Application of Two Modeling Methods in Optimization for Adenosine Extraction from Mycelium of Cordyceps Militaris

Abstract:

An experimental mixture design coupled with data analysis by means of both response surface methodology(RSM) and artificial neural network(ANN) was applied to explore the optimum process parameters for adenosine extraction from cultured mycelium of Cordyceps militaris. With the extraction rate of adenosine as index, the critical factors selected for the investigation were extracting temperature, extracting time and solid-liquid radio. The results obtained by the application of ANN were more reliable since better statistical parameters were obtained. The optimum extraction procedure was as follow: extracting time 2.3 h, extracting temperature 48 °C, solid-liquid ratio 1:38 g⋅mL^-1. Under the optimal conditions, the corresponding response value predicted for adenosine production was 4.59 mg g^-1, which was confirmed by validation experiments.

• Periodical: Advanced Materials Research (Volumes 343-344)
• Pages: 826-831
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.343-344.826
• Online since: September 2011
• Authors: Lin Na Du, Jia Song, Ling Jun Meng, Jia Hui Lu, Qing Fan Meng, Li Rong Teng
• Keywords: Adenosine, Artificial Neural Network (ANN), Cordyceps militaris, Genetic Algorithm (GA), Response Surface Method (RSM)

References

[1] So-Young Won, Eun-Hee Park, Anti-inflammatory and related pharmacological activities of cultured mycelia and fruiting bodies of Cordyceps militaris, J. Ethnopharmacol, vol. 96, no. 3, pp.555-561, January (2005).

DOI: 10.1016/j.jep.2004.10.009

[2] Maki Hattori, Shigeki Isomura, Eiji Yokoyama; Minoru Ujita, Akira Hara, Extracellular Trypsin-like Proteases Produced by Cordyceps militaris, J BIOSCI BIOENG, vol. 100, no. 6, pp.631-636, August (2005).

DOI: 10.1263/jbb.100.631

[3] Yerra Koteswara Rao, Chia-Hsien Chou, Yew-Min Tzeng, A simple and rapid method for identification and determination of cordycepin in Cordyceps militaris by capillary electrophoresis, ANAL CHIM ACTA, vol. 566, no. 2, pp.253-258, May (2006).

DOI: 10.1016/j.aca.2006.02.071

[4] Yuxiang Gu, Zunsheng Wang, Suxia Li, Qinsheng Yuan, Effect of multiple factors on accumulation of nucleosides Cordyceps militaris, Food Chem, vol. 102, no. 4, pp.1304-1309, (2007).

DOI: 10.1016/j.foodchem.2006.07.018

[5] Li Cui, Mingsheng Dong, Xiaohong Chen, Mei Jiang, Xin Lv, Guijun Yan, A novel fibrinolytic enzyme from Cordyceps militaris, a Chinese traditional medicinal mushroom, World J Microbiol Biotechnol, vol. 24, no. 4, pp.483-489, August (2008).

DOI: 10.1007/s11274-007-9497-1

[6] J S Zhu, GM Halpern, KJones, The scientific rediscovery of an ancient Chinese herbal medicine: Cordyceps sinensis: part I, J Altern Complement Med, vol. 4, no. 3, pp.289-303, (1998).

DOI: 10.1089/acm.1998.4.3-289

[7] Chun Kiat Pua, Nazimah Sheikh Abd. Hamid, Chin Ping Tan, Hamed Mirhosseini, Russly Bin Abd. Rahman, Gulam Rusul, Optimization of drum drying processing parameters for production of jackfruit (Artocarpus heterophyllus) powder using response surface methodology, LWT-Food SCI TECHNOL, vol. 43, no. 2, pp.343-349, March (2010).

DOI: 10.1016/j.lwt.2009.08.011

[8] Jyothi AN, Sreekumar J, Moorthy SN, Sajeev MS, Response Surface Methodology for the Optimization and Characterization of Cassava Starch-graft-Poly(acrylamide), Starch-Starke, vol. 62, no. 1, pp.18-27, January (2010).

DOI: 10.1002/star.200900157

[9] Guilherme A. Moreira, Gabriela A. Micheloud, Alejandro J. Beccaria, Hector C. Goicoechea., Optimization of the Bacillus thuringiensis var. kurstaki HD-1 δ-endotoxins production by using experimental mixture design and artificial neural networks, Biochem Eng. J, vol. 35, no. 1, pp.48-55, July (2007).

DOI: 10.1016/j.bej.2006.12.025

[10] Yongfen Ran, Guangchi Xiong, Shisheng Li, Liaoyuan Ye, Study on deformation prediction of landslide based on genetic algorithm and improved BP neural network, Kybernetes, vol. 39, no. 8, pp.1245-1254, (2010).

DOI: 10.1108/03684921011063529

[11] C. Sivapathasekaran, Soumen Mukherjee, Arja Ray, Ashish Gupta, Ramkrishna Sen, Artificial neural network modeling and genetic algorithm based medium optimization for the improved production of marine biosurfactant, Bioresour Technol, vol. 101, no. 8, pp.2884-2887, April (2010).

DOI: 10.1016/j.biortech.2009.09.093

[12] Weiliang Guo, et al, Optimization of fermentation medium for nisin production from Lactococcus lactis subsp lactis using response surface methodology (RSM) combined with artificial neural network-genetic algorithm (ANN-GA), African J. Biotechnol, vol. 9, no. 38, pp.6264-6272, Semptember (2010).