Shikha Gupta

Optimizing adsorption of crystal violet dye from water by magnetic nanocomposite using response surface modeling approach

A magnetic nanocomposite was developed and characterized. Adsorption of crystal violet (CV) dye from water was studied using the nanocomposite. A four-factor central composite design (CCD) combined with response surface modeling (RSM) was employed for maximizing CV removal from aqueous solution by the nanocomposite based on 30 different experimental data obtained in a batch study. Four independent variables, viz. temperature (10-50°C), pH of solution (2-10), dye concentration (240-400 mg/l), and adsorbent dose (1-5 g/l) were transformed to coded values and a second-order quadratic model was built to predict the responses. The significance of independent variables and their interactions were tested by the analysis of variance (ANOVA) and t-test statistics. Adequacy of the model was tested by the correlation between experimental and predicted values of the response and enumeration of prediction errors. Optimization of the process variables for maximum adsorption of CV by nanocomposite was performed using the quadratic model. The Langmuir adsorption capacity of the adsorbent was determined as 81.70 mg/g. The model predicted maximum adsorption of 113.31 mg/g under the optimum conditions of variables (concentration 240 mg/l; temperature 50°C; pH 8.50; dose 1g/l), which was very close to the experimental value (111.80 mg/g) determined in batch experiment.

Support vector machines in water quality management

Support vector classification (SVC) and regression (SVR) models were constructed and applied to the surface water quality data to optimize the monitoring program. The data set comprised of 1500 water samples representing 10 different sites monitored for 15 years. The objectives of the study were to classify the sampling sites (spatial) and months (temporal) to group the similar ones in terms of water quality with a view to reduce their number; and to develop a suitable SVR model for predicting the biochemical oxygen demand (BOD) of water using a set of variables. The spatial and temporal SVC models rendered grouping of 10 monitoring sites and 12 sampling months into the clusters of 3 each with misclassification rates of 12.39% and 17.61% in training, 17.70% and 26.38% in validation, and 14.86% and 31.41% in test sets, respectively. The SVR model predicted water BOD values in training, validation, and test sets with reasonably high correlation (0.952, 0.909, and 0.907) with the measured values, and low root mean squared errors of 1.53, 1.44, and 1.32, respectively. The values of the performance criteria parameters suggested for the adequacy of the constructed models and their good predictive capabilities. The SVC model achieved a data reduction of 92.5% for redesigning the future monitoring program and the SVR model provided a tool for the prediction of the water BOD using set of a few measurable variables. The performance of the nonlinear models (SVM, KDA, KPLS) was comparable and these performed relatively better than the corresponding linear methods (DA, PLS) of classification and regression modeling.

Modeling and optimization of reductive degradation of chloramphenicol in aqueous solution by zero-valent bimetallic nanoparticles

PurposeThe present study aims to investigate the individual and combined effects of temperature, pH, zero-valent bimetallic nanoparticles (ZVBMNPs) dose, and chloramphenicol (CP) concentration on the reductive degradation of CP using ZVBMNPs in aqueous medium.MethodIron–silver ZVBMNPs were synthesized. Batch experimental data were generated using a four-factor statistical experimental design. CP reduction by ZVBMNPs was optimized using the response surface modeling (RSM) and artificial neural network-genetic algorithm (ANN-GA) approaches. The RSM and ANN methodologies were also compared for their predictive and generalization abilities using the same training and validation data set. Reductive by-products of CP were identified using liquid chromatography-mass spectrometry technique.ResultsThe optimized process variables (RSM and ANN-GA approaches) yielded CP reduction capacity of 57.37 and 57.10xa0mgxa0g−1, respectively, as compared to the experimental value of 54.0xa0mgxa0g−1 with un-optimized variables. The ANN-GA and RSM methodologies yielded comparable results and helped to achieve a higher reduction (>6%) of CP by the ZVBMNPs as compared to the experimental value. The root mean squared error, relative standard error of prediction and correlation coefficient between the measured and model-predicted values of response variable were 1.34, 3.79, and 0.964 for RSM and 0.03, 0.07, and 0.999 for ANN models for the training and 1.39, 3.47, and 0.996 for RSM and 1.25, 3.11, and 0.990 for ANN models for the validation set.ConclusionPredictive and generalization abilities of both the RSM and ANN models were comparable. The synthesized ZVBMNPs may be used for an efficient reductive removal of CP from the water.

Predicting adsorptive removal of chlorophenol from aqueous solution using artificial intelligence based modeling approaches

The research aims to develop artificial intelligence (AI)-based model to predict the adsorptive removal of 2-chlorophenol (CP) in aqueous solution by coconut shell carbon (CSC) using four operational variables (pH of solution, adsorbate concentration, temperature, and contact time), and to investigate their effects on the adsorption process. Accordingly, based on a factorial design, 640 batch experiments were conducted. Nonlinearities in experimental data were checked using Brock–Dechert–Scheimkman (BDS) statistics. Five nonlinear models were constructed to predict the adsorptive removal of CP in aqueous solution by CSC using four variables as input. Performances of the constructed models were evaluated and compared using statistical criteria. BDS statistics revealed strong nonlinearity in experimental data. Performance of all the models constructed here was satisfactory. Radial basis function network (RBFN) and multilayer perceptron network (MLPN) models performed better than generalized regression neural network, support vector machines, and gene expression programming models. Sensitivity analysis revealed that the contact time had highest effect on adsorption followed by the solution pH, temperature, and CP concentration. The study concluded that all the models constructed here were capable of capturing the nonlinearity in data. A better generalization and predictive performance of RBFN and MLPN models suggested that these can be used to predict the adsorption of CP in aqueous solution using CSC.

Large-scale separation of antipsychotic alkaloids from Rauwolfia tetraphylla L. by pH-zone-refining fast centrifugal partition chromatography.

pH-zone-refining centrifugal partition chromatography was successively applied in the large-scale separation of close R(f) antipsychotic indole alkaloids directly from CHCl(3) fraction of Rauwolfia tetraphylla leaves. Two experiments with increasing mass from 500 mg to 3 g of crude alkaloid extracts (1C) of R. tetraphylla were carried out in normal-displacement mode using a two-phase solvent system composed of methyl tert-butyl ether/ACN/water (4:1:5, v/v/v) where HCl (12 mM) was added to the lower aqueous stationary phase as a retainer and triethylamine (5 mM) to the organic mobile phase as an eluter. The two centrifugal partition chromatography separations afforded a total of 162.6 mg of 10-methoxytetrahydroalstonine (1) and 296.5 mg of isoreserpiline (2) in 97% and 95.5% purity, respectively, along with a 400.9 mg mixture of α-yohimbine and reserpiline (3 and 4). Further, this mixture was resolved over medium pressure LC using TLC grade silica gel H (average particle size 10 μm), which afforded 160.4 mg of α-yohimbine (3) and 150.2 mg of reserpiline (4) in >95% purities. The purity of the isolated antipsychotic alkaloids was analyzed by high-performance LC and their structures were characterized on the basis of their 1D, 2D NMR and electrospray ionization-mass spectroscopic data.

Multi-target QSTR modeling for simultaneous prediction of multiple toxicity endpoints of nano-metal oxides

Abstract The metal oxide nanoparticles (MeONPs) due to their unique physico-chemical properties have widely been used in different products. Current studies have established toxicity of some NPs to human and environment, hence, imply for their comprehensive safety assessment. Here, the potential of using a multi-target QSTR modeling for simultaneous prediction of multiple toxicity endpoints of various MeONPs has been investigated. A multi-target QSTR model has been established using four different experimental toxicity data sets of MeONPs. Diversity of the considered experimental toxicity data sets was tested using the Kruskal–Wallis (K–W) statistics. The optimal validated model yielded high correlations (R2 between 0.828 and 0.956) between the experimental and simultaneously predicted endpoint toxicity values in test arrays for all the four systems. The structural features (oxygen percent, LogS, and Mulliken’s electronegativity) identified by the QSTR model were mechanistically interpretable in view of the accepted toxicity mechanisms for NPs. Single target QSTR models were also established (R2Testu2009>0.882) for individual toxicity endpoint prediction of MeONPs. The performance of the multi-target QSTR model was closely comparable with individual models and with those reported earlier in the literature for toxicity prediction of NPs. The model reliably predicts the toxicity of all considered MeONPs, and the methodology is expected to provide guidance for the future design of safe NP-based products. The proposed multi-target QSTR can be successfully used for screening new, untested metal oxide NPs for their safety assessment within the defined applicability domain of the model.

Predicting dissolved oxygen concentration using kernel regression modeling approaches with nonlinear hydro-chemical data

Kernel function-based regression models were constructed and applied to a nonlinear hydro-chemical dataset pertaining to surface water for predicting the dissolved oxygen levels. Initial features were selected using nonlinear approach. Nonlinearity in the data was tested using BDS statistics, which revealed the data with nonlinear structure. Kernel ridge regression, kernel principal component regression, kernel partial least squares regression, and support vector regression models were developed using the Gaussian kernel function and their generalization and predictive abilities were compared in terms of several statistical parameters. Model parameters were optimized using the cross-validation procedure. The proposed kernel regression methods successfully captured the nonlinear features of the original data by transforming it to a high dimensional feature space using the kernel function. Performance of all the kernel-based modeling methods used here were comparable both in terms of predictive and generalization abilities. Values of the performance criteria parameters suggested for the adequacy of the constructed models to fit the nonlinear data and their good predictive capabilities.

In Silico Strategy for Diagnosis of Chronic Kidney Disease

Chronic kidney disease (CKD) is the third deadliest reason for mortality worldwide. An early detection of CKD would help to decelerate the loss of kidney function. Computational approaches provide opportunities to screen large populations for diagnosis of CKD. In this study, qualitative and quantitative models were developed to discriminate CKD and non-CKD subjects and to predict serum creatinine (SC) levels in populations using three simple clinical attributes as the predictors. The models were rigorously validated using stringent statistical coefficients, and applicability domains were also determined. The qualitative models yielded a binary classification accuracy >94% in test data, whereas, the quantitative models rendered a correlation (R2) of >0.94 in the test data. Values of all the statistical checks were within their respective thresholds, thus putting a high confidence in the proposed models. The proposed models can be used as the tools for screening large populations for their renal status. KeywoRDS Applicability Domain, Chronic Kidney Disease, Clinical Attributes, Ensemble Machine Learning, Qualitative Model, Quantitative Model, Serum Creatinine