Chemometrics

Wavelet Transform-Based UV Spectroscopy for Pharmaceutical Analysis

In research and development laboratories, chemical or pharmaceutical analysis has been carried out by evaluating sample signals obtained from instruments. However, the qualitative and quantitative determination based on raw signals may not be always possible due to sample complexity. In such cases, there is a need for powerful signal processing methodologies that can effectively process raw signals to get correct results. Wavelet transform is one of the most indispensable and popular signal processing methods currently used for noise removal, background correction, differentiation, data smoothing and filtering, data compression and separation of overlapping signals etc. This review article describes the theoretical aspects of wavelet transform (i.e., discrete, continuous and fractional) and its characteristic applications in UV spectroscopic analysis of pharmaceuticals.

Chemometric-assisted QuEChERS extraction method for post-harvest pesticide determination in fruits and vegetables

An effective analysis method was developed based on a chemometric tool for the simultaneous quantification of five different post-harvest pesticides (2,4-dichlorophenoxyacetic acid (2,4-D), carbendazim, thiabendazole, iprodione, and prochloraz) in fruits and vegetables. In the modified QuEChERS (quick, easy, cheap, effective, rugged and safe) method, the factors and responses for optimization of the extraction and cleanup analyses were compared using the Plackett-Burman (P-B) screening design. Furthermore, the significant factors (toluene percentage, hydrochloric acid (HCl) percentage, and graphitized carbon black (GCB) amount) were optimized using a central composite design (CCD) combined with Derringer's desirability function (DF). The limits of quantification (LOQs) were estimated to be 1.0 μg/kg for 2,4-D, carbendazim, thiabendazole, and prochloraz, and 1.5 μg/kg for iprodione in food matrices. The mean recoveries were in the range of 70.4-113.9% with relative standard deviations (RSDs) of less than 16.9% at three spiking levels. The measurement uncertainty of the analytical method was determined using the bottom-up approach, which yielded an average value of 7.6%. Carbendazim was most frequently found in real samples analyzed using the developed method. Consequently, the analytical method can serve as an advantageous and rapid tool for determination of five preservative pesticides in fruits and vegetables.

Bioelectronic tongues: New trends and applications in water and food analysis

Over the last years, there has been an increasing demand for fast, highly sensitive and selective methods of analysis to meet new challenges in environmental monitoring, food safety and public health. In response to this demand, biosensors have arisen as a promising tool, which offers accurate chemical data in a timely and cost-effective manner. However, the difficulty to obtain sensors with appropriate selectivity and sensitivity for a given analyte, and to solve analytical problems which do not require the quantification of a certain analyte, but an overall effect on a biological system (e.g. toxicity, quality indices, provenance, freshness, etc.), led to the concept of electronic tongues as a new strategy to tackle these problems. In this direction, to improve the performance of electronic tongues, and thus to spawn new application fields, biosensors have recently been incorporated to electronic tongue arrays, leading to what is known as bioelectronic tongues. Bioelectronic tongues provide superior performance by combining the capabilities of electronic tongues to derive meaning from complex or imprecise data, and the high selectivity and specificity of biosensors. The result is postulated as a tool that exploits chemometrics to solve biosensors' interference problems, and biosensors to solve electronic tongues' selectivity problems. The review presented herein aims to illustrate the capabilities of bioelectronic tongues as analytical tools, especially suited for screening analysis, with particular emphasis in water analysis and the characterization of food and beverages. After briefly reviewing the key concepts related to the design and principles of electronic tongues, we provide an overview of significant contributions to the field of bioelectronic tongues and their future perspectives.

Chemometric-assisted QuEChERS extraction method for the residual analysis of thiacloprid, spirotetramat and spirotetramat's four metabolites in pepper: Application of their dissipation patterns

Chemometric tools equipped with a Plackett-Burman (P-B) design, a central composite design (CCD) and a desirability profile were employed to optimise the QuEChERS (quick, easy, cheap, effective, rugged and safe) method for the quantification of thiacloprid, spirotetramat and spirotetramat's four metabolites in pepper. The average recoveries were in the range of 71.6-119.5%, with relative standard deviations ⩽ 12.1%. The limit of quantification for the method was less than 0.01 mg/kg. The method was applied to field samples to evaluate the residual characteristics of thiacloprid and spirotetramat. The data showed that the first+first-order model is a better fit than the first order model for the dissipation of thiacloprid and spirotetramat in pepper. The half-lives of thiacloprid and spirotetramat in pepper are 0.81 and 1.21 days, respectively. The final residues were between 0.016 mg/kg and 0.13 mg/kg for thiacloprid and 0.08 mg/kg and 0.12 mg/kg for spirotetramat.

Assessment of the water self-purification capacity on a river affected by organic pollution: application of chemometrics in spatial and temporal variations

Water pollution caused by organic matter is a major global problem which requires continuous evaluation. Multivariate statistical analysis was applied to assess spatial and temporal changes caused by natural and anthropogenic phenomena along Potrero de los Funes River. Cluster analysis (CA), principal component analysis (PCA) and analysis of variance (ANOVA) were applied to a data set collected throughout a period of 3 years (2010-2012), which monitored 22 physical, chemical and biological parameters. Content of dissolved oxygen in water and biochemical oxygen demand in a watercourse are indicators of pollution caused by organic matter. For this reason, the Streeter-Phelps model was used to evaluate the water self-purification capacity. Hierarchical cluster analysis grouped the sampling sites based on the similarity of water quality characteristics. PCA resulted in two latent factors explaining 75.2 and 17.6 % of the total variance in water quality data sets. Multidimensional ANOVA suggested that organic pollution is mainly due to domestic wastewater run-offs and anthropogenic influence as a consequence of increasing urbanization and tourist influx over the last years. Besides, Streeter-Phelps parameters showed a low reaeration capacity before dam with low concentration of dissolved oxygen. Furthermore, self-purification capacity loss was correlated with the decrease of the Benthic Index. This measurement suggested that biological samplings complement the physical-chemical analysis of water quality.

Spatial water quality assessment of Langat River Basin (Malaysia) using environmetric techniques

This study investigates the spatial water quality pattern of seven stations located along the main Langat River. Environmetric methods, namely, the hierarchical agglomerative cluster analysis (HACA), the discriminant analysis (DA), the principal component analysis (PCA), and the factor analysis (FA), were used to study the spatial variations of the most significant water quality variables and to determine the origin of pollution sources. Twenty-three water quality parameters were initially selected and analyzed. Three spatial clusters were formed based on HACA. These clusters are designated as downstream of Langat river, middle stream of Langat river, and upstream of Langat River regions. Forward and backward stepwise DA managed to discriminate six and seven water quality variables, respectively, from the original 23 variables. PCA and FA (varimax functionality) were used to investigate the origin of each water quality variable due to land use activities based on the three clustered regions. Seven principal components (PCs) were obtained with 81% total variation for the high-pollution source (HPS) region, while six PCs with 71% and 79% total variances were obtained for the moderate-pollution source (MPS) and low-pollution source (LPS) regions, respectively. The pollution sources for the HPS and MPS are of anthropogenic sources (industrial, municipal waste, and agricultural runoff). For the LPS region, the domestic and agricultural runoffs are the main sources of pollution. From this study, we can conclude that the application of environmetric methods can reveal meaningful information on the spatial variability of a large and complex river water quality data.

What is the origin of positional fluctuation of spectral features: true frequency shift or relative intensity changes of two overlapped bands?

We investigated what is really meant by so-called positional or frequency fluctuation of spectral features. To show the difference between the true frequency shift of a single band and apparent peak maximum shift caused by relative intensity changes of overlapped adjacent bands, we analyzed infrared (IR) spectra of the OH stretching band of ethylene glycol during the heating process and the C=O stretching band of acetone in a mixed solvent CHCl(3)/CCl(4) with varying solvent compositions. These spectra are well-known examples of so-called "band shift" phenomena often interpreted as the manifestation of gradual changes in the IR frequency associated with a specific chemical bond under the influence of molecular interactions. Analyses of IR spectra showed that the apparent positional shifts of peak maxima in these systems are actually due to relative contribution changes of two overlapped bands, instead of the gradual frequency shift of a single band induced by the change in the strength of molecular interactions. To further clarify our interpretation of "peak maximum shifts", we also analyzed simulated spectral datasets, comparing the true band frequency shift and change in the relative contributions of overlapped bands. It was found that principal component analysis (PCA) is a surprisingly sensitive tool to distinguish the two possible mechanisms of peak maximum shift. The new insight revealed by this study should help the interpretation of molecular interactions probed by vibrational spectroscopy.

Quantum chemical calculations to reveal the relationship between the chemical structure and the fluorescence characteristics of phenylquinolinylethynes and phenylisoquinolinylethynes derivatives, and to predict their relative fluorescence intensity

In this paper the relationship between the chemical structure and fluorescence characteristics of 30 phenylquinolinylethyne (PhQE), and phenylisoquinolinylethyne (PhIE) derivatives compounds employing ab initio calculations have been elucidated. Quantum chemical calculations (6-31G) were carried out to obtain: the optimized geometry, energy levels, charges and dipole moments of these compounds, in the singlet (steady and excited states) and triplet states. The relationship between quantum chemical descriptors, and wavelength of maximum excitation and emission indicated that these two parameters have the most correlation with quantum chemical hardness (eta). Also, stokes shift has the most correlation with the square of difference between the maximum of positive charges in the singlet steady and singlet excited states. The quantitative structure-property relationship (QSPR) of PhQE and PhIE was studied for relative fluorescence intensity (RFI). The genetic algorithm (GA) was applied to select the variables that resulted in the best-fit models. After the variable selection, multiple linear regression (MLR) and support vector machine (SVM) were both utilized to construct linear and non-linear QSPR models, respectively. The SVM model demonstrated a better performance than that of the MLR model. The route mean square error (RMSE) in the training and the test sets for the SVM model was 0.195 and 0.324, and the correlation coefficients were 0.965 and 0.960, respectively, thus revealing the reliability of this model. The resulting data indicated that SVM could be used as a powerful modeling tool for QSPR studies. According to the best of our knowledge, this is the first research on QSPR studies to predict RFI for a series of PhQE and PhIE derivative compounds using SVM.

Optimization of dispersive liquid-liquid microextraction for the selective determination of trace amounts of palladium by flame atomic absorption spectroscopy

A new simple and reliable method for rapid and selective extraction and determination of the trace levels of Pd(2+) ion was developed by dispersive liquid-liquid microextraction preconcentration and flame atomic absorption spectrometry detection. In the proposed approach, thioridazine HCl (TRH) was used as a Pd(2+) ion selective complexing agent. The effective parameters on the extraction recovery were studied and optimized utilizing two decent optimization methods; factorial design and central composite design (CCD). Through factorial design the best efficiency of extraction acquired using ethanol and chloroform as dispersive and extraction solvents respectively. CCD optimization resulted in 1.50 mL of dispersive solvent; 0.15 mL of extraction solvent; 0.45 mg of TRH and 250 mg of potassium chloride salt per 5 mL of sample solution. Under the optimum conditions the calibration graph was linear over the range 100-2000 microgL(-1). The average relative standard deviation was 0.7% for five repeated determinations. The limit of detection was 90 microg L(-1). The average enrichment factor and recovery reached 45.7% and 74.2% respectively. The method was successfully applied to the determination of trace amounts of palladium in the real water samples.

Chemometrical examination of active parameters and interactions in flow injection-capillary electrophoresis

The first detailed examination of flow injection-capillary electrophoresis (FI-CE) active parameters and their interactions via response surface methodology (RSM) is presented. Specifically, RSM in the form of a Box-Behnken design was implemented to effectively predict the significance of capillary length, voltage and injection volume on the optimization of an in-house built FI-CE analyzer. Initial studies were performed assessing peak height and peak shape of the model compound N,N-dimethylformamide. Optimum model conditions were then derived and used in the model separation of two small molecules, nicotinamide adenine dinucleotide, reduced form (NADH) and benzenesulfonamide. By implementing the RSM approach, detailed examination of active FI-CE parameters was possible, including the ability to reveal a significant interactive effect. This work is not only highly significant for advancing FI-CE developments, but instructive for investigators actively exploring other coupled analytical techniques and associated experimental parameters.

Chemometric experimental design based optimization techniques in capillary electrophoresis: a critical review of modern applications

A critical review of recent developments in the use of chemometric experimental design based optimization techniques in capillary electrophoresis applications is presented. Current advances have led to enhanced separation capabilities of a wide range of analytes in such areas as biological, environmental, food technology, pharmaceutical, and medical analysis. Significant developments in design, detection methodology and applications from the last 5 years (2002-2007) are reported. Furthermore, future perspectives in the use of chemometric methodology in capillary electrophoresis are considered.

Method development and validation for optimized separation of benzo[a]pyrene–quinone isomers using liquid chromatography–mass spectrometry and chemometric response surface methodology

The successful separation of three benzo[a]pyrene-quinone isomers, two of which were previously unresolved, using liquid chromatography-mass spectrometry (LC-MS) and response surface methodology is presented. Initial efforts centered on chromatographic separation of benzo[a]pyrene-1,6/3,6-quinone peaks evaluated for both resolution and retention time. The mergence of the two parameters was accomplished using the Derringer's desirability function with subsequent optimization by a Box-Behnken response surface design. By implementing the optimal flow rate, column temperature and eluent composition predicted by the validated model, enhanced resolution of the two isomers was achieved in less than 20 min. Calibrations were performed to quantify these isomers and the limits of detection were determined. Optimal model conditions were then used to identify three independent benzo[a]pyrene-quinone isomers produced in the irradiation of a benzo[a]pyrene standard solvated in oxygen-saturated methanol/methylene chloride. This work is not only highly significant to the field of environmental chemistry, but instructive for investigators struggling with the co-elusion of isomeric compounds in their work.

Combination of Genetic Algorithm and Partial Least Squares for Cloud Point Prediction of Nonionic Surfactants from Molecular Structures

Quantitative structure-property relationship (QSPR) analysis has been directed to a series of pure nonionic surfactants containing linear alkyl, cyclic alkyl, and alkey phenyl ethoxylates. Modeling of cloud point of these compounds as a function of the theoretically derived descriptors was established by multiple linear regression (MLR) and partial least squares (PLS) regression. In this study, a genetic algorithm (GA) was applied as a variable selection method in QSPR analysis. The results indicate that the GA is a very effective variable selection approach for QSPR analysis. The comparison of the two regression methods used showed that PLS has better prediction ability than MLR.

Soap Bubbles in Analytical Chemistry. Conductometric Determination of Sub-Parts Per Million Levels of Sulfur Dioxide with a Soap Bubble

Soap bubbles provide a fascinating tool that is little used analytically. With a very low liquid volume to surface area ratio, a soap bubble can potentially provide a very useful interface for preconcentration where mass transfer to an interfacial surface is important. Here we use an automated system to create bubbles of uniform size and film thickness. We utilize purified Triton-X 100, a nonionic surfactant, to make soap bubbles. We use such bubbles as a gas-sampling interface. Incorporating hydrogen peroxide into the bubble provides a system where electrical conductance increases as the bubble is exposed to low concentrations of sulfur dioxide gas. We theoretically derive the conductance of a hollow conducting spherical thin film with spherical cap electrodes. We measure the film thickness by incorporating a dye in the bubble making solution and laser transmission photometry and find that it agrees well with the geometrically computed thickness. With the conductance of the bubble-making soap solution being measured by conventional methods, we show that the measured values of the bubble conductance with known bubble and electrode dimensions closely correspond to the theoretically computed value. Finally, we demonstrate that sub-ppm levels of SO(2) can readily be detected by the conductivity change of a hydrogen peroxide-doped soap bubble, measured in situ, when the gas flows around the bubble.

Identification and quantification of industrial grade glycerol adulteration in red wine with fourier transform infrared spectroscopy using chemometrics and artificial neural networks

Fourier transform infrared (FT-IR) single bounce micro-attenuated total reflectance (mATR) spectroscopy, combined with multivariate and artificial neural network (ANN) data analysis, was used to determine the adulteration of industrial grade glycerol in selected red wines. Red wine samples were artificially adulterated with industrial grade glycerol over the concentration range from 0.1 to 15% and calibration models were developed and validated. Single bounce infrared spectra of glycerol adulterated wine samples were recorded in the fingerprint mid-infrared region, 900-1500 cm(-1). Partial least squares (PLS) and PLS first derivatives were used for quantitative analysis (r2 = 0.945 to 0.998), while linear discriminant analysis (LDA) and canonical variate analysis (CVA) were used for classification and discrimination. The standard error of prediction (SEP) in the validation set was between 1.44 and 2.25%. Classification of glycerol adulterants in the different brands of red wine using CVA resulted in a classification accuracy in the range between 94 and 98%. Artificial neural network analysis based on the quick back propagation network (BPN) and the radial basis function network (RBFN) algorithms had classification success rates of 93% using BPN and 100% using RBFN. The genetic algorithm network was able to predict the concentrations of glycerol in wine up to an accuracy of r2 = 0.998.

Process analytical technology case study part I: feasibility studies for quantitative near-infrared method development

This article is the first of a series of articles detailing the development of near-infrared (NIR) methods for solid-dosage form analysis. Experiments were conducted at the Duquesne University Center for Pharmaceutical Technology to qualify the capabilities of instrumentation and sample handling systems, evaluate the potential effect of one source of a process signature on calibration development, and compare the utility of reflection and transmission data collection methods. A database of 572 production-scale sample spectra was used to evaluate the interbatch spectral variability of samples produced under routine manufacturing conditions. A second database of 540 spectra from samples produced under various compression conditions was analyzed to determine the feasibility of pooling spectral data acquired from samples produced at diverse scales. Instrument qualification tests were performed, and appropriate limits for instrument performance were established. To evaluate the repeatability of the sample positioning system, multiple measurements of a single tablet were collected. With the application of appropriate spectral preprocessing techniques, sample repositioning error was found to be insignificant with respect to NIR analyses of product quality attributes. Sample shielding was demonstrated to be unnecessary for transmission analyses. A process signature was identified in the reflection data. Additional tests demonstrated that the process signature was largely orthogonal to spectral variation because of hardness. Principal component analysis of the compression sample set data demonstrated the potential for quantitative model development. For the data sets studied, reflection analysis was demonstrated to be more robust than transmission analysis.

Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti River (India)--a case study

This case study reports different multivariate statistical techniques applied for evaluation of temporal/spatial variations and interpretation of a large complex water-quality data set obtained during monitoring of Gomti River in Northern part of India. Water quality of the Gomti River, a major tributary of the Ganga River was monitored at eight different sites selected in relatively low, moderate and high pollution regions, regularly over a period of 5 years (1994-1998) for 24 parameters. The complex data matrix (17,790 observations) was treated with different multivariate techniques such as cluster analysis, factor analysis/principal component analysis (FA/PCA) and discriminant analysis (DA). Cluster analysis (CA) showed good results rendering three different groups of similarity between the sampling sites reflecting the different water-quality parameters of the river system. FA/PCA identified six factors, which are responsible for the data structure explaining 71% of the total variance of the data set and allowed to group the selected parameters according to common features as well as to evaluate the incidence of each group on the overall variation in water quality. However, significant data reduction was not achieved, as it needed 14 parameters to explain 71% of both the temporal and spatial changes in water quality. Discriminant analysis showed the best results for data reduction and pattern recognition during both temporal and spatial analysis. Discriminant analysis showed five parameters (pH, temperature, conductivity, total alkalinity and magnesium) affording more than 88% right assignations in temporal analysis, while nine parameters (pH, temperature, alkalinity, Ca-hardness, DO, BOD, chloride, sulfate and TKN) to afford 91% right assignations in spatial analysis of three different regions in the basin. Thus, DA allowed reduction in dimensionality of the large data set, delineating a few indicator parameters responsible for large variations in water quality. This study presents necessity and usefulness of multivariate statistical techniques for evaluation and interpretation of large complex data sets with a view to get better information about the water quality and design of monitoring network for effective management of water resources.

Application of multivariate curve resolution for analysis of FT-IR microspectroscopic images of in situ plant tissue

The chemometric techniques of multivariate curve resolution (MCR) are aimed at extracting the spectra and concentrations of individual components present in mixtures using a minimum set of initial assumptions. We present results from the application of alternating least squares (ALS) based MCR to the analysis of hyperspectral images of in situ biological material. The spectra of individual pure components were mathematically extracted and then identified by searching the spectra against a commercial library. No prior information about the chemical composition of the material was used in the data analysis. The spectra recovered by ALS-MCR analysis of an FT-IR microspectroscopic image of an 8-micron-cornkernel section matched very well the spectra of the corn storage protein, zein, and starch. Through the application of MCR, we were able to show the presence of a second spectrally different protein, which could not be easily seen using univariate analysis. These results demonstrate the value of multivariate curve resolution techniques for the analysis of biological tissue. The value of principal components analysis (PCA) for hyperspectral image analysis is also discussed.

Quantitative structure-enantioselective retention relationships for chromatographic separation of arylalkylcarbinols on Pirkle type chiral stationary phases

Quantitative structure-retention (QSRR, retention factors log k1 and log k2 for the first and second eluted enantiomer) as well as enantioselective retention relationships (QSERR, separation factor log a) for a series of 42 chiral arylalkylcarbinols on four brush-type chiral stationary phases are derived by multiple linear regression analyses and artificial neuronal network calculations using 2D and 3D molecular descriptors including those obtained by quantum chemical calculations. Separation factors are in addition modeled by the 3D-QSAR method of comparative molecular field analysis (CoMFA). For the retention factors the LUMO energy turns out to be the most important descriptor, whereas for log a it is the hydrophobicity of the analytes. With CoMFA both the steric and electrostatic field are found to be of almost comparable significance.

Application of quantitative chemometric analysis techniques to direct sampling mass spectrometry

This paper explores the use of direct sampling mass spectrometry coupled with multivariate chemometric analysis techniques for the analysis of sample mixtures containing analytes with similar mass spectra. Water samples containing varying mixtures of toluene, ethyl benzene, and cumene were analyzed by purge-and-trap/direct sampling mass spectrometry. Multivariate calibration models were built using partial least-squares regression (PLS), trilinear partial least-squares regression (tri-PLS), and parallel factor analysis (PARAFAC), with the latter two methods taking advantage of the differences in the temporal profiles of the analytes. The prediction errors for each model were compared to those obtained with simple univariate regression. Multivariate quantitative methods were found to be superior to univariate regression when a unique ion for quantitation could not be found. For prediction samples that contained unmodeled, interfering compounds, PARAFAC outperformed the other analysis methods. The uniqueness of the PARAFAC model allows for estimation of the mass spectra of the interfering compounds, which can be subsequently identified via visual inspection or a library search.

Surface characterization of hydroxyapatite and related calcium phosphates by XPS and TOF-SIMS

The surfaces of six biologically interesting calcium phosphate (CaP) phases (hydroxyapatite, dibasic calcium phosphate dihydrate, dibasic calcium phosphate, monobasic calcium phosphate, beta-tribasic calcium phosphate, octacalcium phosphate) have been examined by X-ray photoelectron spectroscopy (XPS) and time-of-flight secondary ion mass spectrometry (TOF-SIMS). The intensity of an O(1s) shake-up satellite correlates with the phosphate oxygen content. Together with the Ca/P and O/Ca XPS peak ratios, this feature helps provide identification of the CaP phase(s) present in the surface of unknown samples and establish their mole fractions, as proven with a bone sample. Contributions from carbonate impurities can be quantified using its C(1s) peak at 279.9 eV and subtracted from the O(1s) line shape to aid identification. Principal component analysis (PCA) has been applied successfully to analyze TOF-SIMS spectra of these six CaP phases. Multivariate analysis can help differentiate these CaP phases using the first two PCs, which are dominated by the relative intensities of only a few key ions: PO3-, O-, Ca+, CaOH+, PO2-, and OH-.