Flow-injection and sequential-injection determinations of paracetamol in pharmaceutical preparations using nitrosation reaction.

A simple FI and two different SI systems have been investigated for the determination of paracetamol by employing a simple reagent for a nitrosation reaction. It is based on the on-line nitrosation of paracetamol with sodium nitrite in an acidic medium. The formed nitroso derivative species reacts further with sodium hydroxide to convert it to a more stable compound. The yellow product is continuously monitored at 430 nm. The FI system is very simple and cost effective for fast manual operation (60 injections/h; y = 0.268x + 44.314, r2 = 0.9910 for 400 - 1000 mg/l and y = 0.1687x + 145.72, r2 = 0.9970 for 1000 - 2500 mg/l). The two SI systems with different components and configurations are automated and optimized for the conditions for which no extra dilution is to be required for sample handling: one with a syringe pump and two selection valves (60 samples/h; y = 0.1488x - 4.7297, r2 = 0.9946 for 400 - 1000 mg/l and y = 0.0858x + 63.933, r2 = 0.9849 for 1000 - 2500 mg/l); the other is simpler and more cost-effective, with an autoburette and only one selection valve (15 samples/h; y = 0.0072x + 1.1467, r2 = 0.9977 for 200 - 1000 mg/l and y = 0.0028x + 5.4699, r2 = 0.9879 for 1000 - 2500 mg/l). They have all been applied to assay paracetamol in pharmaceutical preparations. The obtained results agree with those by the US Pharmacopeia method.     

The comparison of two column classification systems during the chromatographic analysis of steroids

Nowadays, due to the availability of hundreds of brands of reversed-phase liquid chromatographic columns, the selection of suitable columns can be difficult. Therefore, a good characterization and classification system is very important. Among published papers, the classification system based on quantitative structure-retention relationships and a method developed at the Katholieke Universiteit Leuven also exist. In quantitative structure-retention relationships, retention is evaluated in terms of the chemical structure of the analytes and the physicochemical properties of both the stationary and mobile phase. The second system allows to rank columns due to the values of four parameters and the calculation of specific F(KUL)-values for a reference column and to be compared with others. In this paper, the classification systems based both on quantitative structure-retention relationships and the F(KUL)-values using principal components analysis were compared. Moreover, the proposed column ranking systems have been checked in clinical practice case considering liquid chromatography determination of six steroid hormones in urine samples. Despite that the matching of both methods is not exactly the same, both classification systems provide simple, reliable and comparable results.     

Canonical Measure of Correlation (CMC) and Canonical Measure of Distance (CMD) between sets of data. Part 3. Variable selection in classification

In multivariate regression and classification issues variable selection is an important procedure used to select an optimal subset of variables with the aim of producing more parsimonious and eventually more predictive models. Variable selection is often necessary when dealing with methodologies that produce thousands of variables, such as Quantitative Structure-Activity Relationships (QSARs) and highly dimensional analytical procedures.In this paper a novel method for variable selection for classification purposes is introduced. This method exploits the recently proposed Canonical Measure of Correlation between two sets of variables (CMC index). The CMC index is in this case calculated for two specific sets of variables, the former being comprised of the independent variables and the latter of the unfolded class matrix. The CMC values, calculated by considering one variable at a time, can be sorted and a ranking of the variables on the basis of their class discrimination capabilities results. Alternatively, CMC index can be calculated for all the possible combinations of variables and the variable subset with the maximal CMC can be selected, but this procedure is computationally more demanding and classification performance of the selected subset is not always the best one.The effectiveness of the CMC index in selecting variables with discriminative ability was compared with that of other well-known strategies for variable selection, such as the Wilks’ Lambda, the VIP index based on the Partial Least Squares-Discriminant Analysis, and the selection provided by classification trees.A variable Forward Selection based on the CMC index was finally used in conjunction of Linear Discriminant Analysis. This approach was tested on several chemical data sets. Obtained results were encouraging.     

Hybrid particle swarm optimization and tabu search approach for selecting genes for tumor classification using gene expression data

Gene expression data are characterized by thousands even tens of thousands of measured genes on only a few tissue samples. This can lead either to possible overfitting and dimensional curse or even to a complete failure in analysis of microarray data. Gene selection is an important component for gene expression-based tumor classification systems. In this paper, we develop a hybrid particle swarm optimization (PSO) and tabu search (HPSOTS) approach for gene selection for tumor classification. The incorporation of tabu search (TS) as a local improvement procedure enables the algorithm HPSOTS to overleap local optima and show satisfactory performance. The proposed approach is applied to three different microarray data sets. Moreover, we compare the performance of HPSOTS on these datasets to that of stepwise selection, the pure TS and PSO algorithm. It has been demonstrated that the HPSOTS is a useful tool for gene selection and mining high dimension data.     

A new and efficient variable selection algorithm based on ant colony optimization. Applications to near infrared spectroscopy/partial least-squares analysis

A new variable selection algorithm is described, based on ant colony optimization (ACO). The algorithm aim is to choose, from a large number of available spectral wavelengths, those relevant to the estimation of analyte concentrations or sample properties when spectroscopic analysis is combined with multivariate calibration techniques such as partial least-squares (PLS) regression. The new algorithm employs the concept of cooperative pheromone accumulation, which is typical of ACO selection methods, and optimizes PLS models using a pre-defined number of variables, employing a Monte Carlo approach to discard irrelevant sensors. The performance has been tested on a simulated system, where it shows a significant superiority over other commonly employed selection methods, such as genetic algorithms. Several near infrared spectroscopic experimental data sets have been subjected to the present ACO algorithm, with PLS leading to improved analytical figures of merit upon wavelength selection. The method could be helpful in other chemometric activities such as classification or quantitative structure-activity relationship (QSAR) problems.     

Quasiliving Carbocationic Polymerization. I. Classification of Living Polymerizations in Carbocationic Systems

It has been shown that in addition to classical living polymerizations, several other polymerization systems exist that may exhibit partially living so-called quasiliving character. The single requirement for quasiliving polymerization is the absence of irreversible termination. The various possible living systems have been classified by taking into consideration the absence or reversibility of termination and the absence, reversibility, or irreversibility of chain transfer. In regard to chain transfer, both unimolecular and/or bimolecular processes have been considered. A comprehensive examination of all possibilities yielded, in addition to the classical terminationless-transferless living system, five quasiliving systems. Kinetic analysis led to equations defining these systems and to diagnostic techniques useful for the classification and characterization of the mechanism of living carbocationic polymerizations.     

Sparse optimal scoring for multiclass cancer diagnosis and biomarker detection using microarray data

Gene expression data sets hold the promise to provide cancer diagnosis on the molecular level. However, using all the gene profiles for diagnosis may be suboptimal. Detection of the molecular signatures not only reduces the number of genes needed for discrimination purposes, but may elucidate the roles they play in the biological processes. Therefore, a central part of diagnosis is to detect a small set of tumor biomarkers which can be used for accurate multiclass cancer classification. This task calls for effective multiclass classifiers with built-in biomarker selection mechanism. We propose the sparse optimal scoring (SOS) method for multiclass cancer characterization. SOS is a simple prototype classifier based on linear discriminant analysis, in which predictive biomarkers can be automatically determined together with accurate classification. Thus, SOS differentiates itself from many other commonly used classifiers, where gene preselection must be applied before classification. We obtain satisfactory performance while applying SOS to several public data sets.     

Optimization of preparative chromatographic separation of multiple rare earth elements

This work presents a method to optimize multi-product chromatographic systems with multiple objective functions. The system studied is a neodymium, samarium, europium, gadolinium mixture separated in an ion exchange chromatography step. A homogeneous Langmuir Mobile Phase Modified model is calibrated to fit the experiments, and then used to perform the optimization task. For the optimization a multi-objective Differential Evolution algorithm was used, with weighting based on relative value of the components to find optimal operation points along the Pareto front. The objectives of the Pareto front are weighted productivity and weighted yield with purity as an equality constraint. A prioritizing scheme based on relative values is applied for determining the pooling order. A simple rule of thumb for pooling strategy selection is presented. The multi-objective optimization gives a Pareto front which shows the rule of thumb, as a gap in one of the objective functions.     

A new computational method for estimating X-ray spectral distributions

A new computational method is described for estimating the exposure-rate spectral distributions of X-rays from attenuation data measured with various filtrations. The estimation problem of X-ray spectra is formulated as the numerical computation of solving a set of linear equation with an ill-conditional nature. In this paper, the singular-value decomposition technique, which differs from the iterative method, is applied to this singular numerical computation problem. The principle of the analysis method is based on that the response matrix of filtrations can be decomposed into some inherent component matrices. X-ray spectral distributions are then represented in a simple combination of some component curves, so that the estimation process can be systematically constructed. The singularity in its computation is removed by selecting the components of the combination, and a performance index is also presented for the optimal selection. The feasibility of the proposed method is studied in detail in a computer simulation using a hypothetical X-ray spectrum produced by assuming experimental conditions. The application results are also shown about the spectral distribution from a 140 kV constant voltage X-ray source.     

Classification of gasoline data obtained by gas chromatography using a piecewise alignment algorithm combined with feature selection and principal component analysis

A fast and objective chemometric classification method is developed and applied to the analysis of gas chromatography (GC) data from five commercial gasoline samples. The gasoline samples serve as model mixtures, whereas the focus is on the development and demonstration of the classification method. The method is based on objective retention time alignment (referred to as piecewise alignment) coupled with analysis of variance (ANOVA) feature selection prior to classification by principal component analysis (PCA) using optimal parameters. The degree-of-class-separation is used as a metric to objectively optimize the alignment and feature selection parameters using a suitable training set thereby reducing user subjectivity, as well as to indicate the success of the PCA clustering and classification. The degree-of-class-separation is calculated using Euclidean distances between the PCA scores of a subset of the replicate runs from two of the five fuel types, i.e., the training set. The unaligned training set that was directly submitted to PCA had a low degree-of-class-separation (0.4), and the PCA scores plot for the raw training set combined with the raw test set failed to correctly cluster the five sample types. After submitting the training set to piecewise alignment, the degree-of-class-separation increased (1.2), but when the same alignment parameters were applied to the training set combined with the test set, the scores plot clustering still did not yield five distinct groups. Applying feature selection to the unaligned training set increased the degree-of-class-separation (4.8), but chemical variations were still obscured by retention time variation and when the same feature selection conditions were used for the training set combined with the test set, only one of the five fuels was clustered correctly. However, piecewise alignment coupled with feature selection yielded a reasonably optimal degree-of-class-separation for the training set (9.2), and when the same alignment and ANOVA parameters were applied to the training set combined with the test set, the PCA scores plot correctly classified the gasoline fingerprints into five distinct clusters.     

Untargeted Identification of Black Rice by Near-Infrared Spectroscopy and One-Class Models

Black rice is one of the famous rare rice varieties in China. It is common to sell inferior black rice intentionally declared as famous brands due to economical motivation. There is an urgent need to develop an analytical method for untargeted identification of black rice. The present work focuses on exploring the feasibility of the untargeted identification of black rice by the combination of near-infrared (NIR) spectroscopy and data driven-based class modeling and variable selection. A total of 142 samples of three brands were collected and used for measurements. The samples of a specific class were used as the target class. Principal component analysis was applied for the preliminary analysis. The model-independent variable selection method, i.e., joint mutual information, was used for spectral compression. Only the 10 most informative variables were picked from original variables based on which an optimal class-model for the target class was constructed and validated by means of an external test set. As a result, the model achieved 100% of sensitivity and specificity. It can be concluded that NIR spectroscopy combined with one-class modeling is a feasible tool for the untargeted identification of black rice.     

Quantum molecular similarity. 3. QTMS descriptors.

Building on the ideas of a previous paper [part 1, J. Phys. Chem. A 1999, 103, 2883] we present a new molecular similarity method based on the topology of the electron density. This method is directly applicable to QSARs and is called quantum topological molecular similarity (QTMS). It has been tested for five sets of carboxylic systems including para- and meta-benzoic acid, para-phenylacetic acid, 4-X-bicyclo[2.2.2]octane-1-carboxylic acids, and polysubstituted benzoic acids. In combination with the partial least squares (PLS) procedure QTMS is able to produce excellent and statistically valid regressions. It is shown that QTMS avoids certain challenges of traditional Carbó-like similarity indices. Finally, QTMS is able to suggest a molecular fragment that contains the active center or the part of the molecule that is responsible for the QSAR.     

iT4SE-EP: Accurate Identification of Bacterial Type IV Secreted Effectors by Exploring Evolutionary Features from Two PSI-BLAST Profiles

Many gram-negative bacteria use type IV secretion systems to deliver effector molecules to a wide range of target cells. These substrate proteins, which are called type IV secreted effectors (T4SE), manipulate host cell processes during infection, often resulting in severe diseases or even death of the host. Therefore, identification of putative T4SEs has become a very active research topic in bioinformatics due to its vital roles in understanding host-pathogen interactions. PSI-BLAST profiles have been experimentally validated to provide important and discriminatory evolutionary information for various protein classification tasks. In the present study, an accurate computational predictor termed iT4SE-EP was developed for identifying T4SEs by extracting evolutionary features from the position-specific scoring matrix and the position-specific frequency matrix profiles. First, four types of encoding strategies were designed to transform protein sequences into fixed-length feature vectors based on the two profiles. Then, the feature selection technique based on the random forest algorithm was utilized to reduce redundant or irrelevant features without much loss of information. Finally, the optimal features were input into a support vector machine classifier to carry out the prediction of T4SEs. Our experimental results demonstrated that iT4SE-EP outperformed most of existing methods based on the independent dataset test.     

Information-theoretical feature selection using data obtained by scanning electron microscopy coupled with and energy dispersive X-ray spectrometer for the classification of glass traces

In this work, a selection of the best features for multivariate forensic glass classification using Scanning Electron Microscopy coupled with an Energy Dispersive X-ray spectrometer (SEM-EDX) has been performed. This has been motivated by the fact that the databases available for forensic glass classification are sparse nowadays, and the acquisition of SEM-EDX data is both costly and time-consuming for forensic laboratories. The database used for this work consists of 278 glass objects for which 7 variables, based on their elemental compositions obtained with SEM-EDX, are available. Two categories are considered for the classification task, namely containers and car/building windows, both of them typical in forensic casework. A multivariate model is proposed for the computation of the likelihood ratios. The feature selection process is carried out by means of an exhaustive search, with an Empirical Cross-Entropy (ECE) objective function. The ECE metric takes into account not only the discriminating power of the model in use, but also its calibration, which indicates whether or not the likelihood ratios are interpretable in a probabilistic way. Thus, the proposed model is applied to all the 63 possible univariate, bivariate and trivariate combinations taken from the 7 variables in the database, and its performance is ranked by its ECE. Results show remarkable accuracy of the best variables selected following the proposed procedure for the task of classifying glass fragments into windows (from cars or buildings) or containers, obtaining high (almost perfect) discriminating power and good calibration. This allows the proposed models to be used in casework. We also present an in-depth analysis which reveals the benefits of the proposed ECE metric as an assessment tool for classification models based on likelihood ratios.     

基于随机森林结合博弈论的特征选择算法在近红外光谱分类中的应用研究

针对近红外光谱中的噪声和冗余信息导致分类模型识别率低的问题,提出了随机森林结合博弈论的特征选择算法。该算法首先根据随机森林对特征重要性进行度量,优选出对分类具有一定相关性的特征;然后利用改进的夏普利值结合互信息计算优选特征的权重,从加权后的特征集合中去掉冗余得到最优特征子集。为了验证算法的有效性,将其应用于烟叶产地识别模型,实验结果表明,该文所提出的特征选择算法对烟叶产地识别效果较好,分类识别率可达95.88%。