Study on response surface methodology (RSM) of lipase-catalyzed synthesis of palm-based wax ester

The synthesis of wax ester using refined, bleached and deodorized (RBD) palm oil and oleyl alcohol catalyzed by lipozyme IM was carried out. Response surface methodology (RSM) based on a five-level, four-variable central composite rotatable design (CCRD) was used to evaluate the interactive effects of synthesis, of reaction time (2.5–10 h), temperature (30–70 °C), amount of enzyme (0.1–0.2 g) and substrate molar ratio (palm oil to oleyl alcohol, 1:1–1:5) on the percentage yield of wax esters. The optimum conditions derived via RSM were: reaction time 7.38 h, temperature 53.9 °C, amount of enzyme 0.149 g, and substrate molar ratio 1:3.41. The actual experimental yield was 84.6% under optimum condition, which compared well to the maximum predicted value of 85.4%.     

Statistical optimization of the phytoremediation of arsenic by Ludwigia octovalvis- in a pilot reed bed using response surface methodology (RSM) versus an artificial neural network (ANN)

In this study, the removal of arsenic (As) by plant, Ludwigia octovalvis, in a pilot reed bed was optimized. A Box-Behnken design was employed including a comparative analysis of both Response Surface Methodology (RSM) and an Artificial Neural Network (ANN) for the prediction of maximum arsenic removal. The predicted optimum condition using the desirability function of both models was 39 mg kg^?1 for the arsenic concentration in soil, an elapsed time of 42 days (the sampling day) and an aeration rate of 0.22 L/min, with the predicted values of arsenic removal by RSM and ANN being 72.6% and 71.4%, respectively. The validation of the predicted optimum point showed an actual arsenic removal of 70.6%. This was achieved with the deviation between the validation value and the predicted values being within 3.49% (RSM) and 1.87% (ANN). The performance evaluation of the RSM and ANN models showed that ANN performs better than RSM with a higher R² (0.97) close to 1.0 and very small Average Absolute Deviation (AAD) (0.02) and Root Mean Square Error (RMSE) (0.004) values close to zero. Both models were appropriate for the optimization of arsenic removal with ANN demonstrating significantly higher predictive and fitting ability than RSM.     

Comparison of the estimation capabilities of response surface methodology and artificial neural network for the optimization of recombinant lipase production by E. coli BL21

Response surface methodology (RSM) and artificial neural network (ANN) were used to optimize the effect of four independent variables, viz. glucose, sodium chloride (NaCl), temperature and induction time, on lipase production by a recombinant Escherichia coli BL21. The optimization and prediction capabilities of RSM and ANN were then compared. RSM predicted the dependent variable with a good coefficient of correlation determination (R ²) and adjusted R ² values for the model. Although the R ² value showed a good fit, absolute average deviation (AAD) and root mean square error (RMSE) values did not support the accuracy of the model and this was due to the inferiority in predicting the values towards the edges of the design points. On the other hand, ANN-predicted values were closer to the observed values with better R ², adjusted R ², AAD and RMSE values and this was due to the capability of predicting the values throughout the selected range of the design points. Similar to RSM, ANN could also be used to rank the effect of variables. However, ANN could not predict the interactive effect between the variables as performed by RSM. The optimum levels for glucose, NaCl, temperature and induction time predicted by RSM are 32 g/L, 5 g/L, 32°C and 2.12 h, and those by ANN are 25 g/L, 3 g/L, 30°C and 2 h, respectively. The ANN-predicted optimal levels gave higher lipase activity (55.8 IU/mL) as compared to RSM-predicted levels (50.2 IU/mL) and the predicted lipase activity was also closer to the observed data at these levels, suggesting that ANN is a better optimization method than RSM for lipase production by the recombinant strain.     

Optimization of lipase-catalyzed synthesis of caffeic acid phenethyl ester in ionic liquids by response surface methodology

Lipase-catalyzed caffeic acid phenethyl ester (CAPE) synthesis in ionic liquid, 1-ethyl-3-methylimidazolium bis[(trifluoromethyl)sulfonyl]imide ([Emim][Tf₂N]), was investigated in this study. The effects of several reaction conditions, including reaction time, reaction temperature, substrate molar ratio of phenethyl alcohol to caffeic acid (CA), and weight ratio of enzyme to CA, on CAPE yield were examined. In a single parameter study, the highest CAPE yield in [Emim][Tf₂N] was obtained at 70 °C with a substrate molar ratio of 30:1 and weight ratio of enzyme to CA of 15:1. Based on these results, response surface methodology (RSM) with a 3-level-4-factor central composite rotatable design (CCRD) was adopted to evaluate enzymatic synthesis of CAPE in [Emim][Tf₂N]. The four major factors were reaction time (36–60 h), reaction temperature (65–75 °C), substrate molar ratio of phenethyl alcohol to CA (20:1–40:1), and weight ratio of enzyme to CA (10:1–20:1). A quadratic equation model was used to analyze the experimental data at a 95 % confidence level (p < 0.05). A maximum conversion yield of 99.8 % was obtained under the optimized reaction conditions [60 h, 73.7 °C, substrate molar ratio of phenethyl alcohol to CA (27.1:1), and weight ratio of enzyme to CA (17.8:1)] established by our statistical method, whereas the experimental conversion yield was 96.6 ± 2 %.     

Optimization of lipase-catalyzed synthesis of acetylated EGCG by response surface methodology

(−)-Epigallocatechin-3-O-gallate (EGCG) acetylated derivatives, which can be widely used as a natural antioxidant in both lipid containing food and cosmetic applications, were prepared by lipase catalyzed acylation of EGCG with vinyl acetate. Response surface methodology (RSM) and 5-level-4-factor central composite rotatable design (CCRD) were employed to evaluate the effects of synthesis parameters, such as reaction time (6–10 h), temperature (30–50 °C), enzyme amount (1.5–2.5% (w/w) of substrate), and substrate molar ratio of EGCG to vinyl acetate (0.5–1.5) on conversion of EGCG. By using multiple regression analysis, the experimental data were fitted to a second order polynomial model. The most suitable combination of variables was 40 °C, 2.12%, 10 h and 1.13 for the reaction temperature, the enzyme amount, the reaction time, and EGCG/vinyl acetate mole ratio, respectively. At these optimal conditions, the conversion yield reached 87.37%. The presence of mono-, di- and tri-acetylated derivatives in acetylated EGCG was confirmed by LC–MS-MS and identified as 5″-O-acetyl-EGCG, 3″, 5″-2-O-acetyl-EGCG and 5′, 3″, 5″-3-O-acetyl-EGCG by NMR.     

Intelligent image analysis (IIA) using artificial neural network (ANN) for non-invasive estimation of chlorophyll content in micropropagated plants of potato

An artificial neural network (ANN) was used to analyze photometric features extracted from the digitized images of leaves from in vitro-regenerated potato plants for non-invasive estimation of chlorophyll content. A MATLAB®-based, feed-forward, backpropagation-type network was developed for an input layer (three input elements), with one hidden layer (one node) and one output layer representing the predicted chlorophyll content. A significant influence of training function during optimization of ANN modeling was observed. Among the 11 training functions tested, “trainlm” was found to be the best on the basis of comparative analysis of root-mean-square error (RMSE) at zero epoch. A significant correlation between the model-predicted and Soil-Plant Analysis Development (SPAD) meter-measured relative chlorophyll contents was obtained when the mean brightness ratio (rgb) parameters were used. Compared to a red (R), green (G), and blue (B) color space model, the rgb model exhibited better performance with a significant correlation (R ² = 0.85). Incorporation of photometric features, such as luminosity (L), blue (B)/L, and green (G)/L, with rgb failed to improve the performance of the network. The developed Intelligent image analysis (IIA) system was able to estimate in real time the chlorophyll content of in vitro-regenerated leaves for assessment of plant nutrient status during micropropagation.     

Optimization of lactose utilization in deproteinated whey by Kluyveromyces marxianus using response surface methodology (RSM)

Kluyveromyces marxianus Y-8281 yeast culture was utilized for the biological treatment of deproteinated whey wastewater in a batch system. Removal of lactose was optimized by the utilization of response surface methodology, RSM. The empirical model developed through RSM in terms of effective operational factors of medium pH, temperature, lactose and ammonia concentrations was found adequate to describe the treatment of deproteinated whey. Through the analysis, medium pH and temperature were found to be the most significant factors and an increment in both had a positive effect on lactose utilization, while lactose and ammonia concentrations had the least weight within the ranges investigated. Based on contour plots and variance analysis, optimum operational conditions for maximizing lactose removal were found to be 31 degrees C, 45 g/L whey powder concentration, 4 g/L total ammonium salt concentration and medium pH 6. Under the optimum operating conditions determined, 95% lactose removal was achieved after an 18-h fermentation.     

Copper biosorption on Lyngbya putealis: Application of response surface methodology (RSM)

The potential use of biosorbent prepared from an indigenously isolated cyanobacterium, Lyngbya putealis, for the removal of copper from aqueous solution has been investigated under optimized conditions in this study. Batch mode experiments were performed to determine the adsorption equilibrium and kinetic behavior of copper in aqueous solution allowing the computation of kinetic parameters and maximum metal adsorption capacity. Influences of other parameters like initial metal ion concentration (10-100 mg l⁻¹), pH (2-8) and biosorbent dose (0.1-1.0 g/100 ml) on copper adsorption were also examined, using Box-Behnken design matrix. Very high regression coefficient between the variables and the response (R² = 0.9533) indicates excellent evaluation of experimental data by second order polynomial regression model. The response surface method indicated that 40-50 mg l⁻¹ initial copper concentration, 6.0-6.5 pH and biosorbent dose of 0.6-0.8 g/100 ml were optimal for biosorption of copper by biosorbent prepared from L. putealis. On the basis of experimental results and model parameters, it can be inferred that the biosorbent which has quite high biosorption capacity can be utilized for the removal of copper from aqueous solution.     

Application of an artificial neural network (ANN) model for predicting mosquito abundances in urban areas

The mosquito species is one of most important insect vectors of several diseases, namely, malaria, filariasis, Japanese encephalitis, dengue, and so on. In particular, in recent years, as the number of people who enjoy outdoor activities in urban areas continues to increase, information about mosquito activity is in demand. Furthermore, mosquito activity prediction is crucial for managing the safety and the health of humans. However, the estimation of mosquito abundances frequently involves uncertainty because of high spatial and temporal variations, which hinders the accuracy of general mechanistic models of mosquito abundances. For this reason, it is necessary to develop a simpler and lighter mosquito abundance prediction model. In this study, we tested the efficacy of the artificial neural network (ANN), which is a popular empirical model, for mosquito abundance prediction. For comparison, we also developed a multiple linear regression (MLR) model. Both the ANN and the MLR models were applied to estimate mosquito abundances in 2-year observations in Yeongdeungpo-gu, Seoul, conducted using the Digital Mosquito Monitoring System (DMS). As input variables, we used meteorological data, including temperature, wind speed, humidity, and precipitation. The results showed that performances of the ANN model and the MLR model are almost same in terms of R and root mean square error (RMSE). The ANN model was able to predict the high variability as compared to MLR. A sensitivity analysis of the ANN model showed that the relationships between input variables and mosquito abundances were well explained. In conclusion, ANNs have the potential to predict fluctuations in mosquito numbers (especially the extreme values), and can do so better than traditional statistical techniques. But, much more work needs to be conducted to assess meaningful time delays in environmental variables and mosquito numbers.     

Side-by-side comparison of horizontal subsurface flow and free water surface flow constructed wetlands and artificial neural network (ANN) modelling approach

A horizontal subsurface flow (HSSF) and a free water surface flow (FWSF) constructed wetlands (4 m² of each) were set up on the campus of Harran University, Sanliurfa, Turkey. The main objective of the research was to compare the performance of two systems to decide the better one for future planning of wastewater treatment system on the campus. Both of the wetland systems were planted with Phragmites australis and Canna indica. During the observation period (10 months), environmental conditions such as pH, temperature and total chemical oxygen demand (COD), soluble COD, total biochemical oxygen demand (BOD), soluble BOD, total suspended solids (TSS), total phosphate (TP), total nitrogen (TN) removal efficiencies of the systems were determined. According to the results, average yearly removal efficiencies for the HSSF and the FWSF, respectively, were as follows: total COD (75.7% and 69.9%), soluble COD (85.4% and 84.3%), total BOD (79.6% and 87.6%), soluble BOD (87.7% and 95.3%), TN (33.2% and 39.4%), and TP (31.5% and 6.5%). Soluble COD and BOD removal efficiencies of both systems increased gradually since the start-up. After nine months of operation, above 90% removal of organic matters were observed. The treatment performances of the HSSF were better than that of the FWSF with regard to the removal of suspended solids and total COD at especially high temperatures. In FWSF systems, COD concentrations extremely exceeded the discharge limit values due to high concentrations of algae in spring months.The performance of the two systems was modelled using an artificial neural network-back-propagation algorithm. The ANN model was competent at providing reasonable match between the measured and the predicted concentrations of total COD (R = 0.90 for HSSF and R = 0.96 for FWSF), soluble COD (R = 0.90 for HSSF and R = 0.74 for FWSF) and total BOD (R = 0.94 for HSSF and R = 0.84 for FWSF) in the effluents of constructed wetlands.     

Use of response surface methodology for the optimization of the lipase-catalyzed synthesis of mannosyl myristate in pure ionic liquid

The purpose of the present study is to find the conditions allowing to reach the highest 24 h-yield (24 h-η) for the synthesis of mannosyl myristate catalyzed by the immobilized lipase B from Candida antarctica (Novozym^® 435) in the ionic liquid (IL) [Bmpyrr][TFO] (1-butyl-1-methylpyrrolidinium trifluoromethanesulfonate). A full factorial design (FFD) was used in order to study the influence of three variables (temperature, mannose/vinyl myristate ratio and total substrate quantity) on the 24 h-η. This design led to a model based on a second order polynomial response function. The resulting predicted contour plots have shown that the highest 24 h-η should be obtained with high temperatures, low sugar/vinyl ester molar ratio and intermediate total substrate quantities (mmol). The model has been successfully verified and experimentally confirmed at the optimal conditions of 80 °C, substrate molar ratio of 1/10 and total substrate quantity of 0.26 mmol leading to the highest predicted 24 h-η of 72.2%.     

Optimization of caulogenesis in Populus nigra under lead (Pb) stress via response surface methodology (RSM) and desirability function analysis

Plant Cell, Tissue and Organ Culture (PCTOC) - Polyethylene glycol (PEG)-mediated transient expression system in plant protoplasts has been widely used in a variety of plants for gene function...     

Modeling and optimization of lipase-catalyzed esterification of policosanols with conjugated linoleic acid by response surface methodology

Abstract

The aim of this study was to model the lipase-catalyzed esterification of policosanols with conjugated linoleic acid (CLA) in a solvent-free system to produce wax esters which had a lower melting point than that of their corresponding policosanol forms and to optimize the reaction conditions by response surface methodology (RSM). Novozym 435 was selected as a suitable biocatalyst for the reaction. The molar ratio of substrates (policosanols to CLA) was 1:2. A well-fitting quadratic polynomial regression model for the degree of esterification (DE) of policosanols with CLA was established with regard to temperature (35–65°C), enzyme loading (1–5% of weight of total substrates), and reaction time (10–50 min). Optimal reaction conditions were 61.3°C for temperature, 3.7% for enzyme loading, and 34.1 min for reaction time, and the DE was ? 95 mol% under these conditions. The policosanols and wax esters synthesized under optimal conditions had melting points of 79°C and 57°C, respectively.     

Optimization of polysaccharides (ABP) extraction from the fruiting bodies of Agaricus blazei Murill using response surface methodology (RSM)

Response surface methodoloty (RSM) was used to optimize the extraction conditions of polysaccharides (ABP) from the fruiting body of Agaricus blazei. A central composite design (CCD) was used for experimental design and analysis of the results to obtain the optimal processing parameters. Four independent variables such as extraction temperature (°C), ratio of water to raw material, number of extraction, and extraction time (h) were investigated. The experimental data obtained were fitted to a second-order polynomial equation using multiple regression analysis and also analyzed by appropriate statistical methods. The 3-D response surface plot and the contour plot derived from the mathematical models were applied to determine the optimal conditions. The optimum extraction conditions were as follows: extraction temperature 91 °C, ratio of water to raw material 14, number of extraction 6, and extraction time 2.1 h. Under these conditions, the experimental value was 65.8 ± 1.42, which is well in close agreement with value predicted by the model.     

城市边缘区景观生态规划的人工神经网络模型

景观生态规划是景观生态学的一个重要应用领域,本文在地理信息系统的辅助下引入了人工神经网络这一新兴应用技术,建立了城市边缘区景观生态规划的BP神经网络模型,模型以区域的高程、高程离差、坡度、坡度离差、地貌分区、离黄河距离、居民点数七个要素作为输入变量,选取斑块密度、分维数、Shannon多样性指数和聚集度指数作为输出变量,精心采集了20个样本对网络进行训练,结果表明网络收敛效果理想,泛化能力强,为景观生态规划提供了一个新的模拟分析手段。     

Statistical optimization of chromium (VI) reduction using response surface methodology (RSM) by newly isolated Stenotrophomonas sp. (a novel strain)

BioMetals - Isolation of Microorganisms capable of reducing toxic chromium (VI) into less toxic one (Cr (III)) has been given attention due to their significance in bioremediation of the...     

An approach for prediction of optimum reaction conditions for laccase-catalyzed bio-transformation of 1-naphthol by response surface methodology (RSM)

Response surface methodology (RSM) was successfully applied to enzymatic bio-transformation of 1-naphthol. The experiments were conducted in a closed system containing acetone and sodium acetate buffer, with laccase enzyme. Laccase enzyme used as catalyst was derived from Trametes versicolor (ATCC 200801). The enzymatic bio-transformation rate of 1-naphthol, based on measurements of initial dissolved oxygen (DO) consumption rate in the closed system, was optimized by the application of RSM. The independent variables, which had been found as the most effective variables on the initial DO consumption rate by screening experiments, were determined as medium temperature, pH and acetone content. A quadratic model was developed through RSM in terms of related independent variables to describe the DO consumption rate as the response. Based on contour plots and variance analysis, optimum operational conditions for maximizing initial DO consumption rate, while keeping acetone content at its minimum value, were 301 K of temperature, pH 6 and acetone content of 7% to obtain 9.17 x 10(-3) mM DO/min for initial oxidation rate.     

Extraction and optimization of exopolysaccharide from Lactobacillus sp. using response surface methodology and artificial neural networks

Abstract

The microbial polysaccharides secreted and produced from various microbes into their extracellular environment is known as exopolysaccharide. These polysaccharides can be secreted from the microbes either in a soluble or insoluble form.Lactobacillus sp. is one of the organisms that have been found to produce exopolysaccharide. Exo-polysaccharides (EPS) have various applications such as drug delivery, antimicrobial activity, surgical implants and many more in different fields. Medium composition is one of the major aspects for the production of EPS from Lactobacillus sp., optimization of medium components can help to enhance the synthesis of EPS . In the present work, the production of exopolysaccharide with different medium composition was optimized by response surface methodology (RSM) followed by tested for fitting with artificial neural networks (ANN). Three algorithms of ANN were compared to investigate the highest yeild of EPS. The highest yeild of EPS production in RSM was achieved by the medium composition that consists of (g/L) dextrose 15, sodium dihydrogen phosphate 3, potassium dihydrogen phosphate 2.5, triammonium citrate 1.5, and, magnesium sulfate 0.25. The output of 32 sets of RSM experiments were tested for fitting with ANN with three algorithms viz. Levenberg–Marquardt Algorithm (LMA), Bayesian Regularization Algorithm (BRA) and Scaled Conjugate Gradient Algorithm (SCGA) among them LMA found to have best fit with the experiments as compared to the SCGA and BRA.     

响应面法优化酶法转化合成维生素C糖苷（AA-2G）条件的研究

采用Design—Expert软件的Central Composite Design（CCD）响应面设计对环糊精葡萄糖苷转移酶转化合成糖基抗坏血酸（AA-2G）的五个主要因素（转化时间、转化温度、pH、Vc浓度、β-环糊精浓度）进行了研究。采用降维分析方法对pH与转化时间、转化温度、Vc浓度、β-环糊精浓度以及反应温度与反应时间的交互作用对酶法转化合成AA-2G的影响进行了分析。建立了影响因素与响应值之间的回归方程,根据回归方程优化得到最佳转化条件为：转化时间25h,温度36．5℃,pH5．4,Vc72dL,β-环糊精55g／L。在此条件下,AA-2G的理论产量为10．06g／L,在验证实验中AA-2G的产量为9．76g／L,与预测的理论产量接近,比优化前提高了33％。