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.

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.

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.

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.

The use of multivariate curve resolution methods to improve the analysis of muramic acid as bacterial marker using gas chromatography-mass spectrometry: an alternative method to gas chromatography-tandem mass spectrometry.

In analysis of muramic acid (MA) as bacterial marker, two dominant disturbing factors lead the researchers to use gas chromatography-tandem mass spectrometry (GC-MS/MS) technique instead of gas chromatography-mass spectrometry (GC-MS). These factors are the trace concentration of MA and fundamental disturbance of base line mass channels in GC-MS technique. This study aimed to utilize multivariate curve resolution (MCR) methods combined with GC-MS to improve the analysis of MA. First, the background and noise in GC-MS analysis were corrected and reduced using MCR methods. In addition, the MA overlapped peaks were resolved to its pure chromatographic and mass spectral profiles. Then the two-way response of each component was reconstructed by the outer product of the pure chromatographic and mass spectral profiles. The overall volume integration (OVI) method was used for quantitative determination. The MA peak area was decreased dramatically after the background correction and noise reduction. The findings severely ratify the appropriateness of using MCR techniques combined with GC-MS analysis as a simple, fast and inexpensive method for the analysis of MA in complex mixtures. The proposed method may be considered as an alternative method to GC-MS/MS for thorough analysis of the bacterial marker.

Thorough tuning of the aspect ratio of gold nanorods using response surface methodology.

In the present work a central composite design based on response surface methodology (RSM) is employed for fine tuning of the aspect ratios of seed-mediated synthesized gold nanorods (GNRs). The relations between the affecting parameters, including ratio of l-ascorbic acid to Au(3+) ions, concentrations of silver nitrate, CTAB, and CTAB-capped gold seeds, were explored using a RSM model. It is observed that the effect of each parameter on the aspect ratio of developing nanorods highly depends on the value of the other parameters. The concentrations of silver ions, ascorbic acid and seeds are found to have a high contribution in controlling the aspect ratios of NRs. The optimized parameters led to a high yield synthesis of gold nanorods with an ideal aspect ratio ranging from 1 (spherical particle) to 4.9. In addition, corresponding tunable surface Plasmon absorption band has been extended to 880 nm. The resulted nanorods were characterized by UV-visible spectrometry and transmission electron microscopy.

Multivariate curve resolution-particle swarm optimization: a high-throughput approach to exploit pure information from multi-component hyphenated chromatographic signals.

Multivariate curve resolution-particle swarm optimization (MCR-PSO) algorithm is proposed to exploit pure chromatographic and spectroscopic information from multi-component hyphenated chromatographic signals. This new MCR method is based on rotation of mathematically unique PCA solutions into the chemically meaningful MCR solutions. To obtain a proper rotation matrix, an objective function based on non-fulfillment of constraints is defined and is optimized using particle swarm optimization (PSO) algorithm. Initial values of rotation matrix are calculated using local rank analysis and heuristic evolving latent projection (HELP) method. The ability of MCR-PSO in resolving the chromatographic data is evaluated using simulated gas chromatography-mass spectrometry (GC-MS) and high-performance liquid chromatography-diode array detection (HPLC-DAD) data. To present a comprehensive study, different number of components and various levels of noise under proper constraints of non-negativity, unimodality and spectral normalization are considered. Calculation of the extent of rotational ambiguity in MCR solutions for different chromatographic systems using MCR-BANDS method showed that MCR-PSO solutions are always in the range of feasible solutions like true solutions. In addition, the performance of MCR-PSO is compared with other popular MCR methods of multivariate curve resolution-objective function minimization (MCR-FMIN) and multivariate curve resolution-alternating least squares (MCR-ALS). The results showed that MCR-PSO solutions are rather similar or better (in some cases) than other MCR methods in terms of statistical parameters. Finally MCR-PSO is successfully applied in the resolution of real GC-MS data. It should be pointed out that in addition to multivariate resolution of hyphenated chromatographic signals, MCR-PSO algorithm can be straightforwardly applied to other types of separation, spectroscopic and electrochemical data.

Determination of volatile components of green, black, oolong and white tea by optimized ultrasound-assisted extraction-dispersive liquid-liquid microextraction coupled with gas chromatography.

Ultrasound assisted extraction (UAE) followed by dispersive liquid-liquid microextraction (DLLME) was used for extraction and preconcentration of volatile constituents of six tea plants. The preconcentrated compounds were analyzed by gas chromatography-mass spectrometry (GC-MS). Totally, 42 compounds were identified and caffeine was quantitatively determined. The main parameters (factors) of the extraction process were optimized by using a central composite design (CCD). Methanol and chloroform were selected as the extraction solvent and preconcentration solvent, respectively .The optimal conditions were obtained as 21 in for sonication time; 32°C for temperature; 27 L for volume of extraction solvent and 7.4% for salt concentration (NaCl/H(2)O). The determination coefficient (R(2)) was 0.9988. The relative standard deviation (RSD %) was 4.8 (n=5), and the enhancement factors (EFs) were 4.0-42.6.

Integrated One-Against-One Classifiers as Tools for Virtual Screening of Compound Databases: A Case Study with CNS Inhibitors.

A total of 21 833 inhibitors of the central nervous system (CNS) were collected from Binding-database and analyzed using discriminant analysis (DA) techniques. A combination of genetic algorithm and quadratic discriminant analysis (GA-QDA) was proposed as a tool for the classification of molecules based on their therapeutic targets and activities. The results indicated that the one-against-one (OAO) QDA classifiers correctly separate the molecules based on their therapeutic targets and are comparable with support vector machines. These classifiers help in charting the chemical space of the CNS inhibitors and finding specific subspaces occupied by particular classes of molecules. As a next step, the classification models were used as virtual filters for screening of random subsets of PUBCHEM and ZINC databases. The calculated enrichment factors together with the area under curve values of receiver operating characteristic curves showed that these classifiers are good candidates to speed up the early stages of drug discovery projects. The "relative distances" of the center of active classes of biosimilar molecules calculated by OAO classifiers were used as indices for sorting the compound databases. The results revealed that, the multiclass classification models in this work circumvent the definition inactive sets for virtual screening and are useful for compound retrieval analysis in Chemoinformatics.

Detection of addition of barley to coffee using near infrared spectroscopy and chemometric techniques.

The current study presents an application of near infrared spectroscopy for identification and quantification of the fraudulent addition of barley in roasted and ground coffee samples. Nine different types of coffee including pure Arabica, Robusta and mixtures of them at different roasting degrees were blended with four types of barley. The blending degrees were between 2 and 20 wt% of barley. D-optimal design was applied to select 100 and 30 experiments to be used as calibration and test set, respectively. Partial least squares regression (PLS) was employed to build the models aimed at predicting the amounts of barley in coffee samples. In order to obtain simplified models, taking into account only informative regions of the spectral profiles, a genetic algorithm (GA) was applied. A completely independent external set was also used to test the model performances. The models showed excellent predictive ability with root mean square errors (RMSE) for the test and external set equal to 1.4% w/w and 0.8% w/w, respectively.

Chromatographic fingerprint analysis of secondary metabolites in citrus fruits peels using gas chromatography-mass spectrometry combined with advanced chemometric methods.

Multivariate curve resolution (MCR) and multivariate clustering methods along with other chemometric methods are proposed to improve the analysis of gas chromatography-mass spectrometry (GC-MS) fingerprints of secondary metabolites in citrus fruits peels. In this way, chromatographic problems such as baseline/background contribution, low S/N peaks, asymmetric peaks, retention time shifts, and co-elution (overlapped and embedded peaks) occurred during GC-MS analysis of chromatographic fingerprints are solved using the proposed strategy. In this study, first, informative GC-MS fingerprints of citrus secondary metabolites are generated and then, whole data sets are segmented to some chromatographic regions. Each chromatographic segment for eighteen samples is column-wise augmented with m/z values as common mode to preserve bilinear model assumption needed for MCR analysis. Extended multivariate curve resolution alternating least squares (MCR-ALS) is used to obtain pure elution and mass spectral profiles for the components present in each chromatographic segment as well as their relative concentrations. After finding the best MCR-ALS model, the relative concentrations for resolved components are examined using principal component analysis (PCA) and k-nearest neighbor (KNN) clustering methods to explore similarities and dissimilarities among different citrus samples according to their secondary metabolites. In general, four clear-cut clusters are determined and the chemical markers (chemotypes) responsible to this differentiation are characterized by subsequent discriminate analysis using counter-propagation artificial neural network (CPANN) method. It is concluded that the use of proposed strategy is a more reliable and faster way for the analysis of large data sets like chromatographic fingerprints of natural products compared to conventional methods.

Navigating Drug-Like Chemical Space of Anticancer Molecules Using Genetic Algorithms and Counterpropagation Artificial Neural Networks.

A total of 6289 drug-like anticancer molecules were collected from Binding database and were analyzed by using the classification techniques. The collected molecules were encoded to a diverse set of descriptors, spanning different physical and chemical properties of the molecules. A combination of genetic algorithms and counterpropagation artificial neural networks was used for navigating the generated drug-like chemical space and selecting the most relevant molecular descriptors. The proposed method was used for the classification of the molecules according to their therapeutic targets and activities. The selected molecular descriptors in this work define discrete areas in chemical space, which are mainly occupied by particular classes of anticancer molecules. The obtained structure-activity relationship (SAR) patterns and classification rules contain valuable information, which help to screen the large databases of compounds, more precisely. Such rules and patterns can be considered as virtual filters for mining the large databases of compounds and are useful in finding new anticancer candidates.

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.

Recent trends in application of multivariate curve resolution approaches for improving gas chromatography-mass spectrometry analysis of essential oils.

Essential oils (EOs) are valuable natural products that are popular nowadays in the world due to their effects on the health conditions of human beings and their role in preventing and curing diseases. In addition, EOs have a broad range of applications in foods, perfumes, cosmetics and human nutrition. Among different techniques for analysis of EOs, gas chromatography-mass spectrometry (GC-MS) is the most important one in recent years. However, there are some fundamental problems in GC-MS analysis including baseline drift, spectral background, noise, low S/N (signal to noise) ratio, changes in the peak shapes and co-elution. Multivariate curve resolution (MCR) approaches cope with ongoing challenges and are able to handle these problems. This review focuses on the application of MCR techniques for improving GC-MS analysis of EOs published between January 2000 and December 2010. In the first part, the importance of EOs in human life and their relevance in analytical chemistry is discussed. In the second part, an insight into some basics needed to understand prospects and limitations of the MCR techniques are given. In the third part, the significance of the combination of the MCR approaches with GC-MS analysis of EOs is highlighted. Furthermore, the commonly used algorithms for preprocessing, chemical rank determination, local rank analysis and multivariate resolution in the field of EOs analysis are reviewed.

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.

Analysis of Iranian rosemary essential oil: application of gas chromatography-mass spectrometry combined with chemometrics.

This paper focuses on characterization of the components of Iranian rosemary essential oil using gas chromatography-mass spectrometry (GC-MS). Multivariate curve resolution (MCR) approach was used to overcome the problem of background, baseline offset and overlapping/embedded peaks in GC-MS. The analysis of GC-MS data revealed that sixty eight components exist in the rosemary essential oil. However, with the help of MCR this number was extended to ninety nine components with concentrations higher than 0.01%, which accounts for 98.23% of the total relative content of the rosemary essential oil. The most important constituents of the Iranian rosemary are 1,8-cineole (23.47%), α-pinene (21.74%), berbonone (7.57%), camphor (7.21%) and eucalyptol (4.49%).