Optimisation of the separation of herbicides by linear gradient high performance liquid chromatography utilising artificial neural networks

Effective degradation of bentazone by two-dimensional and three-phase, three-dimensional electro-oxidation system: kinetic studies and optimization using ANN

Bentazone is a broad-leaved weed-specific herbicide in the pesticide industry. This study focused on removing bentazone from water using three different methods: a two and three-dimensional electro-oxidation process (2D/EOP and 3D/EOP) with a fluid-type reactor arrangement using tetraethylenepentamine-loaded particle electrodes and an adsorption method. Additionally, we analysed the effects of two types of supporting electrolytes  (Na2SO4 and NaCl) on the degradation process. The energy consumption amounts were calculated to evaluate the obtained results. The degradation reaction occurs 3.5 times faster in 3D/EOP than in 2D/EOP at 6 V in Na2SO4. Similarly, the degradation reaction of bentazone in NaCl occurs 2.5 times faster in 3D/EOP than in 2D/EOP at a value of 7.2 mA/cm2. Removal of bentazone is significantly better in 3D/EOPs than in 2D/EOPs. The use of particle electrodes can significantly enhance the degradation efficiency. The study further assessed the prediction abilities of the machine learning model (ANN). The ANN presented reasonable accuracy in bentazone degradation with high R2 values of 0.97953, 0.98561, 0.98563, and 0.99649 for 2D with Na2SO4, 2D with NaCl, 3D with Na2SO4, and 3D with NaCl, respectively.

Modelling of multilinear gradient retention time of bio-sweetener rebaudioside A in HPLC analysis

Artificial neural network (ANN), as one of the artificial intelligence methods, has been widely using in HPLC studies for modelling purposes. Stevia rebaudiana is an important industrial plant due to its bio-sweetener molecule, rebaudioside-a, in its leaves. Although rebaudioside-a is up to 300-fold sweeter than sucrose, its calorie is almost zero. In this study, HPLC optimization of rebaudioside-a was studied and the optimization data based on multilinear gradient retention times were modelled by ANN. The input parameters were selected as concentrations, column temperatures, initial acetonitrile percentage for the first step of gradient elution, initial acetonitrile percentage for the second step of gradient elution, slope of acetonitrile, wavelengths, flow rates. The retention time was the output. Also, dried S. rebaudiana leaves were extracted and the concentrations were evaluated by HPLC. According to the ANN results, the most effective parameters on the prediction of non-linear gradient retention time for rebaudioside-a were found as flow rate and initial acetonitrile percentage for the second step of gradient. The best back propagation was selected as Levenberg-Marquardt algorithm. The highest rebaudioside-a level was found as 96.53 ± 6.36 μg mL^-1. ANN modelling methods can be used in preparative HPLC applications to estimate the retention time of steviol glycosides.

Improvement of Ficin-Based Inhibitive Enzyme Assay for Toxic Metals Using Response Surface Methodology and Its Application for Near Real-Time Monitoring of Mercury in Marine Waters

Potentially toxic metals pollution in the Straits of Malacca warrants the development of rapid, simple and sensitive assays. Enzyme-based assays are excellent preliminary screening tools with near real-time potential. The heavy-metal assay based on the protease ficin was optimized for mercury detection using response surface methodology. The inhibitive assay is based on ficin action on the substrate casein and residual casein is determined using the Coomassie dye-binding assay. Toxic metals strongly inhibit this hydrolysis. A central composite design (CCD) was utilized to optimize the detection of toxic metals. The results show a marked improvement for the concentration causing 50% inhibition (IC₅₀) for mercury, silver and copper. Compared to one-factor-at-a-time (OFAT) optimization, RSM gave an improvement of IC₅₀ (mg/L) from 0.060 (95% CI, 0.030–0.080) to 0.017 (95% CI, 0.016–0.019), from 0.098 (95% CI, 0.077–0.127) to 0.028 (95% CI, 0.022–0.037) and from 0.040 (95% CI, 0.035–0.045) to 0.023 (95% CI, 0.020–0.027), for mercury, silver and copper, respectively. A near-real time monitoring of mercury concentration in the Straits of Malacca at one location in Port Klang was carried out over a 4 h interval for a total of 24 h and validated by instrumental analysis, with the result revealing an absence of mercury pollution in the sampling site.

UHPLC Analysis of Saffron (Crocus sativus L.): Optimization of Separation Using Chemometrics and Detection of Minor Crocetin Esters

Ultra-high performance liquid chromatography (UHPLC) coupled with diode array detection (DAD) was applied to improve separation and detection of mono- and bis-glucosyl esters of crocetin (crocins), the main red-colored constituents of saffron (Crocus sativus L.), and other polar components. Response surface methodology (RSM) was used to optimise the chromatographic resolution on the Kinetex C18 (Phenomenex) column taking into account of the combined effect of the column temperature, the eluent flow rate and the slope of a linear eluent concentration gradient. A three-level full-factorial design of experiments was adopted to identify suitable combinations of the above factors. The influence of the separation conditions on the resolutions of 22 adjacent peaks was simultaneously modelled by a multi-layer artificial neural network (ANN) in which a bit string representation was used to identify the target analytes. The chromatogram collected under the optimal separation conditions revealed a higher number of crocetin esters than those already characterised by means of mass-spectrometry data and usually detected by HPLC. Ultra-high performance liquid chromatography analyses carried out on the novel Luna Omega Polar C18 (Phenomenex) column confirmed the large number of crocetin derivatives. Further work is in progress to acquire mass-spectrometry data and to clarify the chemical structure to the newly found saffron components.

Contamination of rice field water with sulfonylurea and phenoxy herbicides in the Muda Irrigation Scheme, Kedah, Malaysia

The purpose of the present study was to investigate the potential risk of herbicide contamination (2,4-dichlorophenoxy (2,4-D), 2-methyl-4-chlorophenoxyacetic acid (MCPA), metsulfuron, bensulfuron, and pyrazosulfuron) in the rice fields of the Muda Irrigation Scheme, Kedah, Malaysia. The study included two areas with different irrigation water sources namely non-recycled (N-RCL) and recycled (RCL) water. Periodic water sampling was carried out from the drainage canals during the planting period of the wet season 2006/2007 and dry season 2007. The HPLC-UV was used to detect herbicide residues in the water samples collected from the rice fields. The results showed that the concentration of sulfonylurea herbicides such as bensulfuron and metsulfuron in the rice field was 0.55 and 0.51 μg/L, respectively. The potential risk of contamination depended on the actual dosage of each herbicide applied by farmers to their rice fields. The potential risk of water pollution by the five herbicides studied in the area with RCL water tended to be more widespread compared to the area with N-RCL water due to surface water runoff with higher levels of weedicides to the surrounding areas. During the two seasons, 50-73% of the water samples collected from the area receiving RCL water contained the five herbicides studied at concentrations of more than 0.05 μg/L, and this percentage was higher than that from the areas receiving N-RCL water (45-69%). During the wet season, the overall total mean concentration of the eight herbicides found in the samples collected from the area with RCL water (6.27 μg/L) was significantly higher (P < 0.01) than that from the area receiving N-RCL water (2.39 μg/L). Meanwhile, during the dry season, there was no significant difference (P > 0.05) in the herbicide concentrations between the areas receiving RCL (6.16 μg/L) and N-RCL water (7.43 μg/L) water.

Optimization of on-line solid phase extraction and HPLC conditions using response surface methodology for determination of WM-5 in mouse plasma and its application to pharmacokinetic study

Response surface methodology (RSM) was utilized for rapid and systematic optimization of on-line solid-phase extraction (SPE) parameters to maximize the response and separation of WM-5. The optimization was performed with Box-Behnken designs. Four major parameters were investigated for their contributions to the response and separation of WM-5, with a total of 29 experiments being performed for each instrument, respectively. Quantitative determination of WM-5 in mouse plasma was performed to evaluate the statistical significance of the parameters on chromatographic response. A fully automated on-line SPE and high-performance liquid chromatography (HPLC) with diode array detection (DAD) method was developed for the determination of WM-5 in mouse plasma. Calibration curve with good linearity (r=0.9989) was obtained in the range of 20-4000 ng/mL in mouse plasma. The limit of detection (LOD) and lower limit of quantification (LLOQ) of the assay were 6 ng/mL and 20 ng/mL, respectively. The overall intra-day and the inter-day variations were less than 1.90%. The recovery of the method was in the range of 93.74-96.33% with RSD less than 3.06%. The optimized method demonstrated good performance in terms of specificity, LLOQ, linearity, recovery, precision and accuracy, and was successfully applied to quantify WM-5 in mouse plasma to support the pharmacokinetic study.

Artificial neural network modeling and optimization of ultrahigh pressure extraction of green tea polyphenols

In this study, the ultrahigh pressure extraction of green tea polyphenols was modeled and optimized by a three-layer artificial neural network. A feed-forward neural network trained with an error back-propagation algorithm was used to evaluate the effects of pressure, liquid/solid ratio and ethanol concentration on the total phenolic content of green tea extracts. The neural network coupled with genetic algorithms was also used to optimize the conditions needed to obtain the highest yield of tea polyphenols. The obtained optimal architecture of artificial neural network model involved a feed-forward neural network with three input neurons, one hidden layer with eight neurons and one output layer including single neuron. The trained network gave the minimum value in the MSE of 0.03 and the maximum value in the R(2) of 0.9571, which implied a good agreement between the predicted value and the actual value, and confirmed a good generalization of the network. Based on the combination of neural network and genetic algorithms, the optimum extraction conditions for the highest yield of green tea polyphenols were determined as follows: 498.8 MPa for pressure, 20.8 mL/g for liquid/solid ratio and 53.6% for ethanol concentration. The total phenolic content of the actual measurement under the optimum predicated extraction conditions was 582.4 ± 0.63 mg/g DW, which was well matched with the predicted value (597.2mg/g DW). This suggests that the artificial neural network model described in this work is an efficient quantitative tool to predict the extraction efficiency of green tea polyphenols.

Prediction of extraction efficiency in supported liquid membrane with a stagnant acceptor phase by means of artificial neural network

An artificial neural network model of supported liquid membrane extraction process with a stagnant acceptor phase is proposed. Triazine herbicides and phenolic compounds were used as model compounds. The model is able to predict the compound extraction efficiency within the same family based on the octanol-water partition coefficient, water solubility, molecular mass and ionisation constant of the compound. The network uses the back-propagation algorithm for evaluating the connection strengths representing the correlations between inputs (octanol-water partition coefficients logP, acid dissociation constant pK(a), water solubility and molecular weight) and outputs (extraction efficiency in dihexyl ether and undecane as organic solvents). The model predicted results in good agreement with the experimental data and the average deviations for all the cases are found to be smaller than ±3%. Moreover, standard statistical methods were applied for exploration of relationships between studied parameters.

An experimental design approach employing artificial neural networks for the determination of potential endocrine disruptors in food using matrix solid-phase dispersion

Matrix solid-phase dispersion (MSPD) as a sample preparation method for the determination of two potential endocrine disruptors, linuron and diuron and their common metabolites, 1-(3,4-dichlorophenyl)-3-methylurea (DCPMU), 1-(3,4-dichlorophenyl) urea (DCPU) and 3,4-dichloroaniline (3,4-DCA) in food commodities has been developed. The influence of the main factors on the extraction process yield was thoroughly evaluated. For that purpose, a 3 fractional factorial design in further combination with artificial neural networks (ANNs) was employed. The optimal networks found were afterwards used to identify the optimum region corresponding to the highest average recovery displaying at the same time the lowest standard deviation for all analytes. Under final optimal conditions, potato samples (0.5 g) were mixed and dispersed on the same amount of Florisil. The blend was transferred on a polypropylene cartridge and analytes were eluted using 10 ml of methanol. The extract was concentrated to 50 microl of acetonitrile/water (50:50) and injected in a high performance liquid chromatography coupled to UV-diode array detector system (HPLC/UV-DAD). Recoveries ranging from 55 to 96% and quantification limits between 5.3 and 15.2 ng/g were achieved. The method was also applied to other selected food commodities such as apple, carrot, cereals/wheat flour and orange juice demonstrating very good overall performance.

Statistical Assessment of Solvent Mixture Models Used for Separation of Biological Active Compounds

Two mathematical models with seven and six parameters have been created for use as methods for identification of the optimum mobile phase in chromatographic separations. A series of chromatographic response functions were proposed and implemented in order to assess and validate the models. The assessment was performed on a set of androstane isomers. Pearson, Spearman, Kendall tau-a,b,c and Goodman-Kruskal correlation coefficients were used in order to identify and to quantify the link and its nature (quantitative, categorical, semi-quantitative, both quantitative and categorical) between experimental values and the values estimated by the mathematical models. The study revealed that the six parameter model is valid and reliable for five chromatographic response factors (retardation factor, retardation factor ordered ascending by the chromatographic peak, resolution of pairs of compound, resolution matrix of successive chromatographic peaks, and quality factor). Furthermore, the model could be used as an instrument in analysis of the quality of experimental data. The results obtained by applying the model with six parameters for deviations of rank sums suggest that the data of the experiment no. 8 are questionable.