A Bayesian Regularized Artificial Neural Network for Simultaneous Determination of Loratadine, Naproxen and Diclofenac in Wastewaters

2020 ◽  
Vol 16 (8) ◽  
pp. 1083-1092
Author(s):  
Mojtaba Mohammadpoor ◽  
Roya Mohammadzadeh Kakhki ◽  
Hakimeh Assadi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Simultaneous Determination ◽  
Back Propagation ◽  
Optimum Number ◽  
Mean Square ◽  
Vis Spectroscopy ◽  
Artificial Neural ◽  
Mean Square Errors

Background:: Simultaneous determination of medication components in pharmaceutical samples using ordinary methods have some difficulties and therefore these determinations usually were made by expensive methods and instruments. Chemometric methods are an effective way to analyze several components simultaneously. Objective:: In this paper, a novel approach based on Bayesian regularized artificial neural network is developed for the determination of Loratadine, Naproxen, and Diclofenac in water using UV-Vis spectroscopy. Methods: A dataset is collected by performing several chemical experiments and recording the UV-Vis spectra and actual constituent values. The effect of a different number of neurons in the hidden layer was analyzed based on final mean square error, and the optimum number was selected. Principle Component Analysis (PCA) was also applied to the data. Other back-propagation methods, such as Levenberg-Marquardt, scaled conjugate gradient, and resilient backpropagation, were tested. Results:: In order to see the proposed network performance, it was performed on two crossvalidation methods, namely partitioning data into train and test parts, and leave-one-out technique. Mean square errors between expected results and predicted ones implied that the proposed method has a strong ability in predicting the expected values. Conclusion:: he results showed that the Bayesian regularization algorithm has the best performance among other methods for simultaneous determination of Loratadine, Naproxen, and Diclofenac in water samples.

scholarly journals Simultaneous Determination of Some Components in Detergent Washing Powder by Mid-Infrared Spectrometry and Artificial Neural Network

2011 ◽  
Vol 94 (1) ◽  
pp. 322-326
Author(s):  
Mohammadreza Khanmohammadi ◽  
Amir Bagheri Garmarudi ◽  
Mohammad Babaei Rouchi ◽  
Nafiseh Khoddami
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Simultaneous Determination ◽  
Sodium Tripolyphosphate ◽  
Back Propagation ◽  
Infrared Spectrometry ◽  
Feed Forward Back Propagation ◽  
Powder Samples ◽  
Artificial Neural

Abstract A method has been established for simultaneous determination of sodium sulfate, sodium carbonate, and sodium tripolyphosphate in detergent washing powder samples based on attenuated total reflectance Fourier transform IR spectrometry in the mid-IR spectral region (800–1550 cm−1). Genetic algorithm (GA) wavelength selection followed by feed forward back-propagation artificial neural network (BP-ANN) was the chemometric approach. Root mean square error of prediction for BP-ANN and GA-BP-ANN was 0.0051 and 0.0048, respectively. The proposed method is simple, with no tedious pretreatment step, for simultaneous determination of the above-mentioned components in commercial washing powder samples.

scholarly journals Artificial Neural Network Modelling of Photodegradation in Suspension of Manganese Doped Zinc Oxide Nanoparticles under Visible-Light Irradiation

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Yadollah Abdollahi ◽  
Azmi Zakaria ◽  
Nor Asrina Sairi ◽  
Khamirul Amin Matori ◽  
Hamid Reza Fard Masoumi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Visible Light ◽  
Visible Light Irradiation ◽  
Back Propagation ◽  
Light Irradiation ◽  
Maximum Efficiency ◽  
Data Set ◽  
Artificial Neural

The artificial neural network (ANN) modeling ofm-cresol photodegradation was carried out for determination of the optimum and importance values of the effective variables to achieve the maximum efficiency. The photodegradation was carried out in the suspension of synthesized manganese doped ZnO nanoparticles under visible-light irradiation. The input considered effective variables of the photodegradation were irradiation time, pH, photocatalyst amount, and concentration ofm-cresol while the efficiency was the only response as output. The performed experiments were designed into three data sets such as training, testing, and validation that were randomly splitted by the software’s option. To obtain the optimum topologies, ANN was trained by quick propagation (QP), Incremental Back Propagation (IBP), Batch Back Propagation (BBP), and Levenberg-Marquardt (LM) algorithms for testing data set. The topologies were determined by the indicator of minimized root mean squared error (RMSE) for each algorithm. According to the indicator, the QP-4-8-1, IBP-4-15-1, BBP-4-6-1, and LM-4-10-1 were selected as the optimized topologies. Among the topologies, QP-4-8-1 has presented the minimum RMSE and absolute average deviation as well as maximum R-squared. Therefore, QP-4-8-1 was selected as final model for validation test and navigation of the process. The model was used for determination of the optimum values of the effective variables by a few three-dimensional plots. The optimum points of the variables were confirmed by further validated experiments. Moreover, the model predicted the relative importance of the variables which showed none of them was neglectable in this work.

scholarly journals Estimation of Contact Tip to Work Distance (CTWD) using Artificial Neural Network (ANN) in GMAW

2019 ◽  
Vol 269 ◽  
pp. 04004
Author(s):  
Fuad Mahfudianto ◽  
Eakkachai Warinsiriruk ◽  
Sutep Joy-A-Ka
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Moving Average ◽  
Back Propagation ◽  
Welding Current ◽  
Travel Speed ◽  
Mean Square ◽  
Wire Electrode ◽  
Marquardt Algorithm ◽  
Artificial Neural

A method for optimizing monitoring by using Artificial Neural Network (ANN) technique was proposed based on instability of arc voltage signal and welding current signal of solid wire electrode (GMAW). This technique is not only for effective process modeling, but also to illustrate the correlation between the input and output parameters responses. The algorithms of monitoring were developed in time domain by carrying out the Moving Average (M.A) and Root Mean Square (RMS) based on the welding experiment parameters such as travel speed, thickness of specimen, feeding speed, and wire electrode diameter to detect and estimate with a satisfactory sample size. Experiment data was divided into three subsets: train (70%), validation (15%), and test (15%). Error back-propagation of Levenberg-Marquardt algorithm was used to train for this algorithm. The proposed algorithms on this paper were used to estimate the variety the Contact Tip to Work Distance (CTWD) through Mean Square Error (MSE). Based on the results, the algorithms have shown that be able to detect changes in CTWD automatically and real time with takes 0.147 seconds (MSE 0.0087).

Simultaneous Quantitative Analysis of Clorsulon and Ivermectin in a Veterinary Formulation by Artificial Neural Network

2008 ◽  
Vol 59 (10) ◽  
Author(s):  
Gozde Pektas ◽  
Erdal Dinc ◽  
Dumitru Baleanu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Transfer Function ◽  
Back Propagation ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Simultaneaous spectrophotometric determination of clorsulon (CLO) and invermectin (IVE) in commercial veterinary formulation was performed by using the artificial neural network (ANN) based on the back propagation algorithm. In order to find the optimal ANN model various topogical networks were tested by using different hidden layers. A logsig input layer, a hidden layer of neurons using the logsig transfer function and an output layer of two neurons with purelin transfer function was found suitable for basic configuration for ANN model. A calibration set consisting of CLO and IVE in calibration set was prepared in the concentration range of 1-23 �g/mL and 1-14 �g/mL, repectively. This calibration set contains 36 different synthetic mixtures. A prediction set was prepared in order to evaluate the recovery of the investigated approach ANN chemometric calibration was applied to the simultaneous analysis of CLO and IVE in compounds in a commercial veterinary formulation. The experimental results indicate that the proposed method is appropriate for the routine quality control of the above mentioned active compounds.

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