Application of artificial neural network in predicting the extraction yield of essential oils of Diplotaenia cachrydifolia by supercritical fluid extraction

Application of ionic liquid ultrasound-assisted extraction (IL-UAE) of lycopene from guava (Psidium guajava L.) by response surface methodology and artificial neural network-genetic algorithm

Lycopene-rich guava (Psidium guajava L.) exhibits significant economic potential as a functional food ingredient, making it highly valuable for the pharmaceutical and agro-food industries. However, there is a need to enhance the extraction methods of lycopene to fully exploit its beneficial uses. In this study, we evaluated various ionic liquids to identify the most effective one for extracting lycopene from guava. Among thirteen ionic liquids with varying carbon chains or anions, 1-butyl-3-methylimidazolium chloride demonstrated the highest productivity. Subsequently, a single-factor experiment was employed to test the impact of several parameters on the efficiency of lycopene extraction using this selected ionic liquid. These parameters included extraction time, ultrasonic power, liquid-solid ratio, concentration of the ionic liquid, as well as material particle size. Moreover, models of artificial neural networks using genetic algorithms (ANN-GA) and response surface methodology (RSM) were employed to comprehensively assess the first four key parameters. The optimized conditions for ionic liquid ultrasound-assisted extraction (IL-UAE) were determined as follows: 33 min of extraction time, 225 W of ultrasonic power, 22 mL/g of liquid-solid ratio, 3.0 mol/L of IL concentration, and extraction cycles of three. Under these conditions, lycopene production reached an impressive yield of 9.35 ± 0.36 mg/g while offering advantages such as high efficiency, time savings, preservation benefits, and most importantly environmental friendliness.

Nutritional Aspects, Chemistry Profile, Extraction Techniques of Lemongrass Essential Oil and It's Physiological Benefits

Lemongrass contains a variety of substances that are known to have antioxidant and disease-preventing properties, including essential oils, compounds, minerals, and vitamins. Lemongrass (Cymbopogon Spp.) essential oil (LGEO) has been demonstrated to ameliorate diabetes and accelerate wound healing. A member of the Poaceae family, Lemongrass, a fragrant plant, is cultivated for the extraction of essential oils including myrcene and a mixture of geranial and neral isomers of citral monoterpenes. Active constituents in lemongrass essential oil are myrcene, followed by limonene and citral along with geraniol, citronellol, geranyl acetate, neral, and nerol, which are beneficial to human health. A large part of lemongrass' expansion is driven by the plant's huge industrial potential in the food, cosmetics, and medicinal sectors. A great deal of experimental and modeling study was conducted on the extraction of essential oils. Using Google Scholar and PubMed databases, a systematic review of the literature covering the period from 1996 to 2022 was conducted, in accordance with the PRISMA declaration. There were articles on chemistry, biosynthesis, extraction techniques and worldwide demand of lemongrass oil. We compared the effectiveness of several methods of extracting lemongrass essential oil, including solvent extraction, supercritical CO2 extraction, steam distillation, hydrodistillation (HD), and microwave aided hydrodistillation (MAHD). Moreover, essential oils found in lemongrass and its bioactivities have a significant impact on human health. This manuscript demonstrates the different extraction techniques of lemongrass essential oil and its physiological benefits on diabetic wound healing, tissue repair and regeneration, as well as its immense contribution in ameliorating arthritis and joint pain.Key teaching pointsThe international market demand prediction and the pharmacological benefits of the Lemongrass essential oil have been thoroughly reported here.This article points out that different extraction techniques yield different percentages of citral and other secondary metabolites from lemon grass, for example, microwave assisted hydrodistillation and supercritical carbon dioxide extraction process yields more citral.This article highlights the concept and application of lemongrass oil in aromatherapy, joint-pain, and arthritis.Moreover, this manuscript includes a discussion about the effect of lemongrass oil on diabetic wound healing and tissue regeneration - that paves the way for further research.

Artificial Neural Network and Response Surface-Based Combined Approach to Optimize the Oil Content of Ocimum basilicum var. thyrsiflora (Thai Basil)

Ocimum basilicum var. thyrsiflora is valuable for its medicinal properties. The barriers to the commercialization of essential oil are the lack of requisite high oil-containing genotypes and variations in the quantity and quality of essential oils in different geographic areas. Thai basil's essential oil content is significantly influenced by soil and environmental factors. To optimize and predict the essential oil yield of Thai basil in various agroclimatic regions, the current study was conducted. The 93 datasets used to construct the model were collected from samples taken across 10 different agroclimatic regions of Odisha. Climate variables, soil parameters, and oil content were used to train the Artificial Neural Network (ANN) model. The outcome showed that a multilayer feed-forward neural network with an R squared value of 0.95 was the most suitable model. To understand how the variables interact and to determine the optimum value of each variable for the greatest response, the response surface curves were plotted. Garson's algorithm was used to discover the influential predictors. Soil potassium content was found to have a very strong influence on responses, followed by maximum relative humidity and average rainfall, respectively. The study reveals that by adjusting the changeable parameters for high commercial significance, the ANN-based prediction model with the response surface methodology technique is a new and promising way to estimate the oil yield at a new site and maximize the essential oil yield at a particular region. To our knowledge, this is the first report on an ANN-based prediction model for Ocimum basilicum var. thyrsiflora.

Effect of cold plasma processing on physicochemical and nutritional quality attributes of kiwifruit juice

Cold plasma treatment of kiwifruit juice was studied in the domain of 18-30 kV of voltage, 2-6 mm of juice depth, and 6-10 min of treatment time using the response surface methodology (RSM). The experimental design utilized was a central composite rotatable design. The effect of voltage, juice depth, and treatment time on the various responses, namely peroxidase activity, color, total phenolic content, ascorbic acid, total antioxidant activity, and total flavonoid content, was examined. While modeling, the artificial neural network (ANN) showed greater predictive capability than RSM as the coefficient of determination (R² ) value of responses was greater in the case of ANN (0.9538-0.9996) than in RSM (0.9041-0.9853). The mean square error value was also less in the case of ANN than in RSM. The ANN was coupled with a genetic algorithm (GA) for optimization. The optimum condition obtained from ANN-GA was 30 kV, 5 mm, and 6.7 min, respectively.

Developing a computational toolbased on an artificial neural network for predicting and optimizing propolis oil, an important natural product for drug discovery

Propolis is a promising natural product that has been extensively researched and studied for its potential health and medical benefits. The lack of requisite high oil-containing propolis and existing variation in the quality and quantity of essential oil within agro-climatic regions pose a problem in the commercialization of essential oil. As a result, the current study was carried out to optimize and estimate the essential oil yield of propolis. The essential oil data of 62 propolis samples from ten agro-climatic areas of Odisha, as well as an investigation of their soil and environmental parameters, were used to construct an artificial neural network (ANN) based prediction model. The influential predictors were determined using Garson's algorithm. To understand how the variables interact and to determine the optimum value of each variable for the greatest response, the response surface curves were plotted. The results revealed that the most suited model was multilayer-feed-forward neural networks with an R2 value of 0.93. According to the model, altitude was found to have a very strong influence on response, followed by phosphorous & maximum average temperature. This research shows that using an ANN-based prediction model with a response surface methodology technique to estimate oil yield at a new site and maximize propolis oil yield at a specific site by adjusting variable parameters is a viable commercial option. To our knowledge, this is the first report on the development of a model to optimize and estimate the essential oil yield of propolis.

The Application of Artificial Intelligence in Magnetic Hyperthermia Based Research

The development of nanomedicine involves complex nanomaterial research involving magnetic nanomaterials and their use in magnetic hyperthermia. The selection of the optimal treatment strategies is time-consuming, expensive, unpredictable, and not consistently effective. Delivering personalized therapy that obtains maximal efficiency and minimal side effects is highly important. Thus, Artificial Intelligence (AI) based algorithms provide the opportunity to overcome these crucial issues. In this paper, we briefly overview the significance of the combination of AI-based methods, particularly the Machine Learning (ML) technique, with magnetic hyperthermia. We considered recent publications, reports, protocols, and review papers from Scopus and Web of Science Core Collection databases, considering the PRISMA-S review methodology on applying magnetic nanocarriers in magnetic hyperthermia. An algorithmic performance comparison in terms of their types and accuracy, data availability taking into account their amount, types, and quality was also carried out. Literature shows AI support of these studies from the physicochemical evaluation of nanocarriers, drug development and release, resistance prediction, dosing optimization, the combination of drug selection, pharmacokinetic profile characterization, and outcome prediction to the heat generation estimation. The papers reviewed here clearly illustrate that AI-based solutions can be considered as an effective supporting tool in drug delivery, including optimization and behavior of nanocarriers, both in vitro and in vivo, as well as the delivery process. Moreover, the direction of future research, including the prediction of optimal experiments and data curation initiatives has been indicated.

Unassisted and Carbon Dioxide-Assisted Hydro- and Steam-Distillation: Modelling Kinetics, Energy Consumption and Chemical and Biological Activities of Volatile Oils

The demand for more suitable eco-friendly extraction processes has grown over the last few decades and driven research to develop efficient extraction processes with low energy consumption and low costs, but always assuring the quality of the volatile oils (VOs). The present study estimated the kinetic extraction and energy consumption of simultaneous hydro- and steam-distillation (SHSD), and SHSD assisted by carbon dioxide (SHSDACD), using an adopted modelling approach. The two isolation methods influenced the VOs yield, chemical composition and biological activities, namely, antioxidant, anti-glucosidase, anti-acetylcholinesterase and anti-inflammatory properties. SHSDACD provided higher VOs yields than the SHSD at a shorter extraction time: 2.8% at 30 min vs. 2.0% at 120 min, respectively, for Rosmarinus officinalis, 1.5% at 28 min vs. 1.2% at 100 min, respectively, for Lavandula angustifolia, and 1.7% at 20 min vs. 1.6% at 60 min, respectively, for Origanum compactum. The first order and sigmoid model fitted to SHSD and SHSDACD, respectively, with R2 value at 96% and with mean square error (MSE) < 5%, where the k distillation rate constant of SHSDACD was fivefold higher and the energy consumption 10 times lower than the SHSD. The rosemary SHSD and SHSDACD VOs chemical composition were similar and dominated by 1,8-cineole (50% and 48%, respectively), and camphor (15% and 12%, respectively). However, the lavender and oregano SHSDACD VOs were richer in linalyl acetate and carvacrol, respectively, than the SHSD VOs. The SHSDACD VOs generally showed better capacity for scavenging the nitric oxide and superoxide anions free radicals as well as for inhibiting α-glucosidase, acetylcholinesterase, and lipoxygenase.

High-spatiotemporal-resolution estimation of solar energy component in the United States using a new satellite-based model

Diffuse solar radiation (Rd), known as an important component of global solar radiation (Rg), is a key parameter for solar energy related applications and ecosystem photosynthesis. Some meteorological models have been developed to estimate Rd with acceptable accuracy, but their spatial scales are often small due to the limited meteorological station number. Satellite-based models provide accurate and large-scale Rg estimates. However, remote sensing estimations of Rd are often with low spatial resolutions and large uncertainties, because their methods were based on inaccurate surface and atmospheric parameters. To address these challenges, the high-spatiotemporal-resolution (half-hourly and 1-km) Rd in the United States was estimated using the top-of-atmosphere (TOA) data from the new-generation geostationary satellite Geostationary Operational Environmental Satellites (GOES-16) and the method iterative random forest (RF). The results showed that the iterative RF model had higher accuracy than the simple RF and artificial neural network (ANN) models, and using TIR (thermal infrared) bands can improve models' accuracy. The best model can estimate half-hourly Rd with the accuracy R² = 0.88, RMSE = 37.81 W/m², and MBE = 0.01 W/m². Compared with the previous 0.01-degree (∼11-km) Rd product Earth Polychromatic Imaging Camera (EPIC), the GOES-16 estimated 1-km Rd had similar spatial patterns. Moreover, based on the GOES-16 estimated half-hourly and 1-km Rd in the United States, the spatiotemporal heterogeneity of Rd was quantitatively observed. The proposed approach can be used to produce more high-spatiotemporal-resolution Rd products, and these products are very helpful for many solar-related research topics and industrial applications.

Currently Applied Extraction Processes for Secondary Metabolites from Lippia turbinata and Turnera diffusa and Future Perspectives

The poleo (Lippia turbinata Griseb.) and damiana (Turnera diffusa Wild) are two of the most valued species in the Mexican semidesert due to their medicinal uses. The conventional essential oil extraction process is hydrodistillation, and for the extraction of antioxidants, the use of organic solvents. However, these techniques are time-consuming and degrade thermolabile molecules, and the efficiency of the process is dependent on the affinity of the solvent for bioactive compounds. Likewise, they generate solvent residues such as methanol, hexane, petroleum ether, toluene, chloroform, etc. Therefore, in recent years, ecofriendly alternatives such as ohmic heating, microwaves, ultrasound, and supercritical fluids have been studied. These methodologies allow reducing the environmental impact and processing times, in addition to increasing yields at a lower cost. Currently, there is no up-to-date information that provides a description of the ecofriendly trends for the recovery process of essential oils and antioxidants from Lippia turbinata and Turnera diffusa. This review includes relevant information on the most recent advancements in these processes, including conditions and methodological foundation.

Supercritical Fluid Extraction Kinetics of Cherry Seed Oil: Kinetics Modeling and ANN Optimization

This study was primarily focused on the supercritical fluid extraction (SFE) of cherry seed oil and the optimization of the process using sequential extraction kinetics modeling and artificial neural networks (ANN). The SFE study was organized according to Box-Behnken design of experiment, with additional runs. Pressure, temperature and flow rate were chosen as independent variables. Five well known empirical kinetic models and three mass-transfer kinetics models based on the Sovová’s solution of SFE equations were successfully applied for kinetics modeling. The developed mass-transfer models exhibited better fit of experimental data, according to the calculated statistical tests (R², SSE and AARD). The initial slope of the SFE curve was evaluated as an output variable in the ANN optimization. The obtained results suggested that it is advisable to lead SFE process at an increased pressure and CO₂ flow rate with lower temperature and particle size values to reach a maximal initial slope.

Comprehensive study on applications of artificial neural network in food process modeling

Artificial neural network (ANN) is a simplified model of the biological nervous system consisting of nerve cells or neurons. The application of ANN to food process engineering is relatively novel. ANN had been employed in diverse applications like food safety and quality analyses, food image analysis, and modeling of various thermal and non-thermal food-processing operations. ANN has the ability to map nonlinear relationships without any prior knowledge and predicts responses even with incomplete information. Every neural network possesses data in the form of connection weights interconnecting lines between the input to hidden layer neurons and weights of hidden to output layer neurons, which has a significant role in predicting the output data. The applications of ANN in different unit operations in food processing were described that includes theoretical developments using intelligent characteristics for adaptability, automatic learning, classification, and prediction. The parallel architecture of ANN resulted in a fast response and low computational time making it suitable for application in real-time systems of different food process operations. The predicted responses obtained by the ANN model exhibited high accuracy due to lower relative deviation and root mean squared error and higher correlation coefficient. This paper presented the various applications of ANN for modeling nonlinear food engineering problems. The application of ANN in the modeling of the processes such as extraction, extrusion, drying, filtration, canning, fermentation, baking, dairy processing, and quality evaluation was reviewed.HIGHLIGHTS1. This paper discusses application of ANN in different emerging trends in food process.2. Application of ANN to develop non-linear multivariate modeling is illustrated.3. ANNs have been shown to be useful tool for prediction of outcomes with high accuracy.4. ANN resulted in fast response making it suitable for application in real time systems.

Establishment of the Predicting Models of the Dyeing Effect in Supercritical Carbon Dioxide Based on the Generalized Regression Neural Network and Back Propagation Neural Network

With the growing demand of supercritical carbon dioxide (SC-CO2) dyeing, it is important to precisely predict the dyeing effect of supercritical carbon dioxide. In this work, Generalized Regression Neural Network (GRNN) and Back Propagation Neural Network (BPNN) models have been employed to predict the dyeing effect of SC-CO2. These two models have been constructed based on published experimental data and calculated values. A total of 386 experimental data sets were used in the present work. In GRNN and BPNN models, two input parameters, such as temperature, pressure, dye stuff types, carrier types and dyeing time, were selected for the input layer and one variable, K/S value or dye-uptake, was used in the output layer. It was found that the values of mean-relative-error (MRE) for BPNN model and for GRNN model are 3.27–6.54% and 1.68–3.32%, respectively. The results demonstrate that both BPNN and GPNN models can accurately predict the effect of supercritical dyeing but the former is better than the latter.

Modeling and optimization of pectinase-assisted low-temperature extraction of cashew apple juice using artificial neural network coupled with genetic algorithm

In this study, pectinase-assisted extraction of cashew apple juice was modeled and optimized using a multi-layer artificial neural network (ANN) coupled with genetic algorithm (GA). The effect of incubation time, incubation temperature, and enzyme concentration on different responses such as yield, turbidity, ascorbic acid content, polyphenol content, total soluble solids, and pH was also determined. The developed ANN has minimum mean squared error values of 0.83, 40.92, 29.01, and 8.95 and maximum R values of 0.9999, 0.9972, 0.9995, and 0.9996 for training, testing, validation, and all data sets, respectively, which shows good agreement between the actual and predicted values. The optimum extraction parameters obtained using the developed ANN-GA were as follows: an incubation time of 64 min, incubation temperature of 32 °C, and enzyme concentration of 0.078%. The measured value of responses at the optimized process conditions were in accordance with the predicted values obtained using the developed ANN model.

Climate-induced thermoregulatory responses in a non-linear thermal environment: investigating the inter-dependencies using a facile artificial neural network-based predictive strategy

Objectives. Given the burgeoning impacts of climatic variability on human health, suitable computational paradigms are used to explore the subsequent ergonomic repercussions. The artificial neural network (ANN), in particular, exhibits near-accurate input-output mapping. However, employment of the ANN to trace the inter-dependencies between the climatic and human thermoregulatory parameters in real-world fuzzy problem landscapes is relatively inadequate. In the present study, the ANN models examined the relationships between climatic, behavioral and intrinsic input factors and the thermoregulatory outputs, namely, sweating and the evaporative heat transfer at the skin surface (E_sk). Methods. The data were obtained from nearly 1800 subjects who were exposed to a hot and humid climate outdoors. The ANN models were trained using the Levenberg-Marquardt algorithm combined with Bayesian regularization. Results. The predictability of the ANN models was statistically substantiated. The clothing insulation factor was not included as an input parameter, given its similar values. Intriguingly, the ANN results indicated that fabrics with similar thermal resistances could still affect E_sk, plausibly owing to the temporal variation in the evaporative resistance of fabrics among individuals. Conclusion. The reasonably accurate results affirmed the suitability of ANN as a pragmatic technique that could elucidate heat-induced ergonomic challenges.

A new nutraceutical resource from a rare native plant growing in Turkey and for its spectro-chemical and biological insights: Endemic Diplotaenia bingolensis (Apiaceae)

The present study designed to investigate the quantitative distributions in the secondary metabolites and biological activity of sub-fractions obtained successively from water and methanol: dichloromethane (rate 1:1; v:v) solvent decoction of Diplotenia bingolensis aerial parts. The crude extracts were obtained from the aerial parts of the endemic D. bingolensis species refluxing with water and organic solvents. Sub-fractions of water extract were obtained by successive fractionation of the water extract with hexane (WH), dichloromethane (WD), ethyl acetate (WE) and n-butanol (WB), respectively. Sub-fractions of organic solvent were obtained by fractionation of the organic crude extract with hexane (OH), dichloromethane (OD), ethyl acetate (OE), n-butanol (OB) and water (OW), respectively. The total amount of phenols and flavonoids contained in each sub-fraction was analyzed by UV-VIS spectrophotometer, analysis of lipophilic components by GC-MS spectrometer, and quantitative analysis of hydrophilic components by HPLC-TOF/MS spectrophotometer. Furthermore, the biological activity of each sub-fraction was compared with different antioxidant activities such as DPPH radical scavenging activity and ferric ion reduction capacity. Sub-fraction WD (137.1 ± 2.1 μg QE/mg DI) and OE (127.1 ± 5.2 μg QE/mg DI) in terms of flavonoid content, sub-fraction WD (665.8 ± 47.6 μg GAE/mg DI) and OE (724.6 ± 43.6 μg GAE/mg DI) were the richest isolates in terms of total phenol content. Sub-fractions OH and OD contained linoleic acid (17.0 and 11.0%, respectively) and linolenic acid (22.1 and 18.5%, respectively). It was revealed that sub-fractions were rich in terms of rutin (1.2-47.2 μg HC/mg DI) and chlorogenic acid (0.1-12.1 μg HC/mg DI). Sub-fractions WD and OE were showed the highest DPPH radical scavenging activity with 46.4 ± 1.4 and 47.6 ± 10.0 μg/mL EC₅₀ values, respectively. This study is the first to demonstrate biological insight of the potential antioxidant activity of D. bingolensis. These findings warrant the popular use of the endemic D. bingolensis and highlight the potential of its active constituents in the development of new antioxidative drugs.

Application of the artificial neural network and imperialist competitive algorithm for optimization of molecularly imprinted solid phase extraction of methylene blue

In this study, a hybrid of the artificial neural network-imperialist competitive algorithm (ANN-ICA) has been applied for prediction and optimization of the molecularly imprinted solid phase extraction method. This method has been used for the pre-concentration of methylene blue (MB) from environmental water samples prior to UV-Vis spectrophotometry. Molecular imprinted polymer sorbents were synthesized using radical polymerization by MB, 4-vinylpyridine, ethylene-glycol-dimethacrylate, 2,2′-azobisisobutyronitrile and methanol as a template, functional monomer, cross-linker, initiator, and porogen, respectively. The imprinted polymer was characterized by Fourier transform infrared spectroscopy and scanning electron microscopy. The pH, adsorbent mass, adsorption time, eluent volume, and extraction time were been selected as input parameters and the recovery of MB was considered as an output variable of the ANN model. The results were then compared according to the performance function and determination coefficient. The Freundlich and Langmuir adsorption models were used to explain the isotherm constant. The maximum adsorption capacity was 417 mg g-1. At the optimized conditions, the limit of detection and relative standard deviation was found to be 0.31 μg l-1 and <1.7%, respectively. This method was applied to analysis the MB in various water samples.

A hybrid artificial neural network and particle swarm optimization for prediction of removal of hazardous dye brilliant green from aqueous solution using zinc sulfide nanoparticle loaded on activated carbon

In the present study, zinc sulfide nanoparticle loaded on activated carbon (ZnS-NP-AC) simply was synthesized in the presence of ultrasound and characterized using different techniques such as SEM and BET analysis. Then, this material was used for brilliant green (BG) removal. To dependency of BG removal percentage toward various parameters including pH, adsorbent dosage, initial dye concentration and contact time were examined and optimized. The mechanism and rate of adsorption was ascertained by analyzing experimental data at various time to conventional kinetic models such as pseudo-first-order and second order, Elovich and intra-particle diffusion models. Comparison according to general criterion such as relative error in adsorption capacity and correlation coefficient confirm the usability of pseudo-second-order kinetic model for explanation of data. The Langmuir models is efficiently can explained the behavior of adsorption system to give full information about interaction of BG with ZnS-NP-AC. A multiple linear regression (MLR) and a hybrid of artificial neural network and partial swarm optimization (ANN-PSO) model were used for prediction of brilliant green adsorption onto ZnS-NP-AC. Comparison of the results obtained using offered models confirm higher ability of ANN model compare to the MLR model for prediction of BG adsorption onto ZnS-NP-AC. Using the optimal ANN-PSO model the coefficient of determination (R(2)) were 0.9610 and 0.9506; mean squared error (MSE) values were 0.0020 and 0.0022 for the training and testing data set, respectively.

Optimization of technological procedure for amygdalin isolation from plum seeds (Pruni domesticae semen)

The process of amygdalin extraction from plum seeds was optimized using central composite design (CCD) and multilayer perceptron (MLP). The effect of time, ethanol concentration, solid-to-liquid ratio, and temperature on the amygdalin content in the extracts was estimated using both mathematical models. The MLP 4-3-1 with exponential function in hidden layer and linear function in output layer was used for describing the extraction process. MLP model was more superior compared with CCD model due to better prediction ability. According to MLP model, the suggested optimal conditions are: time of 120 min, 100% (v/v) ethanol, solid-to liquid ratio of 1:25 (m/v) and temperature of 34.4°C. The predicted value of amygdalin content in the dried extract (25.42 g per 100 g) at these conditions was experimentally confirmed (25.30 g per 100 g of dried extract). Amygdalin (>90%) was isolated from the complex extraction mixture and structurally characterized by FT-IR, UV, and MS methods.

Synthesis of zinc oxide nanoparticles-chitosan for extraction of methyl orange from water samples: cuckoo optimization algorithm-artificial neural network

In this work, zinc nanoparticles-chitosan based solid phase extraction has been developed for separation and preconcentration of trace amount of methyl orange from water samples. Artificial neural network-cuckoo optimization algorithm has been employed to develop the model for simulation and optimization of this method. The pH, volume of elution solvent, mass of zinc oxide nanoparticles-chitosan, flow rate of sample and elution solvent were the input variables, while recovery of methyl orange was the output. The optimum conditions were obtained by cuckoo optimization algorithm. At the optimum conditions, the limit of detections of 0.7μgL(-1)was obtained for the methyl orange. The developed procedure was then applied to the separation and preconcentration of methyl orange from water samples.

Artificial Intelligence in Numerical Modeling of Silver Nanoparticles Prepared in Montmorillonite Interlayer Space

Artificial neural network (ANN) models have the capacity to eliminate the need for expensive experimental investigation in various areas of manufacturing processes, including the casting methods. An understanding of the interrelationships between input variables is essential for interpreting the sensitivity data and optimizing the design parameters. Silver nanoparticles (Ag-NPs) have attracted considerable attention for chemical, physical, and medical applications due to their exceptional properties. The nanocrystal silver was synthesized into an interlamellar space of montmorillonite by using the chemical reduction technique. The method has an advantage of size control which is essential in nanometals synthesis. Silver nanoparticles with nanosize and devoid of aggregation are favorable for several properties. In this investigation, the accuracy of artificial neural network training algorithm was applied in studying the effects of different parameters on the particles, including the AgNO3concentration, reaction temperature, UV-visible wavelength, and montmorillonite (MMT) d-spacing on the prediction of size of silver nanoparticles. Analysis of the variance showed that the AgNO3concentration and temperature were the most significant factors affecting the size of silver nanoparticles. Using the best performing artificial neural network, the optimum conditions predicted were a concentration of AgNO3of 1.0 (M), MMT d-spacing of 1.27 nm, reaction temperature of 27°C, and wavelength of 397.50 nm.