Comparative study of response surface methodology and artificial neural network for optimization of process parameters for synthesis of gold nanoparticles by Desmostachya bipinnata extract

Green synthesis of nanoparticles has gained attention due to its eco-friendly and sustainable approach to synthesize nanoparticles at a reduced cost. Artificial neural network (ANN) and response surface model (RSM) are important to reduce experimental efforts in nanoparticle synthesis. In this work, optimization of gold nanoparticle synthesis by Desmostachya bipinnata extract was performed using the volume of plant extract, concentration of auric chloride, reaction time, pH, and temperature as process parameters, and the output was absorbance. The experimental design was obtained from RSM and the model was optimized further using ANN. Thirty-two experimental runs generated by RSM were performed and the results obtained experimentally were compared with those generated by RSM and ANN. Different algorithms of ANN were tested to obtain the best one. The optimization studies resulted in a maximum response for 20^th run with 15 ml, 2.5 mM, 45 min, 7, and 40 °C as parameters. Optimized input parameters obtained by RSM were 10 ml, 2 mM, 30 min, 6, and 30 °C. The formation of gold nanoparticles was confirmed by UV spectroscopy, XRD, and SEM. Different algorithms of ANN, such as leven marquardt, scaled conjugate gradient, and bayesian network were used. Leven marquardt algorithm was found to be the most suitable algorithm for the current study.

Evaluation of carbon sources for the production of inulinase by Aspergillus niger A42 and its characterization

Inulinases are used for the production of high-fructose syrup and fructooligosaccharides, and are widely utilized in food and pharmaceutical industries. In this study, different carbon sources were screened for inulinase production by Aspergillus niger in shake flask fermentation. Optimum working conditions of the enzyme were determined. Additionally, some properties of produced enzyme were determined [activation (E_a)/inactivation (E_ia) energies, Q₁₀ value, inactivation rate constant (k_d), half-life (t_1/2), D value, Z value, enthalpy (ΔH), free energy (ΔG), and entropy (ΔS)]. Results showed that sugar beet molasses (SBM) was the best in the production of inulinase, which gave 383.73 U/mL activity at 30 °C, 200 rpm and initial pH 5.0 for 10 days with 2% (v/v) of the prepared spore solution. Optimum working conditions were 4.8 pH, 60 °C, and 10 min, which yielded 604.23 U/mL, 1.09 inulinase/sucrase ratio, and 2924.39 U/mg. Additionally, E_a and E_ia of inulinase reaction were 37.30 and 112.86 kJ/mol, respectively. Beyond 60 °C, Q₁₀ values of inulinase dropped below one. At 70 and 80 °C, t_1/2 of inulinase was 33.6 and 7.2 min; therefore, inulinase is unstable at high temperatures, respectively. Additionally, t_1/2, D, ΔH, ΔG values of inulinase decreased with the increase in temperature. Z values of inulinase were 7.21 °C. Negative values of ΔS showed that enzymes underwent a significant process of aggregation during denaturation. Consequently, SBM is a promising carbon source for inulinase production by A. niger. Also, this is the first report on the determination of some properties of A. niger A42 (ATCC 204,447) inulinase.