Optimization of anticancer exopolysaccharide production from probiotic Lactobacillus acidophilus by response surface methodology

Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths in the Western world. Recently, much attention has been focused on decreasing the risk of CRC by consuming probiotics. In the present study, exopolysaccharide (EPS) extracted from Lactobacillus acidophilus was found to inhibit the growth of CaCo2 colon cancer cell line in a dose-dependent manner. The experiment was performed in both normoxic and hypoxic conditions, and EPS was found to reduce the survival of CaCo2 cell line in both the conditions. Quantitative polymerase chain reaction (qPCR) studies demonstrated that EPS treatment upregulated the expression of peroxisome proliferator activator receptor-γ (PPAR-γ) in both normoxia and hypoxia conditions, whereas it upregulated the expression of erythropoietin (EPO) in the normoxic condition, but there was no significant expression under hypoxic conditions. Hence, the EPS production was optimized by Plackett–Burman design followed by central composite rotatory design. The optimized production of EPS at 24 hr was found to be 400 mg/L. During batch cultivation the production peaked at 21 hr, resulting in an EPS concentration of 597 mg/L.     

响应面分析法优化黑根霉胞外多糖发酵工艺

实验以商品化的马铃薯葡萄糖液体培养基为基础培养基,以胞外粗多糖产量为考察指标,运用响应面分析法考察玉米浆浓度、KH₂PO₄浓度和发酵时间3个因素对胞外多糖发酵产量的影响,以获得黑根霉胞外多糖发酵最优工艺,建立高产、稳定、可控的胞外多糖发酵生产工艺技术方案。经响应面分析,各因素按照对响应值的影响顺序为：玉米浆浓度>发酵时间> KH₂PO₄浓度,且玉米浆浓度、发酵时间对胞外多糖产量的影响极显著,KH₂PO₄浓度对胞外多糖产量的影响不显著。胞外多糖发酵最优工艺为：玉米浆3.2mg/mL、KH₂PO₄ 1.5mg/mL和发酵时间132h,在此条件下胞外多糖的最大预测产量为0.824mg/mL。实验重复性好,是一个高产、稳定、可控的胞外多糖发酵生产工艺技术方案,可以指导黑根霉胞外多糖发酵。     

效应面法优化石榴皮抑菌物的提取工艺

采用单因素和效应面法对石榴皮抑菌物提取条件进行优化,并对结果进行验证。结果发现:最佳提取温度为59℃、时间为73 min、液料比为40∶1(mL/g)。实验证明石榴皮提取物对枯草杆菌具有很强的抑制作用,且所用工艺简单易行,说明效应面优化法建立的数学模型预测结果可靠。     

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.     

Optimization of culture parameters for extracellular protease production from a newly isolated Pseudomonas sp. using response surface and artificial neural network models

Radial basis function (RBF) artificial neural network (ANN) and response surface methodology (RSM) were used to build a predictive model of the combined effects of independent variables (pH, temperature, inoculum volume) for extracellular protease production from a newly isolated Pseudomonas sp. The optimum operating conditions obtained from the quadratic form of the RSM and ANN models were pH 7.6, temperature 38 °C, and inoculum volume of 1.5 with 58.5 U/ml of predicted protease activity within 24 h of incubation. The normalized percentage mean squared error obtained from ANN and RSM models were 0.05 and 0.1%, respectively. The results demonstrated an higher prediction accuracy of ANN compared to RSM. This superiority of ANN over other multi factorial approaches could make this estimation technique a very helpful tool for fermentation monitoring and control.     

响应面法优化Burkholderiasp．SYBCLIP—Y发酵产低温脂肪酶条件的研究

用响应面法对Burkholderiasp．SYBCLIP—Y液体发酵产低温脂肪酶的发酵条件进行了快速优化。首先利用Plackett—Burman设计对影响其产酶相关因素进行评估并筛选出具有显著效应的三个因素：牛肉膏,橄榄油,TritonX-100;用最陡爬坡路径逼近最大产酶区域后,利用响应面中心组合设计对显著因素进行优化,确定出牛肉膏,橄榄油,TritonX-100的最佳浓度分别为：牛肉膏31．8g／L、橄榄油21mL／L、TritonX-10036．55mL／L,优化后脂肪酶的酶活达到61．52U／mL,是优化前的2．62倍。     

Artificial neural network and response surface methodology: a comparative analysis for optimizing rice straw pretreatment and saccharification

Abstract

The present study demonstrates a comparative analysis between the artificial neural network (ANN) and response surface methodology (RSM) as optimization tools for pretreatment and enzymatic hydrolysis of lignocellulosic rice straw. The efficacy for both the processes, that is, pretreatment and enzymatic hydrolysis was evaluated using correlation coefficient (R²) & mean squared error (MSE). The values of R² obtained by ANN after training, validation, and testing were 1, 0.9005, and 0.997 for pretreatment and 0.962, 0.923, and 0.9941 for enzymatic saccharification, respectively. On the other hand, the R² values obtained with RSM were 0.9965 for cellulose recovery and 0.9994 for saccharification efficiency. Thus, ANN and RSM together successfully identify the substantial process conditions for rice straw pretreatment and enzymatic saccharification. The percentage of error for ANN and RSM were 0.009 and 0.01 for cellulose recovery and for 0.004 and 0.005 for saccharification efficiency, respectively, which showed the authority of ANN in exemplifying the non-linear behavior of the system.     

Extraction of crude polysaccharides from Duchesnea indica (Andrews) Focke: optimization by response surface methodology

A full set of optimization procedure was applied to the extraction of anti-viral polysaccharides from Duchesnea indica (Andrews) Focke. By Plackett–Burman factorial design, three parameters (extraction time, extraction temperature, and ratio of water to raw material) were identified as significant to the extraction yield. However, no significant parameters had been identified for antiviral activity. A three-level-three-factor Box–Behnken factorial design was then employed to further optimize the extraction condition. The experimental data were fitted to a second-order polynomial equation using multiple regression analysis and also examined using appropriate statistical methods. This led to the construction of a response surface indicating the optimal values for each parameter and response studied. Concerning the extraction yield, an extraction at 98.51?ºC for 6.16 h with a ratio of water to raw material of 30.94 mL/g was found to be optimal. Under the optimized conditions, the experimental yield was 6.430 ± 0.078%, which was well matched with the predicted yield of 6.509%.     

Modeling in vitro cholesterol reduction in relation to growth of probiotic Lactobacillus casei

Lactobacillus casei LA‐1 isolated from a nondairy fermented source was evaluated for its in vitro ability to reduce cholesterol. The bacterium tested positive for bile salt deconjugation in relation to cholesterol removal. Tested growth‐associated physiological variables such as pH, temperature and inoculum size were all found to have significant effects on in vitro cholesterol reduction and biomass production (both P < 0.005). Furthermore, a central composite design was used to evaluate the effects of significant variables and their interactions. A linear regression model was developed for in vitro cholesterol reduction as a function of growth‐associated variables. Maximum cholesterol reduction achieved was 45% whereas maximum biomass yield of 2.34 optical density was observed at the central point. Our study possibly indicates that the growth of L. casei LA‐1 depends on its cholesterol removing ability.     

响应面法优化海洋放线菌Streptomyces sp.YT-10抗菌物质的发酵条件

以啤酒酵母为指示菌,研究了海洋放线菌YT-10产抗菌物质的发酵条件。利用Plackett-Burman设计对影响YT-10产抗菌物质的因素的效应进行评价,筛选出具有显著效应的三个因素:葡萄糖、黄豆粉和氯化钠。利用响应面中心旋转组合设计优化三个显著因素,确定了葡萄糖,黄豆粉和氯化钠浓度分别为0.7%,1.3%,0.952%。优化后抑菌圈直径可达32.6mm,比原来增加了34.7%。     

Determination of an economical medium for growth of Lactobacillus fermentum using response surface methodology

Aim: Lactobacillus fermentum is a widely utilized probiotic compound fed as an alternative to antibiotics for growth promotion in a wide variety of livestock species. The objective of this research is to develop an economical and practical fermentation medium for the growth of Lact. fermentum using response surface methodology. Methods and Results: A two‐level Plackett–Burman design was used to determine which factors in the fermentation medium influence the growth of Lact. fermentum. Under our experimental conditions, peptone, urea and yeast extract were found to be major factors. Then, the steepest ascent method and the central composite design were applied to optimize the culture of Lact. fermentum. The following composition of the fermentation medium was estimated to be the most economical formula (per litre): 30 g corn syrup, 15 g glucose, 14·4 g peptone, 7 g (NH₄)₂SO₄, 0·5 g urea, 3 g sodium acetate, 4 g sodium citrate, 0·1 g MnSO₄·4H₂O, 0·5 g MgSO₄·7H₂O, 7·3 g yeast extract, 0·5 g K₂HPO₄. Conclusion: Based on 10 side‐by‐side comparisons, we found that the yield of Lact. fermentum using our fermentation medium was 64% greater than those using modified de Man, Rogosa and Sharp broth (MRS) medium (1·8 × 10⁹ CFU ml^?1vs 1·1 × 10⁹ CFU ml^?1, respectively), while the cost was 89% lower than MRS. This research indicates that it is possible to increase bacterial yield by using inexpensive materials. Significance and Impact of the Study: It is more likely that the use of Lact. fermentum as a probiotic will increase. The low cost medium developed in this research can be used for large‐scale, commercial application where economics are quite likely to be important.     

响应面设计法优化单葡萄糖醛酸基甘草次酸（GAMG）发酵转化培养基

以甘草酸（dycyfrhizin,GL）为底物,利用产紫青霉（Penicillium purpurogenum Li-3）液态发酵转化单葡萄糖醛酸甘草次酸（GAMG）,采用响应面设计法对初始发酵培养基进行优化。用部分因子分析法研究原始发酵培养基各成分对响应值的显著程度,发现甘草酸（GL）、NaNO3和K2HPO4的质量浓度对发酵产生GAMG的影响显著（P〈0．01）。用中心组合设计确立甘草酸、NaNO3和K2HPO4的适宜质量浓度分别为2．8、3．0和0．8g／L。在优化条件下进行发酵时,GAMG的转化率从75．49％提高到89．11％,比优化前提高了13．62％。     

Statistical optimization of production conditions of alkaline pectin lyase from Bacillus cereus using response surface methodology

Alkaline pectin lyase finds applications in the degumming and retting of plant fibres, textile industry and pectic wastewater treatment where it degrades highly methylesterified pectin without prior action of any other pectinase. Response surface methodology (RSM) has been frequently utilized for the optimization of production process of industrially important enzymes from microbes. In the present work, fermentation conditions for the production of pectin lyase from Bacillus cereus were optimized using the factorial and central composite design of RSM. The cubic order polynomial regression model was found to be adequate and significant with a determination coefficient R² of 0.9505 (p?相似文献   

Formulation optimization of chelerythrine loaded O-carboxymethylchitosan microspheres using response surface methodology

The aims of this investigation were to develop a procedure to prepare chelerythrine (CHE) loaded O-carboxymethylchitosan (O-CMCS) microspheres by emulsion cross-linking method and optimize the process and formulation variables using response surface methodology (RSM) with a three-level, three-factor Box-Behnken design (BBD). The independent variables studied were O-CMCS/CHE ratio, O/W phase ratio, and O-CMCS concentration, dependent variables (responses) were drug loading content and encapsulation efficiency. Mathematical equations and response surface plots were used to relate the dependent and independent variables. The process and formulation variables were optimized to achieve maximum drug loading content and entrapment efficiency by the desirability function. The optimized microsphere formulation was characterized for particle size, shape, morphology and in vitro drug release. Results for mean particle size, drug loading content, entrapment efficiency, and in vitro drug release of CHE-loaded O-CMCS microspheres were found to be of 12.18 μm, 4.16 ± 3.36%, 57.40 ± 2.30%, and 54.5% at pH 7.4 after 70 h, respectively. The combination use of RSM, BBD and desirability function could provide a promising application for O-CMCS as controlled drug delivery carrier and help to develop procedures for a lab-scale microemulsion process.     

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.     

响应面法对红法夫酵母合成虾青素主要影响因素的优化

在单因素试验确定了红法夫酵母生物合成虾青素培养基组份的基础上,用响应面法对其浓度进行优化。首先用分式析因设计评价了培养基的各组份对虾青素产量的影响,并找出主要影响因子为蔗糖和酵母粉,二者分别达到了极显著和显著水平。用最陡爬坡路径逼近最大响应区域后,运用旋转中心复合设计及响应面分析,确定了主要影响因子的最佳浓度。其中,蔗糖的最佳浓度为49.8g/L,酵母粉的浓度为9.6g/L。菌株在优化培养基中的虾青素产量为9861μg/L,比优化前增加了近1倍。     

酶法合成2-氧-α-D-葡萄糖基-L-抗坏血酸(AA-2G)条件的研究

采用单因素优化法对环糊精葡萄糖苷转移酶(CGTase)合成糖基抗坏血酸(AA-2G)条件进行优化,AA-2G的产量为2.76 g/L,比未优化前0.46g/L提高了500%。再采用响应面法对AA-2G合成条件进行优化。由Plackett-Burman法筛选出三个主要因素为:pH、V_C和麦芽糊精浓度;由最陡爬坡实验得出最佳响应面区域;最后由Box-Behnken实验,得到最优条件为:pH 5.51,V_C36.16g/L,麦芽糊精28.54 g/L,转化时间24 h,温度37℃。在此条件下,AA-2G的理论产量为3.15 g/L,通过验证实验,得出AA-2G的产量为3.13 g/L,与预测的理论值接近,比单因素优化的结果(2.76g/L)提高了14%。