Determination of toxic heavy metals in fish samples using dispersive micro solid phase extraction combined with inductively coupled plasma optical emission spectroscopy.

Monitoring of toxic heavy metals in fish samples is a matter of a great importance from the nutritional and toxicological points of view. A dispersive micro solid phase extraction (dµSPE) for preconcentration of trace Pb, Cd, Hg, Co, Ni ions using pectin coated magnetic graphene oxide (pectin/Fe3O4/GO) is presented. Inductively coupled plasma optical emission spectroscopy (ICP/OES) was utilized for analyzing the samples. The influence of parameters name as pH, extraction time, sample volume and amount of sorbent were optimized using central composite design (CCD) methodology. Detection and quantification limits were between 0.01 and 0.21 µg g-1 and 0.04-0.67 µg g-1 of fresh fish sample, respectively. Accuracy of the method verified using certified reference materials (NIST-SRM-1946). Concentration of the toxic heavy metals were successfully determined in 11 different fish samples using the proposed method.

Determination of toxic heavy metals in rice samples using ultrasound assisted emulsification microextraction combined with inductively coupled plasma optical emission spectroscopy.

A straightforward and efficient method was developed by ultrasound assisted emulsification microextraction (USAEME) combined with inductively coupled plasma optical emission spectroscopy (ICP/OES) to trace some toxic heavy metal ions in eight select farmed and four select imported rice samples. In this study, carbon tetrachloride (CCl4) and 1-(2-pyridylazo)2-naphthol (PAN) were used as the extraction solvent and complexing reagent, respectively. The extraction parameters were optimized by means of a central composite design (CCD). Detection and quantification limits were set to be between 0.02 and 0.08 µg L-1 and 0.07-0.30 µg L-1, respectively. The accuracy of our method was further verified against two certified reference materials namely NCS ZC73029 and NIES No 10-b. While no trace of contaminations was found in some rice samples (Tarom, Hashemi, Sadri, Khazar and one imported sample), others such as Kazemi, Jamshidi, Dom Siah, Guilanehand and three of the imported samples were considerably polluted.

A novel similarity search approach for high-performance thin-layer chromatography (HPTLC) fingerprinting of medicinal plants.

INTRODUCTION: In addition to the development of analytical equipment, another movement has also appeared in the field of computer assisted techniques for metabolite assessment. Although, some studies can be found in the literature there is still not available reliable and user-friendly software which is coupled with a simple chromatography method for developing a database to identify medicinal plants. OBJECTIVES: Developing a novel similarity search approach for high-performance thin-layer chromatography (HPTLC) fingerprinting. METHODS: Combined HPTLC with image analysis approach was used for similarity assessment of 70 standard medicinal plants. Ethyl acetate-ethyl methyl ketone-formic acid 98%-water (50:30:10:10) were chosen among different examined mobile phases. Liebermann-Burchard and anisaldehyde reagents were chosen for HPTLC derivatisation for visualisation. Image analysis based on Cannys' method was used to determine the spot size of each HPTLC image. A similarity search algorithm based on colour (RGB, HSV and Lab) information alone or together with retardation factor (Rf ) and spot size information calculated with the software was built to assess the fingerprinting of medicinal plants. RESULTS: The software was capable of calculating spots size and Rf values. It authenticated unknown samples based on comparing images information, spots size and/or Rf in the built database. Similarity values were 75-96% for the selected plants chromatograms with those of the same plant in the database. It presents better results than principal components analysis (PCA), classification and regression trees (CART) and partial least squares discriminant analysis (PLS-DA). CONCLUSION: The procedure paves the way for constructing a database of HPTLC images of medicinal plants.

2D-QSAR study of some 2,5-diaminobenzophenone farnesyltransferase inhibitors by different chemometric methods.

Quantitative structure activity relationship (QSAR) models can be used to predict the activity of new drug candidates in early stages of drug discovery. In the present study, the information of the ninety two 2,5-diaminobenzophenone-containing farnesyltranaferase inhibitors (FTIs) were taken from the literature. Subsequently, the structures of the molecules were optimized using Hyperchem software and molecular descriptors were obtained using Dragon software. The most suitable descriptors were selected using genetic algorithms-partial least squares and stepwise regression, where exhibited that the volume, shape and polarity of the FTIs are important for their activities. The two-dimensional QSAR models (2D-QSAR) were obtained using both linear methods (multiple linear regression) and non-linear methods (artificial neural networks and support vector machines). The proposed QSAR models were validated using internal validation method. The results showed that the proposed 2D-QSAR models were valid and they can be used for prediction of the activities of the 2,5-diaminobenzophenone-containing FTIs. In conclusion, the 2D-QSAR models (both linear and non-linear) showed good prediction capability and the non-linear models were exhibited more accuracy than the linear models.

Quantitative structure-retention relationship modeling of gas chromatographic retention times based on thermodynamic data.

Thermodynamic parameters of ΔH(T0), ΔS(T0), and ΔCP for 156 compounds comprising alkanes, alkyl halides and alcohols were determined for a 5% phenyl 95% methyl stationary phase. The determination of thermodynamic parameters relies on a Nelder-Mead simplex optimization to rapidly obtain the parameters. Two methodologies of external and leave one out cross validations were applied to assess the robustness of the estimations of thermodynamic parameters. The largest absolute errors in predicted retention time across all temperature ramps and all compounds were 1.5 and 0.3s for external and internal sets, respectively. The possibility of an in silico extension of the thermodynamic library was tested using a quantitative structure-retention relationship (QSRR) methodology. The estimated thermodynamic parameters were utilized to develop QSRR models. Individual partial least squares (PLS) models were developed for each of the three classes of the molecules. R(2) values for the test sets of all models across all temperature ramps were larger than 0.99 and the average of relative errors in retention time predictions of the test sets for alkanes, alcohols, and alkyl halides were 1.8%, 2.4%, and 2.5%, respectively.

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.

A standardized method for the calibration of thermodynamic data for the prediction of gas chromatographic retention times.

A new method for calibrating thermodynamic data to be used in the prediction of analyte retention times is presented. The method allows thermodynamic data collected on one column to be used in making predictions across columns of the same stationary phase but with varying geometries. This calibration is essential as slight variances in the column inner diameter and stationary phase film thickness between columns or as a column ages will adversely affect the accuracy of predictions. The calibration technique uses a Grob standard mixture along with a Nelder-Mead simplex algorithm and a previously developed model of GC retention times based on a three-parameter thermodynamic model to estimate both inner diameter and stationary phase film thickness. The calibration method is highly successful with the predicted retention times for a set of alkanes, ketones and alcohols having an average error of 1.6s across three columns.

Rapid determination of thermodynamic parameters from one-dimensional programmed-temperature gas chromatography for use in retention time prediction in comprehensive multidimensional chromatography.

A new method for estimating the thermodynamic parameters of ΔH(T0), ΔS(T0), and ΔCP for use in thermodynamic modeling of GC×GC separations has been developed. The method is an alternative to the traditional isothermal separations required to fit a three-parameter thermodynamic model to retention data. Herein, a non-linear optimization technique is used to estimate the parameters from a series of temperature-programmed separations using the Nelder-Mead simplex algorithm. With this method, the time required to obtain estimates of thermodynamic parameters a series of analytes is significantly reduced. This new method allows for precise predictions of retention time with the average error being only 0.2s for 1D separations. Predictions for GC×GC separations were also in agreement with experimental measurements; having an average relative error of 0.37% for (1)tr and 2.1% for (2)tr.

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.

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.

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.

Self-modeling curve resolution techniques applied to comparative analysis of volatile components of Iranian saffron from different regions.

Volatile components of saffron from different regions of Iran were extracted by ultrasonic-assisted solvent extraction (USE) and were analyzed by gas chromatography-mass spectrometry (GC-MS). Self-modeling curve resolution (SMCR) was proposed for resolving the co-eluted GC-MS peak clusters into pure chromatograms and mass spectra. Multivariate curve resolution-objective function minimization (MCR-FMIN) and multivariate curve resolution-alternating least square (MCR-ALS) were successfully used for this purpose. The accuracy of the qualitative and quantitative results was improved considerably using SMCR techniques. Comparison of the results of saffron from different regions of Iran showed that their volatile components are different from chemical components and relative percentages points of view. Safranal is the main component of all samples. In addition, 4-hydroxy-2,6,6-trimethyl-1-cyclohexene-1-carboxaldehyde (HTCC), 2(5H)-furanone, 2,4,4-trimethyl-3-carboxaldehyde-5-hydroxy-2,5-cyclohexadien-1-one and 2(3H)-furanone, dihydro-4-hydroxy were common in all samples with high percentages. The results proved that combining of SMCR techniques with USE-GC-MS produces a powerful tool for the analysis of the complex samples.

Characterization of volatile components of Iranian saffron using factorial-based response surface modeling of ultrasonic extraction combined with gas chromatography-mass spectrometry analysis.

The volatile components of Iranian saffron were extracted using ultrasonic solvent extraction (USE) technique and then were separated and detected by gas chromatography-mass spectrometry (GC-MS). Variables affecting the extraction procedure were screened by using a 2(5-1) fractional factorial design and among them; sample amount, solvent volume, solvent ratio and extraction time were optimized by applying a rotatable central composite design (CCD). The optimum values of factors were: 2.38g sample, 29.04mL solvent, 69.23% MeOH solvent ratio and 71.8min for the extraction time. Forty constituents were identified for Iranian saffron by GC-MS representing 90% of the total peak area. The major components were 2,6,6-trimethyl-1,3-cyclohexadiene-1-carboxaldehyde, namely safranal (26.29%), bicyclo[3,2,0]hept-2-ene-,4-ethoxy-,endo (5.69%), linoleic acid (4.77%), 4-hydroxy-2,6,6-trimethyl-1-cyclohexene-1-carboxaldehyde, namely HTCC (4.44%), and nonadecanol (3.32%). Some new compounds were identified for the first time in saffron. In addition, the results of this study were compared with those of Greek saffron.