Predicting rejection of emerging contaminants through RO membrane filtration based on ANN-QSAR modeling approach: trends in molecular descriptors and structures towards rejections.

In this work, a quantitative structure-activity relationship (QSAR) study was performed on a set of emerging contaminants (ECs) to predict their rejections by reverse osmosis membrane (RO). A wide range of molecular descriptors was calculated by Dragon software for 72 ECs. The QSAR data was analyzed by an artificial neural network method (ANN), in which four out of 3000 theoretical molecular descriptors were chosen and their significance was computed based on the Garson method. The significance trends of descriptors were as follows in descending order: ESpm14u > R2e > SIC1 > EEig03d. The selected descriptors were ranked based on their importance and then an explorative study was conducted on the QSAR data to show the trends in molecular descriptors and structures toward the rejections values of ECs. The MLR algorithm was used to make a linear model and the results were compared with those of the nonlinear ANN algorithm. The comparison results revealed it is necessary to apply the ANN model to this data with non-linear properties. For the whole dataset, the correlation coefficient (R2) and residual mean squared error (RMSE) of the ANN and MLR methods were 0.9528, 6.4224; and 0.8753, 11.3400, respectively. The comparison results showed the superiority of ANN modeling in the analysis of ECs' QSAR data.

Predicting the aggregation number of cationic surfactants based on ANN-QSAR modeling approaches: understanding the impact of molecular descriptors on aggregation numbers.

In this work, a quantitative structure-activity relationship (QSAR) study is performed on some cationic surfactants to evaluate the relationship between the molecular structures of the compounds with their aggregation numbers (AGGNs) in aqueous solution at 25 °C. An artificial neural network (ANN) model is combined with the QSAR study to predict the aggregation number of the surfactants. In the ANN analysis, four out of more than 3000 molecular descriptors were used as input variables, and the complete set of 41 cationic surfactants was randomly divided into a training set of 29, a test set of 6, and a validation set of 6 molecules. After that, a multiple linear regression (MLR) analysis was utilized to build a linear model using the same descriptors and the results were compared statistically with those of the ANN analysis. The square of the correlation coefficient (R 2) and root mean square error (RMSE) of the ANN and MLR models (for the whole data set) were 0.9392, 7.84, and 0.5010, 22.52, respectively. The results of the comparison revealed the efficiency of ANN in detecting a correlation between the molecular structure of surfactants and their AGGN values with a high predictive power due to the non-linearity in the studied data. Based on the ANN algorithm, the relative importance of the selected descriptors was computed and arranged in the following descending order: H-047 > ESpm12x > JGI6> Mor20p. Then, the QSAR data was interpreted and the impact of each descriptor on the AGGNs of the molecules were thoroughly discussed. The results showed there is a correlation between each selected descriptor and the AGGN values of the surfactants.

A novel magnetic ion imprinted polymer as a selective magnetic solid phase for separation of trace lead(II) ions from agricultural products, and optimization using a Box-Behnken design.

In this work, a magnetic ion-imprinted polymer (Fe3O4@SiO2@IIP) as a novel and selective nanosorbent for selective extraction of Pb(II) ions from various agricultural products is presented. The novel lead magnetic ion-imprinted polymer was synthesized by imidazole as a new ligand and grafted onto the surface of Fe3O4@SiO2 NPs. A Box-Behnken (BBD) design was used for optimization of the extraction and elution steps. In the selected conditions, the limit of detection was 0.48ngmL-1, preconcentration factor was 300, the sorption capacity of this new magnetic ion-imprinted polymer was 105mgg-1, and the precision of the method (RSD%) for six replicate measurements was found 3.2%. Finally, the feasibility of the new magnetic ion-imprinted polymer was evaluated by extraction and determination of trace Pb2+ ions in different agricultural products including (orange, mango, apple, kiwi, lettuce, broccoli, carrot, squash, eggplant, radish, mushroom, cucumber, and tomato).

Second order advantage obtained by spectroelectrochemistry along with novel carbon nanotube modified mesh electrode: Application for determination of acetaminophen in Novafen samples.

A matrix-augmentation multivariate curve resolution-alternating least-squares (MA-MCR-ALS) has been conducted on the spectroelectrochemical data of acetaminophen oxidation in order to quantify acetaminophen in Novafen capsule in the presence of unknown interferences. The experiments were carried out using new cheap mesh electrode, namely carbon nanotube modified mesh electrode (CNMME) as optically transparent thin layer electrode (OTTLE). For each sample, a second order spectroelectrochemical data was obtained and MA-MCR-ALS method was applied to analyze these data. Unlike full trilinear models such as PARAFAC, MCR-ALS is flexible in applying trilinearity constraint for each component, a fact which makes it manage deviations from trilinearity of data effectively. This method was employed in both spectral and kinetic augmentation mode of data under examining different trilinear components. However, spectral augmentation was the only setting which allows MA-MCR-ALS to solve the analytical problem achieving the second order advantage. Therefore, here the results of the augmentation in this mode have been described. In order to obtain the best analytical figures of merit in the analysis, different constraints were investigated. The results indicated the accuracy of the proposed method.

Quantifying aflatoxins in peanuts using fluorescence spectroscopy coupled with multi-way methods: Resurrecting second-order advantage in excitation-emission matrices with rank overlap problem.

A rapid, simple and inexpensive method using fluorescence spectroscopy coupled with multi-way methods for the determination of aflatoxins B1 and B2 in peanuts has been developed. In this method, aflatoxins are extracted with a mixture of water and methanol (90:10), and then monitored by fluorescence spectroscopy producing EEMs. Although the combination of EEMs and multi-way methods is commonly used to determine analytes in complex chemical systems with unknown interference(s), rank overlap problem in excitation and emission profiles may restrain the application of this strategy. If there is rank overlap in one mode, there are several three-way algorithms such as PARAFAC under some constraints that can resolve this kind of data successfully. However, the analysis of EEM data is impossible when some species have rank overlap in both modes because the information of the data matrix is equivalent to a zero-order data for that species, which is the case in our study. Aflatoxins B1 and B2 have the same shape of spectral profiles in both excitation and emission modes and we propose creating a third order data for each sample using solvent as a new additional selectivity mode. This third order data, in turn, converted to the second order data by augmentation, a fact which resurrects the second order advantage in original EEMs. The three-way data is constructed by stacking augmented data in the third way, and then analyzed by two powerful second order calibration methods (BLLS-RBL and PARAFAC) to quantify the analytes in four kinds of peanut samples. The results of both methods are in good agreement and reasonable recoveries are obtained.