Multicomponent acid-base titration by principal component-artificial neural network calibration

In this study, a three-layered feed-forward artificial neural network (ANN) trained by back-propagation learning was used to model the complex non-linear relationship between the concentration of anthranilic acid (HA), nicotinic acid (HN), picolinic acid (HP) and sulfanilic acid (HS) in their quaternary mixtures and the pH of solutions at different volumes of the added titrant. The principal components of the pH matrix were used as the input of the network. The network architecture and parameters were optimized to give low prediction error. The optimized networks predicted the concentrations of acids in synthetic mixtures. The results showed that the ANN used can proceed the titration data with low percent relative error of prediction (i.e.<4%). A comparison between the ANN and PLS methods revealed the superiority of the results obtained by the former method.     

Magnetic recyclable eggshell-based mesoporous catalyst for biodiesel production from crude neem oil: Process optimization by central composite design and artificial neural network

A magnetically recyclable eggshell-based catalyst (MKEC) was synthesized to circumvent saponification during the conversion of neem, Jatropha, and waste cooking oils (free fatty acid, 2.3–6.6%) to biodiesel. The characterization results indicated that MKEC had a mesoporous structure with the pore width of 3.24 nm, a specific surface area of 128 m²/g, and a pore volume of 0.045 cm³/g. The results confirmed that the MKEC is more tolerant to fatty acid poisoning than calcined eggshell. The effects of process parameters for maximum fatty acid methyl ester (FAME) content were evaluated by central composite design (CCD) and artificial neural network (ANN). The experimental FAME content of 94.5% was achieved for neem oil with a standard deviation (SD) of 0.68, which was in reasonable agreement with predicted values (CCD, 96.9%; ANN, 95.9%; SD, 0.73). The reusability studies showed that the mesoporous catalyst can be reused efficiently for five cycles without much deterioration in its activity.     

Simultaneous dual wavelength spectrophotometric determination of citric and ascorbic acids using an artificial neural network

In this study, a very simple spectrophotometric method for the simultaneous determination of citric and ascorbic acid based on the reaction of these acids with a copper(II)-ammonia complex is presented. The Cu²⁺-NH₃ complex (with λ_max = 600 nm) was decomposed by citrate ion and formed a Cu²⁺-citrate complex (with λ_max = 740 nm). On the other hand, during the reaction of ascorbic acid with copper(II)-ammonia complex, ascorbic acid is oxidized and the copper(II)-ammonia complex is reduced to the copper(I)-ammonia complex and the absorbance decreases to 600 nm. Although there is a spectral overlap between the absorbance spectra of complexes Cu²⁺-NH₃ and Cu²⁺-citrate, they have been simultaneously determined using an artificial neural network (ANN). The absorbances at 600 and 740 nm were used as the input layer. The ANN architectures were different for citric and ascorbic acid. The output of the citric acid ANN architecture was used as an input node for the ascorbic acid ANN architecture. This modification improves the capability of the ascorbic acid ANN model for the prediction of ascorbic acid concentrations. The dynamic ranges for citric and ascorbic acid were 1.0–125.0 and 1.0–35.0 mM, respectively. Finally, the proposed method was successfully applied to the determination of citric and ascorbic acids in vitamin C tablets and some powdered drink mixes. The text was submitted by the authors in English.     

Multivariate optimization in the biosynthesis of a triethanolamine (TEA)-based esterquat cationic surfactant using an artificial neural network

An Artificial Neural Network (ANN) based on the Quick Propagation (QP) algorithm was used in conjunction with an experimental design to optimize the lipase-catalyzed reaction conditions for the preparation of a triethanolamine (TEA)-based esterquat cationic surfactant. Using the best performing ANN, the optimum conditions predicted were an enzyme amount of 4.77 w/w%, reaction time of 24 h, reaction temperature of 61.9 °C, substrate (oleic acid: triethanolamine) molar ratio of 1:1 mole and agitation speed of 480 r.p.m. The relative deviation percentage under these conditions was less than 4%. The optimized method was successfully applied to the synthesis of the TEA-based esterquat cationic surfactant at a 2,000 mL scale. This method represents a more flexible and convenient means for optimizing enzymatic reaction using ANN than has been previously reported by conventional methods.     

Prediction of Rheological Properties of Multi-Component Dispersions by Using Artificial Neural Networks

This article shows the ability of artificial neural network (ANN) technology for predicting the correlation between rheological properties of multi-component food model systems and their chemical compositions. Multi-component food model systems were made of whey protein isolate (WPI) (2, 4 wt%), Iranian tragacanth gum (TG) (Astragalus gossypinus) (0.5, 1 wt%) and oleic acid (5, 10% v/v). The input parameters of the neural networks (NN) were these chemical compositions, namely WPI and TG concentrations, and oleic acid volume fractions. The output parameters of the NN models were rheological properties of multi-component food model systems (flow and consistency indices, viscosity, loss and storage moduli). Results showed that, ANN with training algorithm of back propagation (BP) was the best one for the creation of nonlinear mapping between input and output parameters. The best topology was 3-10-5. The ANN model predicted the rheological properties of multi-component food model systems with average RMSE 4.529 and average MAE 3.018. These results show that the ANN can potentially be used to estimate rheological parameters of multi-component food model systems from chemical composition. This development may have significant potential to improve product quality control and reduce time and costs by minimizing the rheological experiments.     

Modeling of Swelling Behaviors of Acrylamide-Based Polymeric Hydrogels by Intelligent System

Hydrogels based on acrylamide (AAm) were synthesized by free radical polymerization in an aqueous solution using N,N’-methylenebisacrylamide (MBAAm) as crosslinker. To obtain anionic hydrogels, 2-acrylamido-2-methylpropanesulfonic acid sodium salt (AMPS) and acrylic acid (AAc) were used as comonomers. The swelling behaviors of all hydrogel systems were modeled using an artificial neural network (ANN) and compared with a multivariable least squares regression (MLSR) model and phenomenal model. The predictions from the ANN model, which associated input parameters, including the amounts of crosslinker (MBA) and comonomer, and swelling values with time, produce results that show excellent correlation with experimental data. The parameters of swelling kinetics and water diffusion mechanisms of the hydrogels were calculated using the obtained experimental data. Model analysis indicated that the ANN models could accurately describe complex swelling behaviors of highly swellable hydrogels.     

Optimization of artificial neural network for retention modeling in high-performance liquid chromatography

An artificial neural network (ANN) model for the prediction of retention times in high-performance liquid chromatography (HPLC) was developed and optimized. A three-layer feed-forward ANN has been used to model retention behavior of nine phenols as a function of mobile phase composition (methanol-acetic acid mobile phase). The number of hidden layer nodes, number of iteration steps and the number of experimental data points used for training set were optimized. By using a relatively small amount of experimental data (25 experimental data points in the training set), a very accurate prediction of the retention (percentage normalized differences between the predicted and the experimental data less than 0.6%) was obtained. It was shown that the prediction ability of ANN model linearly decreased with the reduction of number of experiments for the training data set. The results obtained demonstrate that ANN offers a straightforward way for retention modeling in isocratic HPLC separation of a complex mixture of compounds widely different in pK_a and log K_ow values.     

Definitive Screening Design and Artificial Neural Network for Modeling a Rapid Biodegradation of Date Palm Fronds by a New Trichoderma sp. PWN6 into Citric Acid

Generally, the bioconversion of lignocellulolytics into a new biomolecule is carried out through two or more steps. The current study used one-step bioprocessing of date palm fronds (DPF) into citric acid as a natural product, using a pioneer strain of Trichoderma harzianum (PWN6) that has been selected from six tested isolates based on the highest organic acid (OA) productivity (195.41 µmol/g), with the lowest amount of the released glucose. Trichoderma sp. PWN6 was morphologically and molecularly identified, and the GenBank accession number was MW78912.1. Both definitive screening design (DSD) and artificial neural network (ANN) were applied, for the first time, for modeling the bioconversion process of DPF. Although both models are capable of making accurate predictions, the ANN model outperforms the DSD model in terms of OA production, as ANN is characterized by a higher value of R² (0.963) and validation R² (0.967), and lower values of the RMSE (13.44), MDA (11.06), and SSE (9749.5). Citric acid was the only identified OA as was confirmed by GC-MS and UPLC, with a total of 1.5%. In conclusion, DPF together with T. harzianum PWN6 is considered an excellent new combination for citric acid biosynthesis, after modeling with artificial intelligence procedure.     

烃类混合气体的神经网络模型检测

八十年代末科学家模仿生物鼻研制一种传感器阵列与计算机模式识别的气体检测系统．传感器阵列相当于生物鼻的嗅觉细胞，计算机模式识别系统相当于嗅泡和大脑「‘］．传感器阵列对气体的响应是一个多维空间的响应模式，这种响应模式经过一定的数学处理后可以实现气体的种类识别或浓度检测［’－‘j．传感器的响应和混合气体浓度之间呈非线性关系，这一特性给定量检测多组分气体混合物造成很大的限制．应用人工神经元网络技术（ANN）可以克服这一缺陷，并使检测气体的选择性大大提高．本工作运用ANN中的反向传播（BP）算法识别由16个不同…     

Separation of organic acid compounds from biological samples by zinc oxide nanoparticles–chitosan using genetic algorithm based on response surface methodology and artificial neural network

In this study, zinc oxide nanoparticles–chitosan based on solid phase extraction and high performance liquid chromatography was developed for the separation of organic compounds including citric, tartaric and oxalic acids from biological samples. For simulation and optimization of this method, the hybrids of genetic algorithm with response surface methodology (RSM) and artificial neural network (ANN) have been used. The predictive capability and generalization of both predictive models (RSM and ANN) were compared by unseen data. The results have shown the superiority of ANN compared with RSM. At the optimum conditions, the limits of detections of 2.2–2.9 µg L⁻¹ were obtained for the analytes. The developed procedure was then applied to the extraction and determination of organic acid compounds from biological samples. Copyright © 2014 John Wiley & Sons, Ltd.     

Resolution of phenolic antioxidant mixtures employing a voltammetric bio-electronic tongue

This work reports the application of a Bio-Electronic Tongue (BioET) system made from an array of enzymatic biosensors in the analysis of polyphenols, focusing on major polyphenols found in wine. For this, the biosensor array was formed by a set of epoxy-graphite biosensors, bulk-modified with different redox enzymes (tyrosinase and laccase) and copper nanoparticles, aimed at the simultaneous determination of the different polyphenols. Departure information was the set of voltammograms generated with the biosensor array, selecting some characteristic features in order to reduce the data for the Artificial Neural Network (ANN). Finally, after the ANN model optimization, it was used for the resolution and quantification of each compound. Catechol, caffeic acid and catechin formed the three-analyte case study resolved in this work. Good prediction ability was attained, therefore allowing the separate quantification of the three phenols with predicted vs. expected slope better than 0.970 for the external test set (n = 10). Finally, BioET has been also tested with spiked wine samples with good recovery yields (values of 104%, 117% and 122% for catechol, caffeic acid and catechin, respectively).     

A new method for simultaneous determination of 226Ra and uranium in aqueous samples by liquid scintillation using chemometrics

In this work, simultaneous determination of low levels of ²²⁶Ra and uranium in aqueous samples were performed by alpha-liquid scintillation counting (LSC) in conjunction with artificial neural network (ANN) and partial least squares (PLS). The counting rates at 73 channels, which were selected by genetic algorithm, were used for training. A PLS model with four latent variables and a principle component ANN model (4-4-2) with linear transfer function after hidden and output layers were created. Total relative error of prediction for PLS and ANN in synthetic mixtures was 18.05% and 24.78%, respectively. The matrix effect was studied by spiking the real samples with radium and uranium. Laser induced fluorescence was used for assessment of uranium prediction results in real samples.     

Usage of Artificial Neural Network (Back Propagation) in Optimising Salicylic Acid Determination with Ferric(III) Nitrate

Abstract

The use of artificial neural networks (ANN) in optimizing salicylic acid (SA) determination is presented in this paper. A simple and rapid spectrophotometric method for salicylic acid (SA) determination was carried out based on the complexation of salicylic acid–ferric(III) nitrate, SAFe(III). The SA forms a stable purple complex with ferric(III) nitrate at pH 2.45. The useful dynamic linear range is 0.01–0.35 g/L. It has a maximum absorption at 524 nm and the stability is more than 50 hours. The results were used for artificial neural networks (ANNs) training to optimize data. For training and validation purposes, a back‐propagation (BP) artificial neural network (ANN) was used. The results showed that ANN technique was very effective and useful in broadening the limited dynamic linear response range mentioned to an extensive calibration response (0.01–0.70 g/L). It was found that a network with 22 hidden neurons was highly accurate in predicting the determination of SA. This network scores a summation of squared error (SSE) skill and low average predicted error of 0.0078 and 0.00427 g/L, respectively.     

Modeling of the relationship between electroosmotic flow and separation parameters in capillary zone electrophoresis using artificial neural networks and experimental design

The prediction of migration time of electroosmotic flow (EOF) marker was achieved by applying artificial neural networks (ANN) model based on principal component analysis (PCA) and standard normal distribution simulation to the input variables. The voltage of performance, the temperature in the capillary, the pH and the ionic strength of background electrolytes (BGE) were applied as the input variables to ANN. The range of the performance voltage studied was from 15 to 27 kV, and that of the temperature in the capillary was from 20 to 30 °C. For the pH values studied, the range was from 5.15 to 8.04. The range of the ionic strength investigated in this paper was from 0.040 to 0.097. The prediction abilities of ANN with different pre-processing procedure to the input variables were compared. Under the same performance conditions, the average prediction error of the migration time of the EOF marker was 5.46% with RSD = 1.76% according to 10 parallel runs of the optimized ANN structure by the proposed approach, and that of the 10 parallel predictions of the optimal ANN structure for the different performance conditions was 12.95% with RSD = 2.29% according to the proposed approach. The study showed that the proposed method could give better predicted results than other approaches discussed.     

A comparative study of diesel analysis by FTIR, FTNIR and FT-Raman spectroscopy using PLS and artificial neural network analysis

Diesel properties determined by ASTM reference methods as cetane index, density, viscosity, distillation temperatures at 50% (T50) and 85% (T85) recovery, and the total sulfur content (%, w/w) were modeled by FTIR-ATR, FTNIR, and FT-Raman spectroscopy using partial last square regression (PLS) and artificial neural network (ANN) spectral analysis. In the PLS models, 45 diesel samples were used in the training group and the other 45 samples were used in the validation. In the ANN analysis a modular feedforward network was used. Sixty diesel samples were used in the neural network training and other 30 samples were used in the validation. Two different ATR configurations were compared in the FTIR, a conventional (ATR1) and an immersion (ATR2) cell. The ATR1 cell presented the best results, with smaller prediction errors (root mean square error of prediction, RMSEP). The comparison of the three PLS models (FTIR-ATR1, FTNIR, and FT-Raman) shows that reasonable values of R² and RMSEP were obtained by the FTIR-ATR1 and FTNIR models in the evaluation of density, viscosity, and T50. The PLS/FT-Raman models presented reasonable results only for the T50 property. None of the techniques was able to generate suitable PLS calibration models for the determination of sulfur content. The ANN/FT-Raman models presented the best performances, with all models presenting R²-values above 85% some of them with RMSEP values significantly smaller than those obtained with FTIR-ATR and FTNIR. The ANN/FT-Raman and ANN/FTIR-ATR1 models were able to estimate the total sulfur content of diesel with 0.01% (w/w) accuracy.     

The solubility modelling of non-polar gases in polar and non-polar solvent based on scaled particle theory by artificial neural network

In this work, the effective parameters of the scaled particle theory (SPT) are used as the input to the artificial neural network (ANN) to calculate as the output, the solubility (mole fraction of gas in liquid phase) of non-polar gases in polar and non-polar solvents at 298.15?K and 101.325?kPa. It has been found that ANN used in this work should has five neurons in the hidden layer to achieve the least error. The results of ANN have been compared with the experimental values. The results of this comparison are quite satisfactory. The average relative deviations of the simulations in training and testing stages have been calculated 0.92% and 0.89%, respectively. Finally, the results of ANN were compared with the results of SPT. According to this comparison, it is clear that SPT as a thermodynamic model predicts the solubility of the studied gases in the solvents with the same accuracy of ANN which is a purely mathematical model.     

Comparison of Artificial Neural Networks with Partial Least Squares Regression for Simultaneous Determinations by ICP-AES

Simultaneous determination of several elements （U, Ta, Mn, Zr and W） with inductively coupled plasma atomic emission spectrometry （ICP-AES） in the presence of spectral interference was performed using chemometrics methods. True comparison between artificial neural network （ANN） and partial least squares regression （PLS） for simultaneous determination in different degrees of overlap was investigated. The emission spectra were recorded at uranium analytical line （263.553 nm） with a 0.06 nm spectral window by ICP-AES. Principal component analysis was applied to data and scores on 5 dominant principal components were subjected to ANN. A 5-5-5 （input, hidden and output neurons） network was used with linear transfer function after both hidden and output layers. The PI,S model was trained with five latent variables and 20 samples in calibration set. The relative errors of predictions （REP） in test set were 3.75% and 3.56% for ANN and PLS respectively.