Prediction of multivariate chaotic time series via radial basis function neural network

In this article, a new multivariate radial basis functions neural network model is proposed to predict the complex chaotic time series. To realize the reconstruction of phase space, we apply the mutual information method and false nearest‐neighbor method to obtain the crucial parameters time delay and embedding dimension, respectively, and then expand into the multivariate situation. We also proposed two the objective evaluations, mean absolute error and prediction mean square error, to evaluate the prediction accuracy. To illustrate the prediction model, we use two coupled Rossler systems as examples to do simultaneously single‐step prediction and multistep prediction, and find that the evaluation performances and prediction accuracy can achieve an excellent magnitude. © 2013 Wiley Periodicals, Inc. Complexity, 2013.     

A new control chart in contaminated data of t‐Student distribution for individual observations

Control charts are the most popular tool for monitoring production quality. In traditional control charts, it is usually supposed that the observations follow a multivariate normal distribution. Nevertheless, there are many practical applications where the normality assumption is not fulfilled. Furthermore, the performance of these charts in the presence of measurement errors (outliers) in the historical data has been improved using robust control charts when the observations follow a normal distribution. In this paper, we develop a new control chart for t‐Student data based on the trimmed T² control chart () through the adaptation of the elements of this chart to the case of this distribution. Simulation studies show that a control chart performs better than T² in t‐Student samples for individual observations. Copyright © 2012 John Wiley & Sons, Ltd.     

A Review of Wine Authentication Using Spectroscopic Approaches in Combination with Chemometrics

In a global context where trading of wines involves considerable economic value, the requirement to guarantee wine authenticity can never be underestimated. With the ever-increasing advancements in analytical platforms, research into spectroscopic methods is thriving as they offer a powerful tool for rapid wine authentication. In particular, spectroscopic techniques have been identified as a user-friendly and economical alternative to traditional analyses involving more complex instrumentation that may not readily be deployable in an industry setting. Chemometrics plays an indispensable role in the interpretation and modelling of spectral data and is frequently used in conjunction with spectroscopy for sample classification. Considering the variety of available techniques under the banner of spectroscopy, this review aims to provide an update on the most popular spectroscopic approaches and chemometric data analysis procedures that are applicable to wine authentication.     

Nuclear Magnetic Resonance Metabolomics with Double Pulsed-Field-Gradient Echo and Automatized Solvent Suppression Spectroscopy for Multivariate Data Matrix Applied in Novel Wine and Juice Discriminant Analysis

The quality of foods has led researchers to use various analytical methods to determine the amounts of principal food constituents; some of them are the NMR techniques with a multivariate statistical analysis (NMR-MSA). The present work introduces a set of NMR-MSA novelties. First, the use of a double pulsed-field-gradient echo (DPFGE) experiment with a refocusing band-selective uniform response pure-phase selective pulse for the selective excitation of a 5–10-ppm range of wine samples reveals novel broad ¹H resonances. Second, an NMR-MSA foodomics approach to discriminate between wine samples produced from the same Cabernet Sauvignon variety fermented with different yeast strains proposed for large-scale alcohol reductions. Third a comparative study between a nonsupervised Principal Component Analysis (PCA), supervised standard partial (PLS-DA), and sparse (sPLS-DA) least squares discriminant analysis, as well as orthogonal projections to a latent structures discriminant analysis (OPLS-DA), for obtaining holistic fingerprints. The MSA discriminated between different Cabernet Sauvignon fermentation schemes and juice varieties (apple, apricot, and orange) or juice authentications (puree, nectar, concentrated, and commercial juice fruit drinks). The new pulse sequence DPFGE demonstrated an enhanced sensitivity in the aromatic zone of wine samples, allowing a better application of different unsupervised and supervised multivariate statistical analysis approaches.     

Trace Elements in Lycium barbarum L. Leaves by Inductively Coupled Plasma Mass Spectrometry after Microwave Assisted Digestion and Multivariate Analysis

The concentrations of 14 elements in Lycium barbarum L. leaves collected from the Qaidam basin (China) were determined by inductively coupled plasma mass spectrometry after microwave assisted digestion. This work presents two goals: (1) to determine 14 elements in L. barbarum leaves; (2) to examine the relationship between elements using correlation analysis, principal component analysis, and cluster analysis. The accuracy and precision were verified against a GBW07605 Tea Leaves certified reference material. The results demonstrated that the method was reliable, reproducible, and suitable for determination of the concentrations of trace elements in L. barbarum leaves. Correlation analysis showed that aluminum–copper, arsenic–zinc, manganese–selenium, and chromium–iron have medium correlation coefficients. Principal component loading for L. barbarum leaves extracted seven components explained about 85% of the total variance. Cluster analysis depicts four clusters: (1) arsenic and titanium; (2) calcium, magnesium, manganese, selenium, and zinc; (3) cobalt, iron, and molybdenum; (4) aluminum, copper, and chromium.     

Determination of Pesticide Residues in Fruit and Vegetables by High-Performance Liquid Chromatography–Tandem Mass Spectrometry with Multivariate Response Surface Methodology

Multivariate response surface methodology optimization using Placket–Burman and Box–Behnken designs were respectively used for the screening and optimization of significant factors for liquid chromatography–tandem mass spectrometry. Consequently, the optimized instrument successfully improved the sample preparation protocol and the method was validated. However, modified QuEChERS dispersive solid phase extraction coupled with ionic liquid-based dispersive liquid–liquid microextraction were used for the determination of multi-pesticide residues in fruit and vegetable samples. The analysed samples were jackfruit, strawberries, cucumber, pears, and carrots. The resulting linearity range (5–400?µg/kg) and regression coefficient (>0.99) results were satisfactory. The 94.2 and 95.8% accuracy (89–138%) and precision (0–25%) results were satisfactory and within the recommended ranges (≤20%) and (70–120%), respectively. The limits of detection (0.01–0.54?µg/kg) and quantitation (0.03–1.79?µg/kg) were excellent. The matrix effects (≤?87%) for all analysed samples were not significant. The estimated measurement uncertainties (≤27%) were within the acceptable range (≤50%). Justifiably, the response surface methodology optimized instrument and sample treatment techniques were reliable and convenient for multi-pesticide residue determination in various fruits and vegetables.     

Nanogram/mL detection of nortriptyline: Preparation of a molecularly imprinted polymer for spectrophotometric determination of nortriptyline based on multivariate optimization methods

In this work, the molecularly imprinted polymer was used as a selective sorbent in solid‐phase extraction method for the spectrophotometric determination of nortriptyline at 239 nm. Molecularly imprinted polymer was synthesized by pyrrole as a functional monomer in the presence of nortriptyline as a template. Several factors, consist of the concentration of the monomer to template ratio, amount of initiator, stirring rate, reaction time, the pH of the buffer solution, amount of sorbent, loading time, shaking rate of loading, extraction time, and shaking rate of extraction were evaluated due to their effectiveness in the preparation and extraction capability of molecularly imprinted polymer. Multivariate optimization methods, such as Plackett‐Burman and central composite designs, were employed to find and optimize the significant factors. Under the selected optimal conditions, molecularly imprinted polymer showed a linear range from 0.1 to 100 µmol/L (0.026 to 26 µg/mL) nortriptyline, a detection limit of 10.3 nmol/L (2.7 ng/mL), a highly repeatable (relative standard deviation of 3.7%) and reproducible response (relative standard deviation of 4.6%), and a good selectivity in the presence of structurally related molecules. Furthermore, molecularly imprinted polymer showed high extraction efficiency and was successfully used for the determination of nortriptyline in real samples.     

1H-NMR-Based Metabolomics: An Integrated Approach for the Detection of the Adulteration in Chicken,Chevon, Beef and Donkey Meat

Meat is a rich source of energy that provides high-value animal protein, fats, vitamins, minerals and trace amounts of carbohydrates. Globally, different types of meats are consumed to fulfill nutritional requirements. However, the increasing burden on the livestock industry has triggered the mixing of high-price meat species with low-quality/-price meat. This work aimed to differentiate different meat samples on the basis of metabolites. The metabolic difference between various meat samples was investigated through Nuclear Magnetic Resonance spectroscopy coupled with multivariate data analysis approaches like principal component analysis (PCA) and orthogonal partial least square-discriminant analysis (OPLS-DA). In total, 37 metabolites were identified in the gluteal muscle tissues of cow, goat, donkey and chicken using ¹H-NMR spectroscopy. PCA was found unable to completely differentiate between meat types, whereas OPLS-DA showed an apparent separation and successfully differentiated samples from all four types of meat. Lactate, creatine, choline, acetate, leucine, isoleucine, valine, formate, carnitine, glutamate, 3-hydroxybutyrate and α-mannose were found as the major discriminating metabolites between white (chicken) and red meat (chevon, beef and donkey). However, inosine, lactate, uracil, carnosine, format, pyruvate, carnitine, creatine and acetate were found responsible for differentiating chevon, beef and donkey meat. The relative quantification of differentiating metabolites was performed using one-way ANOVA and Tukey test. Our results showed that NMR-based metabolomics is a powerful tool for the identification of novel signatures (potential biomarkers) to characterize meats from different sources and could potentially be used for quality control purposes in order to differentiate different meat types.     

Some Useful Integral Representations for Information-Theoretic Analyses

This work is an extension of our earlier article, where a well-known integral representation of the logarithmic function was explored and was accompanied with demonstrations of its usefulness in obtaining compact, easily-calculable, exact formulas for quantities that involve expectations of the logarithm of a positive random variable. Here, in the same spirit, we derive an exact integral representation (in one or two dimensions) of the moment of a nonnegative random variable, or the sum of such independent random variables, where the moment order is a general positive non-integer real (also known as fractional moments). The proposed formula is applied to a variety of examples with an information-theoretic motivation, and it is shown how it facilitates their numerical evaluations. In particular, when applied to the calculation of a moment of the sum of a large number, n, of nonnegative random variables, it is clear that integration over one or two dimensions, as suggested by our proposed integral representation, is significantly easier than the alternative of integrating over n dimensions, as needed in the direct calculation of the desired moment.