Nuclear magnetic resonance metabonomic profiling using tO2PLS

Blood plasma collected from adult fish (black bream, Sparidae) exposed to a dose of 5 mg kg⁻¹ 17β-estradiol underwent metabonomic profiling using nuclear magnetic resonance (NMR). An extension of the orthogonal 2 projection to latent structure (O2PLS) analysis, tO2PLS, was proposed and utilized to classify changes between the control and experimental metabolic profiles. As a bidirectional modeling tool, O2PLS examines the (variable) commonality between two different data blocks, and extracts the joint correlations as well as the unique variations present within each data block. tO2PLS is a proposed matrix transposition of O2PLS to allow for commonality between experiments (spectral profiles) to be observed, rather than between sample variables. tO2PLS analysis highlighted two potential biomarkers, trimethylamine-N-oxide (TMAO) and choline, that distinguish between control and 17β-estradiol exposed fish. This study presents an alternative way of examining spectroscopic (metabolite) data, providing a method for the visual assessment of similarities and differences between control and experimental spectral features in large data sets.     

Application of ethyl chloroformate derivatization for gas chromatography-mass spectrometry based metabonomic profiling

A new combined gas chromatography and mass spectrometry (GC-MS) method has been developed suitable for the urine sample treatment in aqueous phase with ethyl chloroformate (ECF) derivatization agents. The method has been extensively optimized and validated over a broad range of different compounds and urine samples. Analysis of test metabolite derivatives, containing spiked standards, or rat urine exhibited acceptable linearity, satisfactory intra-batch precision (repeatability) and stability, relative standard deviations (R.S.D.) less than 10 and 15% within 48 h, respectively. The quantification limits were 150-300 pg on column for most metabolites. Recovery of several representative compounds, at different concentrations, ranged from 70 to 120%, with R.S.D. better than 10% for rat urine. We were able to generally eliminate potentially confounding variables such as medium complexity, different urea concentrations, and/or derivatization procedure variability. Metabonomic profiling of 1,2-dimethylhydrazine (DMH)-induced precancerous colon rat urine using GC-MS with ECF derivatization was performed to evaluate the proposed method. The analytical variation of the method was smaller than the biological variation in the rat urine samples, proving the suitability of the method to analyze differences in the metabonome of a living system with perturbed metabolic network. Thus, the proposed GC-MS analytical method is reliable to analyze a large variety of metabolites and can be used to investigate human pathology including disease onset, progression, and mortality.     

A new hybrid strategy for constructing a robust calibration model for near-infrared spectral analysis

A new hybrid algorithm is proposed for construction of a high-quality calibration model for near-infrared (NIR) spectra that is robust against both spectral interference (including background and noise) and multiple outliers. The algorithm is a combination of continuous wavelet transform (CWT) and a modified iterative reweighted PLS (mIRPLS) procedure. In the proposed algorithm the spectral interference is filtered by CWT at the first stage then mIRPLS is proposed to detect the multiple outliers in the CWT domain. Compared with the original IRPLS method, mIRPLS does not need to adjust variable parameters to achieve optimum calibration results, which makes it very convenient to perform in practice. The final PLS model is constructed robustly because both the spectral interference and multiple outliers are eliminated. In order to validate the effectiveness and universality of the algorithm, it was applied to two different sets of NIR spectra. The results indicate that the proposed strategy can greatly enhance the robustness and predictive ability of NIR spectral analysis.     

Study of metabonomic profiles of human esophageal carcinoma by use of high-resolution magic-angle spinning 1H NMR spectroscopy and multivariate data analysis

Esophageal carcinoma (EC) is one of the most common malignant tumors. EC survival has remained disappointingly low because of the high malignancy of esophageal cancer and the lack of obvious clinical symptoms at an early stage. Early diagnosis is often difficult because the small tumor nodules are frequently missed. Metabonomics based on high-resolution magic-angle spinning (HRMAS) NMR has been popular for tumor detection because it is highly sensitive, provides rich biochemical information and requires no sample pretreatment. ¹H HRMAS spectra of non-involved adjacent esophageal tissues and of well differentiated and moderately differentiated esophageal carcinoma tumors were recorded and analyzed by use of multivariate and statistical analysis techniques. Moderately differentiated EC tumors were found to have increased total choline, alanine, and glutamate and reduced creatine, myo-inositol, and taurine compared with non-involved adjacent tissues. Moreover, clear differences between the metabonomic profiles of EC tissues enabled tumor differentiation. Furthermore, the integral Gly/MI ratio for samples of different tissue types were statistically significantly different; this was sufficient both for distinguishing non-involved tissues from esophageal carcinoma and for classification of well differentiated and moderately differentiated EC tumors.

High-energy detector calibration data for k 0-neutron activation analysis

Journal of Radioanalytical and Nuclear Chemistry - In k 0-neutron activation analysis, HPGe detectors have to be calibrated up to about 3.1 MeV, in order to properly determine the Na, Ca...     

A robust,general automatic phase correction algorithm for high‐resolution NMR data

The trends towards rapid NMR data acquisition, automated NMR spectrum analysis, and data processing and analysis by more naïve users combine to place a higher burden on data processing software to automatically process these data. Downstream data analysis is compromised by poor processing, and the automated processing algorithms must therefore be robust and accurate. We describe a new algorithm for automatic phase correction of frequency‐domain, high‐resolution NMR spectra. We show this to be reliable for data derived from a wide variety of typical NMR usages. We therefore conclude that the method will have wide‐spread applicability and a positive impact on automated spectral processing and analysis. Copyright © 2017 John Wiley & Sons, Ltd.     

Building robust calibration models for the analysis of estrogens by gas chromatography with mass spectrometry detection

Five hormonal growth promotants (diethylstilbestrol, hexestrol, dienestrol, 17-β-estradiol and 17-α-ethynylestradiol) have been analysed by gas chromatography with mass spectrometry detection (GC/MS, SIM mode) for four non-consecutive days. The aim is to build models with stable predictions. The strategies applied are internal standardization and global models carried out by gathering signals recorded on several days. Two models were examined: univariate models (with standardized peak area) and PARAFAC2 (the analyte scores were standardized by the scores of the internal standard). Internal standardization has been proved to be efficient for both models of dienestrol and ethynylestradiol. The mean relative error in absolute value when samples recorded on a different day to the calibration set are quantified by PARAFAC2 is 7.00% and 7.11% for dienestrol and ethynylestradiol, respectively. For diethylstilbestrol and estradiol, internal standardization was combined with global calibration models built with signals recorded under several sources of variability (different days). Thus predictions become steadier over time and in the estradiol example, errors decrease from 33.10% to 9.76%. The mean relative error in absolute value with PARAFAC2 updated models oscillates between 6.34% for ethynylestradiol and 10.74% for diethylstilbestrol. For univariate updated models errors range from 6.42% to 14.19% for ethynylestradiol and estradiol respectively. The combination of both strategies has been proved to be efficient independently of the analyte and of the signal order.     

Multivariate calibration of spectral data using dual-domain regression analysis

To date, few efforts have been made to take simultaneous advantage of the local nature of spectral data in both the time and frequency domains in a single regression model. We describe here the use of a novel chemometrics algorithm using the wavelet transform. We call the algorithm dual-domain regression, as the regression step defines a weighted model in the time-domain based on the contributions of parallel, frequency-domain models made from wavelet coefficients reflecting different scales. In principle, any regression method can be used, and implementation of the algorithm using partial least squares regression and principal component regression are reported here. The performance of the models produced from the algorithm is generally superior to that of regular partial least squares (PLS) or principal component regression (PCR) models applied to data restricted to a single domain. Dual-domain PLS and PCR algorithms are applied to near infrared (NIR) spectral datasets of Cargill corn samples and sets of spectra collected on batch chemical reactions run in different reactors to illustrate the improved robustness of the modeling.     

Modern strategies for PC-supported spectral analysis of 1D NMR data

 Analysis of high-resolution NMR spectra elucidation has been known for many years. Hard-and software development now permits the implementation of such programs on personal computers. The structural information hidden in complex proton NMR spectra becomes easily accessible by using graphical user interfaces and direct data exchange between programs. A new mode has been implemented in 1D WIN-NMR to support the analysis of multiplet patterns with first order rules. Structure display, direct export mechanisms to the simulation program WIN-DAISY, and an archiving possibility complete the state-of-the-art data analysis. Some practical examples are given. Received: 25 October 1996/Revised: 6 March 1997/Accepted: 10 March 1997     

Eliminating the dication-induced intersample chemical-shift variations for NMR-based biofluid metabonomic analysis

NMR-based urinary metabonomic analysis is an essential aspect of systems biology for understanding mammalian physiology and pathophysiology though intersample chemical-shift variations can cause serious problems. Here, we report two optimized and validated methods to eliminate such variations resulting from intersample differences in pH and dication concentration. We found that the Ca(2+) concentration was 7.41 ± 3.48, 1.03 ± 0.34 and 0.87 ± 0.52 mM whereas the Mg(2+) concentration was 3.02 ± 1.41, 2.65 ± 1.20 and 0.80 ± 0.59 mM in rat, mouse and human urine samples, respectively; urinary Ca-EDTA, Mg-EDTA and free EDTA had spin-lattice relaxation time values (600.13 MHz) of 0.38, 0.41 and 0.55 s, respectively. We also found that the combined treatments with potassium fluoride, phosphate buffer and a small amount of K(3)EDTA eliminated intersample chemical-shift variations for all metabolites. EDTA treatment followed with phosphate buffer also achieved similar results although resonances from EDTA and its complexes obscured some metabolite signals. We systematically optimized the amount of additives for rat, mouse and human urine samples taking into consideration the pH control, signal-to-noise ratio and intersample uniformity for metabolite chemical-shifts. Based on thorough validation, we established some optimized procedures for rat, mouse and human urine, respectively. By eliminating both pH and dication effects, these methods enable the reduction of intersample chemical-shift variations to 1.5 Hz for all metabolites. The methods will offer ensured data quality for high-throughput, especially robotic urinary metabonomics studies with no need for peak alignments or corrections.     

Urine profiling using capillary electrophoresis-mass spectrometry and multivariate data analysis

This work presents the development of a general and fast method for metabolic profiling of urine, using capillary electrophoresis-electrospray ionisation mass spectrometry (CE-ESIMS) and multivariate data analysis (DA). Human urine samples collected before and after ingestion of paracetamol were analysed at acidic and basic CE conditions, using both positive and negative ESI-MS detection. Analysis of the entire resulting data set, with no prior knowledge of the target compounds, using pair-wise 'fuzzy' correlation and eigenvalue analysis enabled the samples to be discriminated between on the basis of blank urine and urine collected after drug intake. By generating two-dimensional loadings plots, it was also possible to identify the m/z values of the substances responsible for the differentiation between control and dosed samples.     

Sensor array data profiling for gas identification

The paper presents a new method of qualitative identification of gas. It is based on a dynamic response of sensor array with the emphasis on the processing of discrete measurement data. The information needed for identification of test samples is obtained in course of profiling the data from calibration measurements. This operation consists of the following steps: classification of data sets, selection of representative data sets, parameterization of classifiers associated with representative data sets and determination of data records. In our work Discriminant Function Analysis was used for data classification. The information saved in data record describes: the sequential number of discrete measurement, combination of gas sensors in this measurement which are best for classification of calibration samples, and the parameters of associated classifier. They are identifiers of gas class. The procedure of data record determination itself is time consuming. However this operation will be performed only at the stage of the development of the measurement instrument and when its malfunction is diagnosed. The routine use of the instrument will be restricted to gas identification task, which only utilizes the results of profiling.The identification of unknown gas is performed on the base of data records and measurement data obtained for this gas. Data records guide the preparation of data sets, separately for each class of gases. These data sets are used as input of the discriminant functions which have parameter values also indicated by data records. It was shown in the present contribution, that the qualitative identification of nine test gas samples (vapors of ethanol, acetic acid and ethyl acetate in air) with our method was very accurate and fast.     

Advanced pulse calibration techniques for the quantitative analysis of TG–FTIR data

Two different pulse calibration techniques to estimate the total quantities of evolved gaseous substances formed in thermogravimetric (TG)–FTIR runs were compared and assessed. A gas-pulse calibration method was based on the use of a specific device able of sending a known quantity of a gaseous compound of interest to the FTIR analyzer. A second calibration method was based on the vaporization in the TG analyzer of liquid solutions of the compound of interest. Data obtained by these techniques were compared to those from conventional concentration-based calibration. The results confirmed the reliability of pulse calibration techniques to obtain quantitative data on evolved gaseous products in TG–FTIR applications. Moreover, both the gas-pulse and the vaporization-based calibration techniques proved to have several advantages with respect to conventional techniques. Among these are the need of a more limited number of standards and no need for online gas dilution systems.     

Development and validation of a gas chromatography/mass spectrometry metabonomic platform for the global profiling of urinary metabolites

This paper presents a simple and reliable gas chromatography/mass spectrometry (GC/MS) method for the metabonomic analysis of human urine samples. The sample preparation involved the depletion of excess urea via treatment with urease and subsequent protein precipitation using ice-cold ethanol. An aliquot of the mixture was separated, dried, trimethylsilyl (TMS)-derivatized and 1.0 microL of the derivatized extract was injected into the GC/MS system via split injection (1:10). Approximately 150 putative metabolites belonging to different chemical classes were identified from the pooled human urine samples. All the identified metabolites were selected to evaluate precision and stability of the GC/MS assay. More than 95% of the metabolites demonstrated good reproducibility, with intra-day and inter-day precision values below 15%. Metabolic profiling of 53 healthy male and female urine samples in combination with pattern recognition techniques was performed to further validate the GC/MS metabolite profiling assay. Principal component analysis (PCA) followed by orthogonal partial least squares analysis (OPLS) revealed differences between urinary metabolite profiles of healthy male and female subjects. This validated GC/MS metabolic profiling method may be further applied to the metabonomic screening of urinary biomarkers in clinical studies. Copyright (c) 2008 John Wiley & Sons, Ltd.     

Comparison of robust regression methods based on least-median and adaptive kalman filtering approaches applied to linear calibration data

Five algorithms for data analysis are evaluated for their abilities to discriminate against outliers in small data sets (4–10 points). These methods included least-squares regression, the least absolute -deviation method, the least median of squares method, and two techniques based on an adaptive Kalman filter. For data sets consisting of 4–9 points with one outlier, the average errors in the estimation of the slope were found to be 18.9 % by least-squares, 17.7% by the least absolute deviation method, 0.5% by the least median of squares algorithm, 9.1% by an adaptive Kalman filter algorithm, and 0.9% by a zero-lag adaptive Kalman filter algorithm. Based on these results, the conclusion is that the zero-lag adaptive Kalman filter and the least median of squares approaches are best suited for the detection of outliers in small calibration data sets.     

Multivariate calibration maintenance and transfer through robust fused LASSO

This article studies calibration maintenance and transfer to build a statistical model that is able to predict analyte concentrations by a set of spectra. Noticing that the wavelength atoms are naturally ordered in a meaningful way, we propose a novel robust fused LASSO (RFL) based on high‐dimensional sparsity techniques and a recent Θ‐IPOD technique for robustification. This new approach can attain simultaneous wavelength selection and grouping as well as outlier identification, without any human intervention. An efficient and scalable algorithm is developed on the basis of the alternating direction method of multipliers. The obtained RFL model is sparse and shows improved prediction performance over the LASSO and ridge regression. Our results reveal that wavelengths can be combined into blocks, in a smart manner, to enhance the interpretability and reliability for super‐resolution spectral analysis. Copyright © 2013 John Wiley & Sons, Ltd.     

A metabonomic investigation of hepatotoxicity using diffusion-edited 1H NMR spectroscopy of blood serum

It has been shown extensively, that chemometric investigations of 1H NMR spectra of rat urine taken from animals dosed with model toxins produce characteristic patterns of metabolic responses and that this permits the identification of biomarkers of toxic response and regeneration. To date, metabonomic methods have been mainly optimised for urine which contains mainly low molecular weight moieties, and thus a conventional 1-dimensional 1H NMR pulse sequence is an efficient means of obtaining information-rich data. In the case of biofluids such as blood plasma or serum, which contain a wide range of macromolecules the resonances of which can overlap with peaks from small molecule metabolites, the information giving rise to sample classification can be concealed in a conventional NMR spectrum andthis presents a different analytical challenge in terms of chemometric analysis of spectral profiles. Here, the use of other types of NMR data have been investigated and it is shown that by using spectra where the peak intensities are edited according to their molecular diffusion coefficients, it is possible to improve differentiation of control animals and those treated with the model hepatotoxin, alpha-naphthylisothiocyanate (ANIT). By using diffusion-edited spectroscopy, plasma lipid moieties are less attenuated than those from small endogenous metabolites and thus the toxin-induced changes to the lipoprotein profiles are more easily detectable.