy fatty acids. We conclude that it is possible to detect alterations in circulating bioactive lipid metabolites in response to biodiesel exhaust exposure using LC-MS/MS, with emphasis on metabolites with inflammation related properties and implications on cardiovascular health and disease. These observations aid future investigations on air pollution effects, especially with regard to cardiovascular outcomes.
In this study, cetyltrimethylammonium bromide and zeolitic imidazolate framework-8 (ZIF-8) were first assembled via the chemical co-precipitation, and high-quality carbon-based metal-free nanomaterials were synthesized using a heat-treatment process. The internal and morphological characteristics of hexagonal Star ZIF-8 were investigated using scanning electron microscopy, X-ray diffraction, and X-ray photoelectron spectroscopy. The electrochemical sensor with a good response to Cd(II) was prepared via square-wave anodic stripping voltammetry (SWASV) with Star ZIF-8 nanomaterial-modified glassy carbon electrodes. The main parameters were adjusted to obtain the optimal stripping response and a wide linear range. Concurrently, under the calculation of SWASV, the sensitivity of Star ZIF-8-Nafion/GCE to Cd(II) was increased by five orders of magnitude (0.5–230 μg/L), and the determination level was even low to 0.48 μg/L. Based on the high anti-interference ability and stability of the sensor, the application potential of Star ZIF-8 carbon-based metal-free nanomaterials for the detection of trace Cd(II) in was confirmed.
Substituted imidazoles and leaving groups known from other areas of chemistry have been tested for catalytic efficiency in the peroxyoxalate chemiluminescence (PO-CL) reaction, using bis(2,4,6-trichlorophenyl)oxalate (TCPO) as reagent. Catalytic activity was found for 1,2,4-triazole, 1-methylimidazole, 2-methylimidazole, 4-methylimidazole and 4,5-dichloroimidazole, while 2-mercaptoimidazole, 2-nitroimidazole, 4-nitroimidazole, benzimidazole, benzotriazole, thiazole, o-benzoic sulfimide, phthalimide, succinimide, 2(1H)-pyridone and 1,2,3-benzotriazine-4(3H)-one yielded no detectable chemiluminescence under the conditions used. None of the tested compounds was more efficient than imidazole in catalyzing the PO-CL reaction. Spectrophotometric measurements of the catalytic breakdown of TCPO showed that an intermediate was formed with almost all catalysts, and that intermediates that were more stable or present at lower concentration led to a reduced catalytic efficiency in the PO-CL reaction of TCPO.
Peroxyoxalate chemiluminescence (PO-CL) detection offers an advantage in chromatographic detection, by the virtue of its multiple unique selectivities and high sensitivity. However, many of the analytical separation techniques available today require observation times in the millisecond range to preserve the band resolution, and as the reaction kinetics of the PO-CL reaction is considerably slower, extra flow elements are needed to observe the reaction in a time window at maximum emission intensity. Since these flow elements increase the complexity of the system and contribute to band-broadening, the rational way to adapt PO-CL detection to miniaturised separation systems is to speed up the reaction, so that it emits an initial burst of light within the acceptable detection time-frame. Although this may result in a lower overall quantum yield, the actual detection sensitivity could be equal to, or better than slower PO-CL systems. By making careful selections of oxalic reagent and catalyst(s) the reaction can be fine-tuned to maximise the intensity. In this work, the time-dependent light emission from the reaction of bis(2,4,6-trichlorophenyl)oxalate (TCPO) was studied under the catalytic influence of imidazole, 1,2,4-triazole, 4-dimethylaminopyridine (DMAP), and 1,8-diazabicyclo-[5.4.0]-undec-7-ene (DBU) in acetonitrile. Both DMAP and DBU accelerated the reaction substantially, but the best combination of reaction speed and intensity was found for a mixture of 0.5 mM DBU and 5 mM 1,2,4-triazole, which reached its maximum emission after only 40 ms and had an emission intensity comparable to that seen with 5 mM imidazole as catalyst.
The study of postprandial metabolism is relevant for understanding metabolic diseases and characterizing personal responses to diet. We combined three analytical platforms – gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) – to validate a multi-platform approach for characterizing individual variation in the postprandial state. We analyzed the postprandial plasma metabolome by introducing, at three occasions, meal challenges on a usual diet, and 1.5 years later, on a modified background diet. The postprandial response was stable over time and largely independent of the background diet as revealed by all three analytical platforms. Coverage of the metabolome between NMR and GC-MS included more polar metabolites detectable only by NMR and more hydrophobic compounds detected by GC-MS. The variability across three separate testing occasions among the identified metabolites was in the range of 1.1–86% for GC-MS and 0.9–42% for NMR in the fasting state at baseline. For the LC-MS analysis, the coefficients of variation of the detected compounds in the fasting state at baseline were in the range of 2–97% for the positive and 4–69% for the negative mode. Multivariate analysis (MVA) of metabolites detected with GC-MS revealed that for both background diets, levels of postprandial amino acids and sugars increased whereas those of fatty acids decreased at 0.5 h after the meal was consumed, reflecting the expected response to the challenge meal. MVA of NMR data revealed increasing postprandial levels of amino acids and other organic acids together with decreasing levels of acetoacetate and 3-hydroxybutanoic acid, also independent of the background diet. Together these data show that the postprandial response to the same challenge meal was stable even though it was tested 1.5 years apart, and that it was largely independent of background diet. This work demonstrates the efficacy of a multi-platform metabolomics approach followed by multivariate and univariate data analysis for a broad-scale screen of the individual metabolome, particularly for studies using repeated measures to determine dietary response phenotype.
Yeast and yeast cultures are frequently used as additives in diets of dairy cows. Beneficial effects from the inclusion of yeast culture in diets for dairy mammals have been reported, and the aim of this study was to develop a comprehensive analytical method for the accurate mass identification of the 'global' metabolites in order to differentiate a variety of yeasts at varying growth stages (Diamond V XP, Yea-Sacc and Levucell). Microwave-assisted derivatization for metabolic profiling is demonstrated through the analysis of differing yeast samples developed for cattle feed, which include a wide range of metabolites of interest covering a large range of compound classes. Accurate identification of the components was undertaken using GC-oa-ToFMS (gas chromatography-orthogonal acceleration-time-of-flight mass spectrometry), followed by principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) for data reduction and biomarker discovery. Semi-quantification (fold changes in relative peak areas) was reported for metabolites identified as possible discriminative biomarkers (p-value <0.05, fold change >2), including d-ribose (four fold decrease), myo-inositol (five fold increase), l-phenylalanine (three fold increase), glucopyranoside (two fold increase), fructose (three fold increase) and threitol (three fold increase) respectively. (C) 2013 Elsevier B. V. All rights reserved.
The utility of a liquid core waveguide (LCW) system for acting as a sentry monitor for compounds such as benzene, toluene, ethylbenzene, xylene (BTEX) in water was examined. A vapor-permeable LCW suitable for long path length absorbance spectroscopy based on a Teflon AF 2400 tube was fabricated. Multiwavelength spectroscopy in the near-UV was carried out using a fiber optic-based flashlamp-photodiode array (PDA) combination with hexane as the solvent in the waveguide core. Using multicomponent calibration, quantitation of benzene and toluene accurate to 6+/-5% could be conducted at sub-mg l(-1) levels in mixtures after a sampling period of 10 min. (C) 2003 Elsevier Science B.V. All rights reserved.
We present a sensitive, selective and robust method for the determination of 14 thiol compounds in aqueous samples. Thiols were derivatized with omega-bromoacetonylquinolinium bromide (BQB) and its deuterium labeled equivalent D7-ω-bromoacetonylquinolinium bromide (D7). Derivatized thiols were preconcentrated by online solid-phase extraction (SPE) followed by liquid chromatography separation and electrospray ionization tandem mass spectrometry determination (SPE/LC-ESI-MS/MS). The robustness of the method was validated for wide ranges in pH, salinity, and concentrations of sulfide and dissolved organic carbon (DOC) to cover contrasting natural water types. The limits of detection (LODs) for the thiols were 3.1-66 pM. Between 6 and 14 of the thiols were detected in different natural sample types at variable concentrations: boreal wetland porewater (0.7-51 nM), estuarine sediment porewater (50 pM-11 nM), coastal sea water (60 pM-16 nM), and sulfate reducing bacterium cultures (80 pM-4 nM). MS/MS fragmentation of the compounds produces two pairs of common product ions, m/z 130.2/137.1 and 218.1/225.1, which enables scanning for unknown thiols in precursor ion scan mode. Using this approach, we identified cysteine, mercaptoacetic acid, N-acetyl-L-cysteine and sulfurothioic S-acid in boreal wetland porewater. The performance of the developed method sets a new state of the art for the determination of thiol compounds in environmental and biological samples.
Background OnPLS is an extension of O2PLS that decomposes a set of matrices, in either multiblock or path model analysis, such that each matrix consists of two parts: a globally joint part containing variation shared with all other connected matrices, and another containing unique or locally joint variation, i.e. variation that is specific to a particular matrix or shared with some, but not all, other connected matrices.
Results A further extension of OnPLS suggested here decomposes the non-globally joint parts into locally joint and unique parts, using the OnPLS method to first find and extract a globally joint model, and then applying OnPLS recursively to subsets of matrices containing the non-globally joint variation remaining after the globally joint variation has been extracted. This results in a set of locally joint models. The variation that is left after the globally joint and locally joint variation has been extracted is not related (by definition) to the other matrices and thus represents the strictly unique variation specific to each matrix. The method's utility is demonstrated by its application to both a simulated data set and a real data set acquired from metabolomic, proteomic and transcriptomic profiling of three genotypes of hybrid aspen.
Conclusions The results show that OnPLS can successfully decompose each matrix into global, local and unique models, resulting in lower numbers of globally joint components and higher intercorrelations of scores. OnPLS also increases the interpretability of models of connected matrices, because of the locally joint and unique models it generates.
Metabolomics is a post genomic research field concerned with developing methods for analysis of low molecular weight compounds in biological systems, such as cells, organs or organisms. Analyzing metabolic differences between unperturbed and perturbed systems, such as healthy volunteers and patients with a disease, can lead to insights into the underlying pathology. In metabolomics analysis, large amounts of data are routinely produced in order to characterize samples. The use of multivariate data analysis techniques and chemometrics is a commonly used strategy for obtaining reliable results. Metabolomics have been applied in different fields such as disease diagnosis, toxicology, plant science and pharmaceutical and environmental research. In this review we take a closer look at the chemometric methods used and the available results within the field of disease diagnosis. We will first present some current strategies for performing metabolomics studies, especially regarding disease diagnosis. The main focus will be on data analysis strategies and validation of multivariate models, since there are many pitfalls in this regard. Further, we highlight the most interesting metabolomics publications and discuss these in detail; additional studies are mentioned as a reference for the interested reader. A general trend is an increased focus on biological interpretation rather than merely the ability to classify samples. In the conclusions, the general trends and some recommendations for improving metabolomics data analysis are provided.
This review is an update of a previous review in 2009 and covers publications from 2009 to 2019. The review focuses on experimental design, referred to as the design of experiments (DoE), used in developing bioanalytical solid-phase microextraction (SPME) methods. Characteristics of different SPME approaches are illustrated and critically discussed. The literature selection evidences that two-level full factorial designs, with a limited number of factors (<5), are most frequently used for preliminary factors screening. When applying the response surface methodology for the quantitative assessment of factorial effects, few quadratic models were used. The most popular were the rotatable central composite and Box-Benkhen designs. Models including more than four factors, such as fractional factorial designs (including the Plackett-Burman and Taguchi designs), were rarely used. Definitive screening and D-Optimal designs were not reported anywhere in the literature selection. When examining the diagnostic criteria used to evaluate different model's quality and validity, it was apparent the researchers relied heavily on commercial software for experimental design, analysis, and reporting of the results.
Polycyclic aromatic hydrocarbons (PAHs) are of environmental concern, for instance when found in contaminated soils at sites where industrial activities have occurred. For efficient screening of such soils, the commercially available enzyme-linked immunosorbent assay (ELISA) kit, the PAH RISc® soil test, can be used. However, the site-specific performance may vary due to differences in soil properties and contamination profiles. Hence, in this study we have examined various contributing factors to the total ELISA measurements uncertainties. These factors include contributions from co-extracted (non-target) compounds, the extraction efficiency and differences in cross-reactivity among the target analytes. Reference values were obtained through pressurized liquid extraction (PLE) and gas chromatography coupled to mass spectrometry (GC–MS) analysis. The results showed that the ELISA does not seem to respond to non-target compounds in the soil extracts to any large extent. Furthermore, high molecular weight PAHs were found to be more efficiently extracted with PLE than with methanol agitation, which is used for ELISA. If this, and the cross-reactivity of the individual PAHs, were taken into consideration, the ELISA and GC–MS results were in good agreement.
A commercially available enzyme-linked immunosorbent assay (ELISA) kit, the PAH soil test, was evaluated with regard to cross-reactivity. Phenanthrene in methanol was used as reference substance. Anthracene, naphthalene and fluorene were chosen as representatives of the 16 US-EPA priority-pollutant polycyclic aromatic hydrocarbons (PAHs). In addition, a number of polycyclic aromatic compounds (PACs), including methyl-, phenyl-, and carbonyl-PAHs, as well as NSO-heterocyclic PACs, found at former industrial sites, were chosen for elucidation of structure/cross-reactivity relationships. The study emphasizes the importance of a priori knowledge of sample composition for accurate interpretation of test results.
This article describes an attempt to develop a sensor based on multi-frequency immittance spectroscopy for the determination of methotrexate (MTX) in blood serum using gold electrodes modified with antibodies. The attachment of antibodies was monitored with electrochemical immittance spectroscopy (EIS) and X-ray photoelectron spectroscopy (XPS). The EIS measurements of MTX resulted in a data matrix of size 39 × 55. The data were analysed using multivariate data analysis and showed a concentration dependence and time dependence that could be separated. This allowed the calculation of a multivariate calibration model. The model showed good linear behavior on a logarithmic scale offering a detection limit of 5 × 10−12 mol L−1.
To find and ascertain phenotypic differences, minimal variation between biological replicates is always desired. Variation between the replicates can originate from genetic transformation but also from environmental effects in the greenhouse. Design of experiments (DoE) has been used in field trials for many years and proven its value but is underused within functional genomics including greenhouse experiments. We propose a strategy to estimate the effect of environmental factors with the ultimate goal of minimizing variation between biological replicates, based on DoE. DoE can be analyzed in many ways. We present a graphical solution together with solutions based on classical statistics as well as the newly developed OPLS methodology.In this study, we used DoE to evaluate the influence of plant specific factors (plant size, shoot type, plant quality, amount of fertilizer) and rotation of plant positions on height and section area of 135 cloned wild type poplar trees grown in the greenhouse. Statistical analysis revealed that plant position was the main contributor to variability among biological replicates and applying a plant rotation scheme could reduce this variation.
Biopolymer sequences (e.g., DNA, RNA, proteins and polysaccharides) and chemical processes (e.g., a batch or continuous polymer synthesis run in a chemical plant) have close similarities from the modelling point of view. When a set of sequences or processes is characterized by multivariate data, a three-way data matrix is obtained. With sequences the position and with processes the time is one direction in this matrix. The multivariate modelling of this matrix by principal component analysis (PCA) or partial least-squares (PLS) methods for the following purposes is discussed: classification of sequences; quantitative relationships between sequence and biological activity or chemical properties; optimizing a sequence with respect to selected properties; process diagnostics; and quantitative relationships between process variables and product quality variables. To obtain good models, a number of problems have to be adequately dealt with: appropriate characterization of the sequence or process; experimental design (selecting sequences or process settings); transforming the three-way into a two-way matrix; and appropriate modelling and validation (modelling interactions, periodicities, “time series” structures and “neighbour effects”). A multivariate approach to sequence and process modelling using PCA and PLS projections to latent structures is discussed and illustrated with several sets of peptide and DNA promoter data.
In batch statistical process control (BSPC), data from a number of "good" batches are used to model the evolution (trajectory) of the process and they also define model control limits, against which new batches may be compared. The benchmark methods used in BSPC include partial least squares (PLS) and principal component analysis (PCA). In this paper, we have used orthogonal projections to latent structures (OPLS) in BSPC and compared the results with PLS and PCA. The experimental study used was a batch hydrogenation reaction of nitrobenzene to aniline characterized by both UV spectroscopy and process data. The key idea is that OPLS is able to separate the variation in data that is correlated to the process evolution (also known as 'batch maturity index') from the variation that is uncorrelated to process evolution. This separation of different types of variations can generate different batch trajectories and hence lead to different established model control limits to detect process deviations. The results demonstrate that OPLS was able to detect all process deviations and provided a good process understanding of the root causes for these deviations. PCA and PLS on the other handwere shown to provide different interpretations for several of these process deviations, or in some cases they were unable to detect actual process deviations. Hence, the use of OPLS in BSPC can lead to better fault detection and root cause analysis as compared to existing benchmark methods and may therefore be used to complement the existing toolbox.
Abstract Methotrexate (MTX), a common pharmaceutical drug in cancer therapy and treatment of rheumatic diseases, is known to cause severe adverse side effects at high dose. As the side effect may be life threatening, there is an urgent need for a continuous, bed-side monitoring of the nominal MTX serum level in a patient while the chemical is being administered. This article describes a detection of MTX using a flow system that consists two modified gold electrodes. Interaction of MTX with the antibodies fixed on the electrode surface is detected by electrochemical impedance spectroscopy and evaluated using singular value decomposition (SVD). The key finding of this work is that the change in the electrode capacitance is found to be quantitative with respect to the concentration of MTX. Moreover a calibration curve constructed using the principal component regression method has a linear range of six orders of magnitude and a detection limit of 1.65 × 10−10 M.
Molecular-level chemical information about organic matter (OM) in sediments helps to establish the sources of OM and the prevalent degradation/diagenetic processes, both essential for understanding the cycling of carbon (C) and of the elements associated with OM (toxic trace metals and nutrients) in lake ecosystems. Ideally, analytical methods for characterizing OM should allow high sample throughput, consume small amounts of sample and yield relevant chemical information, which are essential for multidisciplinary, high-temporal resolution and/or large spatial scale investigations. We have developed a high-throughput analytical method based on pyrolysis-gas chromatography/mass spectrometry and automated data processing to characterize sedimentary OM in sediments. Our method consumes 200 mg of freeze-dried and ground sediment sample. Pyrolysis was performed at 450 degrees C, which was found to avoid degradation of specific biomarkers (e. g., lignin compounds, fresh carbohydrates/cellulose) compared to 650 degrees C, which is in the range of temperatures commonly applied for environmental samples. The optimization was conducted using the top ten sediment samples of an annually resolved sediment record (containing 16-18% and 1.3-1.9% of total carbon and nitrogen, respectively). Several hundred pyrolytic compound peaks were detected of which over 200 were identified, which represent different classes of organic compounds (i. e., n-alkanes, n-alkenes, 2-ketones, carboxylic acids, carbohydrates, proteins, other N compounds, (methoxy) phenols, (poly) aromatics, chlorophyll and steroids/hopanoids). Technical reproducibility measured as relative standard deviation of the identified peaks in triplicate analyses was 5.5 +/- 4.3%, with 90% of the RSD values within 10% and 98% within 15%. Finally, a multivariate calibration model was calculated between the pyrolytic degradation compounds and the sediment depth (i. e., sediment age), which is a function of degradation processes and changes in OM source type. This allowed validation of the Py-GC/MS dataset against fundamental processes involved in OM cycling in aquatic ecosystems.
Both plasma and cerebrospinal fluid (CSF) are rich in cholesterol and its metabolites. Here we describe in detail a methodology for the identification and quantification of multiple sterols including oxysterols and sterol-acids found in these fluids. The method is translatable to any laboratory with access to liquid chromatography – tandem mass spectrometry. The method exploits isotope-dilution mass spectrometry for absolute quantification of target metabolites. The method is applicable for semi-quantification of other sterols for which isotope labelled surrogates are not available and approximate quantification of partially identified sterols. Values are reported for non-esterified sterols in the absence of saponification and total sterols following saponification. In this way absolute quantification data is reported for 17 sterols in the NIST SRM 1950 plasma along with semi-quantitative data for 8 additional sterols and approximate quantification for one further sterol. In a pooled (CSF) sample used for internal quality control, absolute quantification was performed on 10 sterols, semi-quantification on 9 sterols and approximate quantification on a further three partially identified sterols. The value of the method is illustrated by confirming the sterol phenotype of a patient suffering from ACOX2 deficiency, a rare disorder of bile acid biosynthesis, and in a plasma sample from a patient suffering from cerebrotendinous xanthomatosis, where cholesterol 27-hydroxylase is deficient.
Optimal handling is the most important means to ensure adequate sample quality. We aimed to investigate whether pre-centrifugation delay time and temperature could be accurately predicted and to what extent variability induced by pre-centrifugation management can be adjusted for. We used untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics to predict and evaluate the influence of pre-centrifugation temperature and delayed time on plasma samples. Pre-centrifugation temperature (4, 25 and 37 °C; classification rate 87%) and time (5–210 min; Q2 = 0.82) were accurately predicted using Random Forest (RF). Metabolites uniquely reflecting temperature and temperature-time interactions were discovered using a combination of RF and generalized linear models. Time-related metabolite profiles suggested a perturbed stability of the metabolome at all temperatures in the investigated time period (5–210 min), and the variation at 4 °C was observed in particular before 90 min. Fourteen and eight metabolites were selected and validated for accurate prediction of pre-centrifugation temperature (classification rate 94%) and delay time (Q2 = 0.90), respectively. In summary, the metabolite profile was rapidly affected by pre-centrifugation delay at all temperatures and thus the pre-centrifugation delay should be as short as possible for metabolomics analysis. The metabolite panels provided accurate predictions of pre-centrifugation delay time and temperature in healthy individuals in a separate validation sample. Such predictions could potentially be useful for assessing legacy samples where relevant metadata is lacking. However, validation in larger populations and different phenotypes, including disease states, is needed.