Acrylamide content of collected food products from Tehran's market: a risk assessment study.

Acrylamide concentration in food products collected from the Tehran market was investigated by the aid of a dispersive liquid-liquid microextraction (DLLME) system coupled with gas chromatography-mass spectrometry (GC-MS). Also, the dietary exposure distribution and related potential risk for acrylamide ingestion were estimated by the Monte Carlo simulation (MCS). The highest and lowest mean concentration of acrylamide was detected in coffee and roasted nuts samples as 549 and 133 µg/kg, respectively. The mean acrylamide dietary exposure values for children (3-10 years), adolescents (11-17 years), adults (18-60 years), and seniors (61-96 years) were estimated to be 1.81, 1.02, 0.61, and 0.53 µg/kg body weight (BW)/day, respectively. In all age groups, except children, the estimated exposure in men and boys was higher than that in women and girls. Bread, despite containing low acrylamide content groups (157 µg/kg while compared with other, except roasted nuts), showed with the highest contribution rate in all age groups due to its high consumption rate. The estimated incremental lifetime cancer risk (ILCR) for all age groups was noted as greater than 10-4 indicating serious risk to the population. Moreover, the margin of exposure (MOE) values based on carcinogenicity showed health concern to all age groups (< 10,000). Regarding the non-carcinogenic risk, the target hazard quotient (THQ) was lower than 1, and MOE based on neurotoxicity was higher than 125 (safety thresholds), which represented negligible and ignorable risk in all age groups except in a small group of children and adolescents. Graphical abstract.

Voltammetric sensing of oxacillin by using a screen-printed electrode modified with molecularly imprinted polyaniline, gold nanourchins and graphene oxide.

An imprinted electrochemical sensor was developed for the determination of the antibiotic oxacillin (OXC). A screen-printed carbon electrode (SPCE) was modified with gold nanourchin and graphene oxide, and then aniline was electro-polymerized in the presence of OXC to obtain a molecular imprint on the SPCE. The morphologies in sequential modification processes and the electrochemical behavior of the modified SCPE were characterized by field emission scanning electron microscopy and cyclic voltammetry. The performance of the sensor was evaluated by differential pulse voltammetry. At a typical peak potential of 0.82 mV (vs. Ag/AgCl), response is linear in the 0.7-575 nM OXC concentration range. The electrochemical sensitivity is 97.6 nA nM -1 cm -2, and the detection limit is 0.2 nM. The relative of replicate assays is 2.6% (for n = 6) at an OXC concentration level of 200 nM. The sensor is sensitive and selective. It was successfully applied for the detection of OXC in spiked cow's milk. Schematic presentation of electropolymerization of aniline on sreen-printed carbon electrode (SPCE) modified with graphene oxide (GO) and gold nanouchins (GNU) for voltammetric sensing of oxacillin.

Deep Transferable Compound Representation across Domains and Tasks for Low Data Drug Discovery.

The main problem of small molecule-based drug discovery is to find a candidate molecule with increased pharmacological activity, proper ADME, and low toxicity. Recently, machine learning has driven a significant contribution to drug discovery. However, many machine learning methods, such as deep learning-based approaches, require a large amount of training data to form accurate predictions for unseen data. In lead optimization step, the amount of available biological data on small molecule compounds is low, which makes it a challenging problem to apply machine learning methods. The main goal of this study is to design a new approach to handle these situations. To this end, source assay (auxiliary assay) knowledge is utilized to learn a better model to predict the property of new compounds in the target assay. Up to now, the current approaches did not consider that source and target assays are adapted to different target groups with different compounds distribution. In this paper, we propose a new architecture by utilizing graph convolutional network and adversarial domain adaptation network to tackle this issue. To evaluate the proposed approach, we applied it to Tox21, ToxCast, SIDER, HIV, and BACE collections. The results showed the effectiveness of the proposed approach in transferring the related knowledge from source to target data set.

A new polymerizable fluorescent PET chemosensor of fluoride (F-) based on naphthalimide-thiourea dye.

A novel N-allyl-4-amino-substituted 1,8-naphthalimide dye, containing thiourea functional group with intense yellow-green fluorescence was successfully synthesized. Copolymerization was done with styrene. The photophysical characteristics of dye and its copolymer in solution and solid film were investigated in the presence of halide ions. The results reveal that the fluorescence emissions of the monomer dye and also its polymer were 'switched off' in the presence of fluoride ions. The dye showed spectral shifts and intensity changes in the presence of more fluoride ions which lead to detect certain fluoride concentrations of 10-150 mM at visible wavelengths. By adding the fluoride ions, green-yellow to purple color changes occurs and the green fluorescence emission quenches, all of which easily observed by naked eyes. These phenomena are essential for producing a dual responsive chemosensor for fluoride ions. The polymeric sensor, in the film state exhibited a fast response to the fluoride ions.

Spectrophotometric determination of trace amounts of fluoride using an Al-xylenol orange complex as a colored reagent.

A new spectrophotometric reagent for the determination of trace amounts of fluoride has been introduced. This method is based on the decolorization of a complex of Al(III) with xylenol orange (XO) as an ultra-sensitive colored reagent. Since the Al-XO complex plays an important role in this method, the protonation and complexation of XO with Al(III) at an ionic strength of 0.1 mol L(-1) at 25 degrees C has been studied by a spectrophotometric global analysis method. The EQUISPEC program was used to evaluate the protonation constants of XO and the stability constants of the formed complexes with Al(III). The protonation and the stability constants of the major complex species such as ML, MLH and MLH2, were determined. Finally, a spectrophotometric method for the assay of fluoride based on a decrease of the color intensity of the Al-XO complex, in an aqueous solution has been designed. The effects of some important variables on the determination of fluoride based on the proposed method were investigated. The method was applied to the determination of fluoride under the optimum conditions (pH 5.2, ionic strength 0.1 mol L(-1), 25 degrees C). The determination of fluoride in the range of 0.08-1.4 microg mL(-1) (SD = 1.2%) was successfully performed. Interferences of Fe(III) were easily eliminated by using ascorbic acid. The proposed method was applied to the determination of trace amounts of fluoride content of some real water samples.

QSRR prediction of the chromatographic retention behavior of painkiller drugs.

Quantitative structure-retention relationship (QSRR) analysis is a useful technique capable of relating chromatographic retention time to the chemical structure of a solute. A QSRR study has been carried out on the reversed-phase high-performance liquid chromatography retention times (log tR) of 62 diverse drugs (painkillers) by using molecular descriptors. Multiple linear regression (MLR) is utilized to construct the linear QSRR model. The applied MLR is based on a variety of theoretical molecular descriptors selected by the stepwise variable subset selection procedure. Stepwise regression was employed to develop a regression equation based on 50 training compounds, and predictive ability was tested on 12 compounds reserved for that purpose. The geometry of all drugs was optimized by the semi-empirical method AM1 and used to calculate different molecular descriptors. The regression equation included three parameters: n-octanol-water partition coefficient (log P), molecular surface area, and hydrophilic-lipophilic balance of the drug molecules, all of which could be related to retention time property. Modeling of retention times of these compounds as a function of the theoretically derived descriptors was established by MLR. The results indicate that a strong correlation exists between the log tR and the previously mentioned descriptors for drug compounds. The prediction results are in good agreement with the experimental values.

Kinetic spectrophotometric determination of trace amounts of palladium by whole kinetic curve and a fixed time method using resazurine sulfide reaction.

The univariate and multivariate calibration methods were applied for the determination of trace amounts of palladium based on the catalytic effect on the reaction between resazurine and sulfide. The decrease in absorbance of resazurine at 602 nm over a fixed time is proportional to the concentration of palladium over the range of 10.0-160.0 ng mL(-1). The calibration matrix for partial least squares (PLS) regression was designed with 14 samples. Orthogonal signal correction (OSC) is a preprocessing technique used for removing the information unrelated to the target variables based on constrained principal component analysis. OSC is a suitable preprocessing method for PLS calibration without loss of prediction ability using spectrophotometric method. The root mean square error of prediction (RMSEP) for palladium determination with fixed-time, PLS and OSC-PLS were 3.71, 2.84 and 0.68, respectively. This procedure allows the determination of palladium in synthetic and real samples with good reliability of the determination.

Quantitative structure-property relationship study of n-octanol-water partition coefficients of some of diverse drugs using multiple linear regression.

A quantitative structure-property relationship (QSPR) study was performed to develop models those relate the structures of 150 drug organic compounds to their n-octanol-water partition coefficients (logP(o/w)). Molecular descriptors derived solely from 3D structures of the molecular drugs. A genetic algorithm was also applied as a variable selection tool in QSPR analysis. The models were constructed using 110 molecules as training set, and predictive ability tested using 40 compounds. Modeling of logP(o/w) of these compounds as a function of the theoretically derived descriptors was established by multiple linear regression (MLR). Four descriptors for these compounds molecular volume (MV) (geometrical), hydrophilic-lipophilic balance (HLB) (constitutional), hydrogen bond forming ability (HB) (electronic) and polar surface area (PSA) (electrostatic) are taken as inputs for the model. The use of descriptors calculated only from molecular structure eliminates the need for experimental determination of properties for use in the correlation and allows for the estimation of logP(o/w) for molecules not yet synthesized. Application of the developed model to a testing set of 40 drug organic compounds demonstrates that the model is reliable with good predictive accuracy and simple formulation. The prediction results are in good agreement with the experimental value. The root mean square error of prediction (RMSEP) and square correlation coefficient (R2) for MLR model were 0.22 and 0.99 for the prediction set logP(o/w).

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.

QSPR prediction of aqueous solubility of drug-like organic compounds.

A quantitative structure property relationship (QSPR) study was performed to develop a model that relates the structures of 150 drug organic compounds to their aqueous solubility (log S(w)). Molecular descriptors derived solely from 3D structure were used to represent molecular structures. A subset of the calculated descriptors selected using stepwise regression that used in the QSPR model development. Multiple linear regression (MLR) is utilized to construct the linear QSPR model. The applied multiple linear regression is based on a variety of theoretical molecular descriptors selected by the stepwise variable subset selection procedure. Stepwise regression was employed to develop a regression equation based on 110 training compounds, and predictive ability was tested on 40 compounds reserved for that purpose. The final regression equation included three parameters that consisted of octanol/water partition coefficient (log P), molecular volume (MV) and hydrogen bond forming ability (HB), of the drug molecules, all of which could be related to solubility property. Application of the developed model to a testing set of 40 drug organic compounds demonstrates that the new model is reliable with good predictive accuracy and simple formulation. The use of descriptors calculated only from molecular structure eliminates the need for experimental determination of properties for use in the correlation and allows for the estimation of aqueous solubility for molecules not yet synthesized. The prediction results are in good agreement with the experimental values. The root mean square error of prediction (RMSEP) and square correlation coefficient (R(2)) of prediction of log S(w) were 0.0959 and 0.9954, respectively.

Simultaneous spectrophotometric determination of nitroaniline isomers after cloud point extraction by using least-squares support vector machines.

Cloud point extraction has been used for the preconcentration of m-nitroaniline, o-nitroaniline and p-nitroaniline and later simultaneous spectrophotometric determination using polyethylene glycol tert-octylphenyl ether (Triton X-100) as surfactant. The resolution of a ternary mixture of the nitroaniline isomers (after extraction by cloud point) by the application of least-squares support vector machines (LS-SVM) was performed. The chemical parameters affecting the separation phase and detection process were studied and optimized. Under the optimum experimental conditions (i.e. pH 7.0, Triton X-100=0.6%, equilibrium time 20 min and cloud point 75 degrees C), calibration graphs were linear in the range of 0.2-20.0, 0.1-15.0 and 0.1-17.0 microg ml(-1) with detection limits of 0.08, 0.05 and 0.06 microg ml(-1) for m-nitroaniline, o-nitroaniline and p-nitroaniline, respectively. The experimental calibration matrix was designed with 21 mixtures of these chemicals. The concentrations were varied between calibration graphs concentrations of nitroaniline isomers. The root mean square error of prediction (RMSEP) for m-nitroaniline, o-nitroaniline and p-nitroaniline were 0.0146, 0.0308 and 0.0304, respectively. This procedure allows the simultaneous determination of nitroaniline isomers in synthetic and real matrix samples good reliability of the determination was proved.

Prediction of gas chromatography/electron capture detector retention times of chlorinated pesticides, herbicides, and organohalides by multivariate chemometrics methods.

A quantitative structure-retention relationship (QSRR) study, has been carried out on the gas chromatograph/electron capture detector (GC/ECD) system retention times (t(R)s) of 38 diverse chlorinated pesticides, herbicides, and organohalides by using molecular structural descriptors. Modeling of retention times of these compounds as a function of the theoretically derived descriptors was established by multiple linear regression (MLR) and partial least squares (PLS) regression. The stepwise regression using SPSS was used for the selection of the variables that resulted in the best-fitted models. Appropriate models with low standard errors and high correlation coefficients were obtained. Three types of molecular descriptors including electronic, steric and thermodynamic were used to develop a quantitative relationship between the retention times and structural properties. MLR and PLS analysis has been carried out to derive the best QSRR models. After variables selection, MLR and PLS methods used with leave-one-out cross validation for building the regression models. The predictive quality of the QSRR models were tested for an external prediction set of 12 compounds randomly chosen from 38 compounds. The PLS regression method was used to model the structure-retention relationships, more accurately. However, the results surprisingly showed more or less the same quality for MLR and PLS modeling according to squared regression coefficients R2 which were 0.951 and 0.948 for MLR and PLS, respectively.

Simultaneous voltammetric determination of morphine and noscapine by adsorptive differential pulse stripping method and least-squares support vector machines.

An adsorptive differential pulse stripping method for the simultaneous determination of morphine and noscapine is proposed. The procedure involves an adsorptive accumulation of morphine and noscapine on a hanging mercury drop electrode (HMDE), followed by oxidation of adsorbed morphine and noscapine by voltammetric scan using differential pulse modulation. The optimum experimental conditions are: pH 10.0, accumulation potential of -100 mV versus Ag/AgCl, accumulation time of 150 s, scan rate of 40 mV s(-1) and pulse height of 100 mV. Morphine and noscapine peak currents were observed in same potential region at about +0.25 V. The simultaneous determination of morphine and noscapine by using voltammetry is a difficult problem in analytical chemistry, due to voltammogram interferences. The resolution of mixture of morphine and noscapine by the application of least-squares support vector machines (LS-SVM) was performed. The linear dynamic ranges were 0.01-3.10 and 0.015-2.75 microg mL(-1) and detection limits were 3 and 7 ng mL(-1) for morphine and noscapine, respectively. The capability of the method for the analysis of real samples was evaluated by the determination of morphine and noscapine in addict's human plasma with satisfactory results.

Development of a model to predict partition coefficient of organic pollutants in cloud point extraction process.

A quantitative structure property relationship (QSPR) study has been performed to establish a model to relate structural descriptors of 45 organic compounds to their partition coefficients in water-hexadecylpyridinium chloride (CPC) micelles at 298K using partial least squares (PLS). 510 of six different categories of structural descriptors were calculated by Dragon software. The descriptors with 0.9 mutually pair correlations and with less than 0.1 with dependent variables were excluded at the early stage of the preprocessing of the structural data matrix. The data set was randomly divided into two groups: training set (40 molecules) and test set (5 molecules). In the final model 50 of the most effective of the structural descriptors on the partition coefficient were remained to model building by PLS calibration method. The optimum number of latent variables 5, which spanned 80% of the original variations of data matrix, was selected using leave one out cross validation method. Prediction ability of the model was tested by prediction of the partition coefficients of five unknown compounds and the mean relative error of prediction was 3.6%. The outliers were treated using leverage and score plots of the first third principal components. The efficiency of the new model was compared with Abraham model and it was found that the proposed model has more prediction ability.

Spectrophotometric and thermodynamic study on the dimerization equilibrium of ionic dyes in water by chemometrics method.

The monomer-dimer equilibrium and thermodynamic of several ionic dyes (Neutral Red, Nile Blue A, Safranine T and Thionine) has been investigated by means of spectrophotometric and chemometrics methods. The dimerization constants of these ionic dyes have been determined by studying the dependence of their absorption spectra on the temperature in the range 20-75 degrees C at concentrations of Neutral Red (1.73 x 10(-5) M), Nile Blue A (3.94 x 10(-5) M), Safranine (6.59 x 10(-5) M) and Thionine (6.60 x 10(-5) M). The monomer-dimer equilibrium of these dyes has been determined by chemometrics refinement of the absorption spectra obtained by thermometric titrations performed. The processing of the data carried out for quantitative analysis of undefined mixtures, based on simultaneous resolution of the overlapping bands in the whole set of absorption spectra. The enthalpy and entropy of the dimerization reactions were determined from the dependence of the equilibrium constants to the temperature (van't Hoff equation).

Spectrophotometric determination of acidity constants of alizarine red S in water, water-Brij-35 and water-SDS micellar media solutions.

The acidity constants of Alizarine Red S in water, water-Brij-35 and water-SDS micellar media solutions at 25 degrees C and an ionic strength of 0.1 M have been determined spectrophotometrically. To evaluate the pH-absorbance data, a resolution method based on the combination of soft- and hard-modeling is applied. The acidity constants of all related equilibria are estimated using the whole spectral fitting of the collected data to an established factor analysis model. DATAN program applied for determination of acidity constants. Results show that the pKa values of Alizarine Red S are influenced as the percentages of a neutral and an anionic surfactant such as Brij-35 and SDS, respectively, added to the solution of this reagent. Effect of surfactant on acidity constants and pure spectrum of each component are also discussed.

Thermodynamics study of the dimerization equilibria of rhodamine B and 6G in different ionic strengths by photometric titration and chemometrics method.

The dimerization constants of rhodamine B and 6G have been determined by studying the dependence of their absorption spectra on the temperature in the range 20-80 degrees C at different total concentrations of rhodamine B (5.89 x 10(-6) to 2.36 x 10(-4)M) and rhodamine 6G (2.34 x 10(-5) to 5.89 x 10(-4)M) and in different concentrations of LiCl, NaCl and KCl salts as supporting electrolytes. The monomer-dimer equilibrium of rhodamine B and 6G have been determined by chemometrics refinement of the absorption spectra obtained by thermometric titrations performed at different ionic strengths. The quantitative analysis of the data of undefined mixtures, was carried out by simultaneous resolution of the overlapping spectral bands in the whole set of absorption spectra. The dimerization constants are varied by changing the ionic strength and the degree of dimerization are decreased by increasing of the ionic strength of the medium. The enthalpy and entropy of the dimerization reactions were determined from the dependence of the equilibrium constants on the temperature (van't Hoff equation). From the thermodynamic results the TDeltaS degrees -DeltaH degrees plot was sketched. It shows a fairly good positive correlation which indicates the enthalpy-entropy compensation in the dimerization reactions (compensation effect).

Spectrophotometric simultaneous determination of nitroaniline isomers by orthogonal signal correction-partial least squares.

The simultaneous determination of nitroaniline isomer mixtures by using spectrophotometric methods is a difficult problem in analytical chemistry, due to spectral interferences. By multivariate calibration methods, such as partial least squares (PLS), it is possible to obtain a model adjusted to the concentration values of the mixtures used in the calibration range. Orthogonal signal correction (OSC) is a preprocessing technique used for removes the information unrelated to the target variables based on constrained principal component analysis. OSC is a suitable preprocessing method for partial least squares calibration of mixtures without loss of prediction capacity using spectrophotometric method. In this study, the calibration model is based on absorption spectra in the 200-500nm range for 21 different mixtures of nitroaniline isomers. Calibration matrices were containing 1-21, 1-15 and 1-18mugml(-1) of m-nitroaniline, o-nitroaniline and p-nitroaniline, respectively. The RMSEP for m-nitroaniline, o-nitroaniline and p-nitroaniline with OSC and without OSC were 0.6567, 0.2692, and 0.3134, and 1.3818, 1.2181, and 0.3953, respectively. This procedure allows the simultaneous determination of nitroaniline isomers in real matrix samples and good reliability of the determination was proved.

Genetic-algorithm-based wavelength selection in multicomponent spectrophotometric determination by PLS: application on copper and zinc mixture.

Genetic algorithm (GA) is a suitable method for selecting wavelengths for PLS (partial least squares) calibration of mixtures with almost identical spectra without loss of prediction capacity using spectrophotometric method. The method is based on the development of the reaction between the analytes and Zincon at pH 9. A series of synthetic solution containing different concentrations of copper and zinc were used to check the prediction ability of the GA-PLS models. The RMSD for copper and zinc with GA and without GA were 0.0407 and 0.0865, 0.2147 and 0.3005, respectively. Calibration matrices were 0.05-1.8 and 0.05-1.5 mug ml(-1) for copper and zinc, respectively. This procedure allows the simultaneous determination of cited ions in natural, tap and waste waters good reliability of the determination was proved.