NMR and pattern recognition methods in metabolomics: From data acquisition to biomarker discovery: A review

Metabolomics is the discipline where endogenous and exogenous metabolites are assessed, identified and quantified in different biological samples. Metabolites are crucial components of biological system and highly informative about its functional state, due to their closeness to functional endpoints and to the organism's phenotypes. Nuclear Magnetic Resonance (NMR) spectroscopy, next to Mass Spectrometry (MS), is one of the main metabolomics analytical platforms. The technological developments in the field of NMR spectroscopy have enabled the identification and quantitative measurement of the many metabolites in a single sample of biofluids in a non-targeted and non-destructive manner. Combination of NMR spectra of biofluids and pattern recognition methods has driven forward the application of metabolomics in the field of biomarker discovery. The importance of metabolomics in diagnostics, e.g. in identifying biomarkers or defining pathological status, has been growing exponentially as evidenced by the number of published papers. In this review, we describe the developments in data acquisition and multivariate analysis of NMR-based metabolomics data, with particular emphasis on the metabolomics of Cerebrospinal Fluid (CSF) and biomarker discovery in Multiple Sclerosis (MScl).     

Using a DEA-cross efficiency approach in public procurement tenders

The paper addresses the topic of supplier selection in public procurement. According to European directives, when tenders are awarded through the “Most Economically Advantageous Tender” (MEAT) criterion, the awarding committee has to decide the tender evaluation criteria of the presented bids in advance. The authors propose a decision making tool that is aimed at helping the awarding committee in this difficult task and, at the same time, maintaining a transparent procedure in accordance with governmental procurement regulations and requirements as well as guaranteeing fair and equal evaluation of all bids. In this regard, the decision problem of supplier selection is addressed by applying an extension of the DEA (data envelopment analysis) methodology. The cross-efficiency evaluation is used for selecting the best supplier among the eligible candidates. The proposed technique allows the evaluation of quantitative data related to vendor selection and keeps the transparency features requested by public procurement. In addition, all bids are equally assessed according to the same objectively defined weights without any subjective setting by the public officers. The effectiveness and efficiency of the approach is supported by a case study that pertains to the tender of an Italian public agency.     

Effects of imprecise weighting in hierarchical preference programming

Preference programming is a general term for multi-criteria decision analytical approaches allowing incomplete preference information. In the PAIRS method, interval judgments are assigned to weight ratios between attributes to model imprecision in multi-attribute value trees. This paper studies the effects of a hierarchical model structure on the overall imprecision, as the form of the hierarchy also affects the form of imprecision that can be assigned to the model. The aim is to find out good procedural practices for reducing overall imprecision descending inherently from the model structure. The study provides simulation results about the ability of various weighting schemes to identify dominated alternatives, which are discussed with respect to other issues related to the weighting process. According to the results, a hierarchical model is structurally somewhat more unable to identify dominances than a corresponding nonhierarchical model, but its cognitive advantages often cancel out this. The results also suggest paying reasonable attention to the precision of the lower level judgments and to identifying possible correlations between the criteria.     

Satisfactory solution concepts and their relations for Stochastic Multiobjective Programming problems

This work deals with the concept of satisfactory solution for Stochastic Multiobjective Programming (SMP) problems. Based on previous literature, we will introduce different concepts of satisfactory solutions for SMP problems, define a new concept of solution (where the decision maker (DM) sets his/her preferences in terms of two aspiration levels for the stochastic objective and two probabilities to reach those levels), and establish some relationship between these concepts. The results will aim at featuring these concepts and determine the differences between them. Moreover, the paper proposes a new step by step procedure to exchange information between the analyst and DM prior to solving the problem. Thus, the DM will be able to choose the transformation criterion for each stochastic objective and the aspiration level.     

Determination of Quercetin in Extracts of Ginkgo biloba L. Leaves by Near‐Infrared Reflectance Spectroscopy Based on Interval Partial Least‐Squares (iPLS) Model

Abstract

This paper developed a multivariate method of analysis of quercetin in Ginkgo biloba leaf extracts, based on reflectance NIR measurements and partial least squares regression. In order to give a better correlation with the results obtained by HPLC, multiplicative scatter correction (MSC) was utilized to correct scattering effect and interval partial least squares (iPLS) to select optimum wavelength region. In general, good calibration statistics were obtained for the prediction of quercetin content, as demonstrated by some figures of merit, namely linearity, repeatability, and accuracy. And the iPLS model was more reliable than the full model.     

Automatic Computerized Analysis of Multi-Exponential Decay Curves

Abstract

A prototype method is described which automatically analyzes exponential decay curves by using nonlinear regression analysis and deviation pattern recognition. Lifetimes and preexponential factors are computed for the proper model. The program successfully classified simulated data consisting of one, two, or three exponentials when noise followed a Poisson distribution. However, for real luminescence decay data, systematic instrumental noise of higher frequency tended to obscure low frequency deviations from incorrect models in smoothed deviation plots. This factor complicated classification of multi-exponential data, although the program easily distinguished between single and multiple exponential behavior.     

The Use of Encoded Structural Data to Predict the Liquid Chromatographic Retention Behavior of Tetradentate Metal Complexes

Abstract

Retention data for tetradentate metal complexes of copper and nickel are reported for microparticulate silica and reversed phase columns. An attempt is made to relate structure and stability constant data to the retention volume by means of multiple linear regression. The results indicate that if the structure is encoded by means of dummy variables it may be possible to predict retention volumes of metal complexes. There is not a strong correlation between the value of the stability constants for the metal diamine and metal ketone complexes and the retention volume.     

Enhanced Characterization of Naproxen Formulation by Near Infrared Spectroscopy

Near infrared spectroscopy in combination with appropriate chemometric methods is an effective technique for quantitative analysis of parameters of interest for the pharmaceutical industry. In this study, the artificial neural network (ANN) was applied to monitor critical parameters (compression force, tablet hardness, mean particle size, and active pharmaceutical ingredient concentration of tablets) in the process of naproxen pharmaceutical preparation. The performance of ANN was compared to linear methods (partial least squares regression (PLS) and synergy interval partial squares (siPLS)). The ANN models for compression force, tablet hardness, mean particle size, and active pharmaceutical ingredient concentration of tablets yielded the low root mean square error of prediction (RMSEP) values of 0.936 KN, 0.302 kg, 4.49 mg, and 2.14 µm, respectively. The predictive ability of the PLS model was improved by siPLS with selection of spectral regions and the best performance among all calibration methods was showed by the nonlinear method (ANN). Effective models were built by using these approaches using near infrared spectroscopy.     

A New Calibration Method for the Accurate Determination of Ethylene Content in Ethylene-Propylene Copolymers by CRYSTEX-IR

Summary: CRYSTEX is a fully automated instrument for the determination of Xylene Solubles in polypropylene and ethylene-propylene copolymers, providing an excellent correlation with gravimetric methods. The instrument can be equipped with a dual band infrared (IR) detector to quantify the ethylene weight percentage (C2%) in the amorphous fraction, and in the whole polymer sample. In this work, a new approach based on multiple linear regression (MLR) models is presented, which makes use of two independent IR absorbance signals acquired simultaneously by the detector. A compromise model to predict directly C2% is proposed, which balances good accuracy and reduced experimental effort. MLR models able to predict the total sample concentration were also developed. The results were successfully validated using copolymer standards in a wide chemical composition range.     

Carbon Nanotube Based Sensor for Simultaneous Determination of Acetaminophen and Ascorbic Acid Exploiting Multiple Response Optimization and Measures in the Presence of Surfactant

A simple procedure for the simultaneous determination of acetaminophen (AC) and ascorbic acid (AA) by differential pulse voltammetry (DPV) using a carbon nanotube paste electrode exploiting measures in cetylpyridinium bromide (CPB) medium is described. Under the best instrumental parameters of DPV, optimized by means of factorial design, the calibration plots in the range 100.0–700.0 µmol L^?1 (r=0.993) and 39.4–146.3 µmol L^?1 (r=0.995) with limits of detection of 7.1 and 2.1 µmol L^?1, were achieved for AA and AC, respectively. The developed method was successfully applied for the AC and AA determination in pharmaceutical formulations, whose accuracy was attested by comparison with HPLC method.