Use of chemometrics to optimize a glucose assay on a paper microfluidic platform.

We describe the use of a chemometrics-based computational platform to optimize a glucose assay on a microfluidic paper-based analytical device (µPAD). Glucose is colorimetrically detected in the presence of glucose oxidase (GOx), horseradish peroxidase (HRP), and potassium iodide (KI). Using a Y-shaped paper microfluidic chip, the concentration of glucose, volume of reagents, and the length and width of the microfluidic channel were examined. The responses of the microfluidic chips were analyzed at the halfway point of the channel length. Variables affecting the response were screened by using a 24 factorial design, and among them, volume and concentration of the glucose were optimized by applying a rotatable central composite design (CCD). The optimum and experimental responses are 151.58 and 149.80, respectively, with an absolute error of 1.2%.

A simple graphical approach to predict local residue conformation using NMR chemical shifts and density functional theory.

The dependency of amino acid chemical shifts on φ and ψ torsion angle is, independently, studied using a five-residue fragment of ubiquitin and ONIOM(DFT:HF) approach. The variation of absolute deviation of (13) C(α) chemical shifts relative to φ dihedral angle is specifically dependent on secondary structure of protein not on amino acid type and fragment sequence. This dependency is observed neither on any of (13) C(ß) , and (1) H(α) chemical shifts nor on the variation of absolute deviation of (13) C(α) chemical shifts relative to ψ dihedral angle. The (13) C(α) absolute deviation chemical shifts (ADCC) plots are found as a suitable and simple tool to predict secondary structure of protein with no requirement of highly accurate calculations, priori knowledge of protein structure and structural refinement. Comparison of Full-DFT and ONIOM(DFT:HF) approaches illustrates that the trend of (13) C(α) ADCC plots are independent of computational method but not of basis set valence shell type.

An improved alkaline direct formate paper microfluidic fuel cell.

Paper-based microfluidic fuel cells (MFCs) are a potential replacement for traditional FCs and batteries due to their low cost, portability, and simplicity to operate. In MFCs, separate solutions of fuel and oxidant migrate through paper due to capillary action and laminar flow and, upon contact with each other and catalyst, produce electricity. In the present work, we describe an improved microfluidic paper-based direct formate FC (DFFC) employing formate and hydrogen peroxide as the anode fuel and cathode oxidant, respectively. The dimensions of the lateral column, current collectors, and cathode were optimized. A maximum power density of 2.53 mW/cm(2) was achieved with a DFFC of surface area 3.0 cm(2) , steel mesh as current collector, 5% carbon to paint mass ratio for cathode electrode and, 30% hydrogen peroxide. The longevity of the MFC's detailed herein is greater than eight hours with continuous flow of streams. In a series configuration, the MFCs generate sufficient energy to power light-emitting diodes and a handheld calculator.

How can chemometrics improve microfluidic research?

Chemometrics has the potential to embolden microfluidics to become that enabling technology for so long sought after. In this Feature article, we describe a historical perspective on microfluidics and its current challenges, a perspective on chemometric methods including response surface methodology (RSM), and how a combination of artificial neural network with experimental design (ANN-ED) have demonstrated promise in addressing basic microfluidic problems.

Experimental design in analytical chemistry--part II: applications.

This paper reviews the applications of experimental design to optimize some analytical chemistry techniques such as extraction, chromatography separation, capillary electrophoresis, spectroscopy, and electroanalytical methods.

Experimental design in analytical chemistry--part I: theory.

This paper reviews the main concepts of experimental design applicable to the optimization of analytical chemistry techniques. The critical steps and tools for screening, including Plackett-Burman, factorial and fractional factorial designs, and response surface methodology such as central composite, Box-Behnken, and Doehlert designs, are discussed. Some useful routines are also presented for performing the procedures.

Resolution and quantification of complex mixtures of polycyclic aromatic hydrocarbons in heavy fuel oil sample by means of GC × GC-TOFMS combined to multivariate curve resolution.

Comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC × GC-TOFMS) combined to multivariate curve resolution-alternating least-squares (MCR-ALS) is proposed for the resolution and quantification of very complex mixtures of compounds such as polycyclic aromatic hydrocarbons (PAHs) in heavy fuel oil (HFO). Different GC × GC-TOFMS data slices acquired during the analysis of HFO samples and PAH standards were simultaneously analyzed using the MCR-ALS method to resolve the pure component elution profiles in the two chromatographic dimensions as well as their pure mass spectra. Outstandingly, retention time shifts within and between GC × GC runs were not affecting the results obtained using the proposed strategy and proper resolution of strongly coeluted compounds, baseline and background contributions was achieved. Calibration curves built up with standard samples of PAHs allowed the quantification of ten of them in HFO aromatic fractions. Relative errors in their estimated concentrations were in all cases below 6%. The obtained results were compared to those obtained by commercial software provided with GC × GC-TOFMS instruments and to Parallel Factor Analysis (PARAFAC). Inspection of these results showed improvement in terms of data fitting, elution process description, concentration relative errors and relative standard deviations.

Modeling of retention behaviors of most frequent components of essential oils in polar and non-polar stationary phases.

The gas chromatography retention indices of 100 different components of essential oils, on three columns with stationary phases of different polarities, were used to develop robust quantitative structure-retention relationship (QSRR) models. Two linear models with only one variable, i.e. solvation entropy, were developed, which explain 95 and 94% of variances of the test set for dimethyl silicone and dimethyl silicone with 5% phenyl group columns, respectively. These models are extremely simple and easy to interpret, but they show higher errors compared with more robust models such as partial least square (PLS) and ridge regressions. For the third column (polyethylene glycol (PEG)), 24 hydrogen bonding descriptors were calculated and were used for modeling. Kernel orthogonal projection to latent structure (KOPLS), as a non-linear technique, was applied for the modeling of the retention indices of the compounds on the PEG column. R(2) values for the test set established by Monte Carlo cross-validation and SPXY (sample set partitioning based on joint x-y distances) test set of the KOPLS were 0.92 and 0.94, respectively. y-Randomization indicated that chance plays no role in constructing the KOPLS model.

Optimisation of a microwave-assisted method for extracting withaferin A from Withania somnifera Dunal. using central composite design.

INTRODUCTION: Recently, there have been growing attention on the modification and optimisation of new extraction and quantification methods, caused by the lack of environmentally friendly methodologies for the extraction of phytochemicals from complex matrices. In the case of pharmaceutical compounds, not only the extraction procedure but also the analysis method should be efficient, precise, fast and easy. OBJECTIVES: The essential pharmaceutical characteristics and trace concentration of withanolides led us to modify and optimise the previously reported extraction and quantification procedure for withaferin A (WA) as a candidate for withanolides. MATERIAL AND METHODS: The WA from the air-dried aerial part of Withania somnifera Dunal. was extracted using a microwave-assisted extraction (MAE) technique. Four variables affecting the extraction procedure were optimised using the central composite design approach. The method of high-performance thin-layer chromatography assay was validated and applied for the quantification of each experiment. RESULTS: The optimum values of factors were: extraction time (150 s), extraction temperature (68°C) and 17 mL of methanol : water in the ratio 25 : 75 as extracting solvent. The solvent system consisted of ethyl acetate : toluene : formic acid : 2-propanol (7.0 : 2.0 : 0.5 : 0.5, v/v/v/v), and densitometric scanning at 220 nm was applied for the analysis. The dynamic linear range, LOD, LOQ and recovery with the inter-day, and intra-day RSDs of the developed method indicated the validity of the method. CONCLUSION: A pressurised MAE method for extracting WA from the plant's aerial part was optimised using factorial-based design. The net effect of time, temperature, solvent volume and its ratio suggests that the yield of WA increases until each factor reaches its optimum value, and decreases with further increase in temperature or solvent ratio.

Neural networks in analytical chemistry.

This chapter covers a part of the spectrum of neural-network uses in analytical chemistry. Different architectures of neural networks are described briefly. The chapter focuses on the development of three-layer artificial neural network for modeling the anti-HIV activity of the HETP derivatives and activity parameters (pIC50) of heparanase inhibitors. The use of a genetic algorithm-kernel partial least squares algorithm combined with an artificial neural network (GA-KPLS-ANN) is described for predicting the activities of a series of aromatic sulfonamides. The retention behavior of terpenes and volatile organic compounds and predicting the response surface of different detection systems are presented as typical applications of ANNs in chromatographic area. The use of ANNs is explored in electrophoresis with emphasizes on its application on peptide mapping. Simulation of the electropherogram of glucagons and horse cytochrome C is described as peptide models. This chapter also focuses on discussing the role of ANNs in the simulation of mass and 13C-NMR spectra for noncyclic alkenes and alkanes and lignin and xanthones, respectively.

Comparative structure-toxicity relationship study of substituted benzenes to Tetrahymena pyriformis using shuffling-adaptive neuro fuzzy inference system and artificial neural networks.

The purpose of this study was to develop the structure-toxicity relationships for a large group of 268 substituted benzene to the ciliate Tetrahymena pyriformis using mechanistically interpretable descriptors. The shuffling-adaptive neuro fuzzy inference system (Shuffling-ANFIS) has been successfully applied to select the important factors affecting the toxicity of substituted benzenes to T. pyriformis. The results of the proposed model were compared with the model of linear-free energy response surface and also the principal component analysis Bayesian-regularized neural network (PCA-BRANN) trained using the same data. The presented model shows a better statistical parameter in comparison with the previous models. The results of the model are promising and descriptive. Five descriptors of octanol-water partition coefficient (logP), bond information content (BIC0), number of R-CX-R (C-026), eigenvalue sum from Z weighted distance matrix (SEigZ) and fragment based polar surface area (PSA) selected by Shuffling-ANFIS reveal the role of hydrophobicity, electronic and steric interactions in the mechanism of toxic action. Sequential zeroing of weights (SZW) as a sensitivity analysis method revealed that the hydrophobicity and electronic interactions play a major role in toxicity of these compounds. Satisfactory results (q(2)=0.828 and RMSE=0.348) in comparison with the previous works indicate that the Shuffling-ANFIS-ANN technique is able to model a diverse chemical class with more than one mechanism of toxicity by using simple and interpretable descriptors. Shuffling-ANFIS can be used as powerful feature selection technique, because its application in prediction of toxicity potency results in good statistical and interpretable physiochemical parameters.

Use of gas chromatography-mass spectrometry combined with resolution methods to characterize the essential oil components of Iranian cumin and caraway.

Gas chromatography-mass spectrometry combined with iterative and non-iterative resolution methods was used to characterize the essential oil components of Iranian cumin and caraway. Orthogonal projection resolution (OPR) as a non-iterative and distance-selection-multivariate curve resolution-alternative least squares (DS-MCR-ALS) as an iterative method were used as auxiliary means to the analysis in the case of overlapping peaks. A total of 19 and 39 components were identified by direct similarity searches for cumin and caraway oils, respectively. These numbers were extended to 49 and 98 components, respectively with the help of chemometric techniques. Major constituents in cumin are gamma-terpinene (15.82%), 2-methyl-3-phenyl-propanal (32.27%) and myrtenal (11.64%) and in caraway are gamma-terpinene (24.40%), 2-methyl-3-phenyl-propanal (13.20%) and 2, 4(10)-thujadien (14.02%). In spite of different cultivation conditions, there are 28 components which are common between the two seeds.

Application of genetic algorithm-kernel partial least square as a novel nonlinear feature selection method: activity of carbonic anhydrase II inhibitors.

This paper introduces the genetic algorithm-kernel partial least square (GA-KPLS), as a novel nonlinear feature selection method. This technique combines genetic algorithms (GAs) as powerful optimization methods with KPLS as a robust nonlinear statistical method for variable selection. This feature selection method is combined with artificial neural network to develop a nonlinear QSAR model for predicting activities of a series of substituted aromatic sulfonamides as carbonic anhydrase II (CA II) inhibitors. Eight simple one- and two-dimensional descriptors were selected by GA-KPLS and considered as inputs for developing artificial neural networks (ANNs). These parameters represent the role of acceptor-donor pair, hydrogen bonding, hydrosolubility and lipophilicity of the active sites and also the size of the inhibitors on inhibitor-isozyme interaction. The accuracy of 8-4-1 networks was illustrated by validation techniques of leave-one-out (LOO) and leave-multiple-out (LMO) cross-validations and Y-randomization. Superiority of this method (GA-KPLS-ANN) over the linear one (MLR) in a previous work and also the GA-PLS-ANN in which a linear feature selection method has been used indicates that the GA-KPLS approach is a powerful method for the variable selection in nonlinear systems.

Characterization of essential oil components of Iranian geranium oil using gas chromatography-mass spectrometry combined with chemometric resolution techniques.

The essential oil components of geranium oil cultivated in center of Iran were identified and determined using gas chromatography-mass spectrometry data combined with the chemometric resolution techniques. A total of 61 components accounting for 91.51% were identified using similarity searches between the mass spectra and MS database. This number was extended to 85 components using chemometric techniques. Various chemometric methods such as morphological scores, simplified Borgen method (SBM) and fixed size moving window evolving factor analysis (FSMWEFA) were used for determining the number of components, pure variables, zero concentration and selective regions. Then the overlapping peak clusters were resolved into pure chromatograms and pure mass spectra using heuristic evolving latent projections (HELP) method. A characteristic feature of the Iranian geranium oil is the absence of 10-epi-gamma-eudesmol in its constituents compared with the oil from northern and southern parts of India. The results of this work show that combination of hyphenated chromatographic methods and resolution techniques provide a complementary method for accurate analysis of essential oils.

Artificial neural network modeling of peptide mobility and peptide mapping in capillary zone electrophoresis.

Recently, we have developed an artificial neural network model, which was able to predict accurately the electrophoretic mobilities of relatively small peptides. To examine the robustness of this methodology, a 3-3-1 back-propagation artificial neural network (BP-ANN) model was developed using the same inputs as the previous model, which were the Offord's charge over mass term (Q/M(2/3)), corrected steric substituent constant (E(s,c)) and molar refractivity (MR). The data set consisted of 102 peptides with a larger range of size than that of our earlier report - up to 42 amino acid residues as compared to 13 amino acids in the initial study - that also included highly charged and hydrophobic peptides. The entire data set was obtained from the published result by Janini and co-workers. The results of this model are compared with those obtained using multiple linear regressions (MLR) model developed in this work and the multi-variable model released by Janini et al. Better predictive ability of the BP-ANN model over the MLR indicates the non-linear characteristics of the electrophoretic mobility of peptides. The present model exhibits better robustness than the MLR models in predicting CZE mobilities of a diverse data set at different experimental conditions. To explore the utility of the ANN model in simulation of the CZE peptide maps, the profiles for the endoproteinase digests of melittin, glucagon and horse cytochrome C is studied in the present work.

CS-MINER: A Tool for Association Mining in Binding-Database.

This paper introduces the algorithms, implementation strategies, features, and applications of CS-MINER, a tool for visualization and analysis of drug-like chemical space. The CS-MINER is the abstract abbreviation for Chemical Space Miner and correlates the medicinal target space and chemical space, in a systematic way. The database in this software consists of a large collection of drug-like molecules. To prepare this database, a large number of molecules for 110 important biological targets were collected from Binding-DB. A total of 1497 physicochemical properties were calculated for each molecule. The CS-MINER uses the discriminant analysis techniques for tracing the collected data and finally separates the molecules based on their therapeutic targets and activities. The developed multivariate classifiers can be used for ligand-based virtual screening of more than 0.5 million random molecules of PubChem and ZINC databases. In order to validate the models, selected subspaces in CS-MINER were compared with DrugBank molecules. At the end of the analysis, the software provides an interactive environment for visualization of the selected chemical subspaces in the form of 2- and 3-dimensional plots. In general, CS-MINER is a tool for comparing the relative position of active biosimilar molecules in chemical space and is freely available at www.csminer.com.

Elimination of chromatographic and mass spectrometric problems in GC-MS analysis of Lavender essential oil by multivariate curve resolution techniques: Improving the peak purity assessment by variable size moving window-evolving factor analysis.

In analysis of complex natural matrices by gas chromatography-mass spectrometry (GC-MS), many disturbing factors such as baseline drift, spectral background, homoscedastic and heteroscedastic noise, peak shape deformation (non-Gaussian peaks), low S/N ratio and co-elution (overlapped and/or embedded peaks) lead the researchers to handle them to serve time, money and experimental efforts. This study aimed to improve the GC-MS analysis of complex natural matrices utilizing multivariate curve resolution (MCR) methods. In addition, to assess the peak purity of the two-dimensional data, a method called variable size moving window-evolving factor analysis (VSMW-EFA) is introduced and examined. The proposed methodology was applied to the GC-MS analysis of Iranian Lavender essential oil, which resulted in extending the number of identified constituents from 56 to 143 components. It was found that the most abundant constituents of the Iranian Lavender essential oil are α-pinene (16.51%), camphor (10.20%), 1,8-cineole (9.50%), bornyl acetate (8.11%) and camphene (6.50%). This indicates that the Iranian type Lavender contains a relatively high percentage of α-pinene. Comparison of different types of Lavender essential oils showed the composition similarity between Iranian and Italian (Sardinia Island) Lavenders.

Draize rabbit eye test compatibility with eye irritation thresholds in humans: a quantitative structure-activity relationship analysis.

Draize rabbit eye test scores, as modified maximum average score (MMAS), for 68 pure bulk liquids were adjusted by the liquid-saturated vapor pressure P. These 68 adjusted scores, as log (MMAS/P), were shown to be completely equivalent to eye irritation thresholds (EIT), expressed as log (1/EIT), for 23 compounds in humans. Thus, for the first time the Draize eye test in rabbits for pure bulk liquids is shown to be perfectly compatible with eye irritation thresholds in humans. The total data set for 91 compounds was analyzed by the general solvation equation of Abraham. Values of log (MMAS/P) or log (1/EIT) could be fitted to a five-parameter equation with R2 = 0.936, SD = 0.433, AD = 0.000, and AAD = 0.340 over a range of 9.6 log units. When divided into a training set of 45 compounds, the corresponding equation could be used to predict the remaining 46 compounds in a test set with AD = -0.037 and AAD = 0.345 log units. Thus, the 91-compound equation can now be used to predict further EIT values to around 0.4 log units. It is suggested that the mechanism of action in the Draize test and in the human EIT involves passive transfer of the compound to a biophase that is quite polar, is a strong hydrogen bond base, a moderate hydrogen bond acid, and quite hydrophobic. The biophase does not resemble water or plasma, but resembles an organic solvent such as N-methylformamide.

Characterization and determination of fatty acids in fish oil using gas chromatography-mass spectrometry coupled with chemometric resolution techniques.

Characterization and determination of a complex mixture of fatty acid methyl esters was performed for commercial fish oil using two-dimensional GC-MS data coupled with resolution techniques. Various principle component analysis methods such as significant factor analysis and fixed size moving window evolving factor analysis were used for the number of factors, zero concentration and selective regions. Then, the convoluted chromatograms were resolved into pure chromatograms and mass spectra using heuristic evolving latent projections (HELP) method. Fatty acids of C16:1omega7, C18:4omega3, C18:1omega11, C18:1omega9, C18:0, C20:2omega6, C20:1omega9, C22:1omega11, C22:1omega9 and C24:1omega9 were resolved an fied by using similarity searches between deconvoluted mass spectra and MS database, in different parts of total ion current chromatogram. Window target testing factor analysis is also applied for confirming the presence or absence of target analytes. The results of the present work show that combination of hyphenated chromatographic methods and resolution techniques provide a complementary method for accurate analysis of real multi-component systems such as fish oil.

Simultaneous determination of theophylline and caffeine by proton magnetic resonance spectroscopy using partial least squares regression techniques.

A 1H-NMR procedure based on an analysis of its data by a multivariate calibration method was conducted for the simultaneous determination of theophylline and caffeine in synthetic and real samples. Partial least squares regression (PLS) was chosen as the calibration method. The methyl signals of theophilline at 3.36 and 3.54 ppm that overlapped with those of caffeine were significant characteristics which were employed in this study for their analyses. The proposed method was successfully applied to recovery studies of theophylline and caffeine from real tablet samples.