Medium Optimization for the Production of Lycopene Based on BP Neural Network and Genetic Algorithms

doi:10.3969/j.issn.1673-1689.2019.02.016

Home > Archive>Volume 38, Issue 2, 2019 >111-119. DOI:10.3969/j.issn.1673-1689.2019.02.016

Medium Optimization for the Production of Lycopene Based on BP Neural Network and Genetic Algorithms
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                        10.3969/j.issn.1673-1689.2019.02.016
                    
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The present study aims at using error Back Propagation（BP） neural network and genetic algorithms to improve lycopene production from semisynthetic medium by Blakeslea trispora. The effects of different kinds of carbon source，nitrogen source and vegetable oil on lycopene concentration and biomass are analyzed to confirm the component of medium. The establishment of BP neural network model is based on 49 group of samples data. In this model，Corn flour，corn steep liquor，soybean oil，monopotassium phosphate，magnesium sulfate are set to inputs，and lycopene volumetric production as output. Then，the genetic algorithms is applied to search the optimal value using BP network model as fitness function. After optimization，the maximum predicted value of lycopene production is 1.27 g/L. And the error of predicted value and actual value is less than 5% by experimental verification. Lycopene production in optimized medium is 31.6% higher than that in initial media. The optimized medium containes 41.2 g/L corn starch，8.93 g/L corn steep liquor，26.5 g/L soybean oil，1.39 g/L KH2PO4，0.46 g/L MgSO4. The combination of BP neural network with genetic algorithms is a power tool to obtain optimized lycopene fermentation medium.

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WANG Qiang, FENG Lingran, YU Xiaobin. Medium Optimization for the Production of Lycopene Based on BP Neural Network and Genetic Algorithms[J]. Journal of Food Science and Biotechnology,2019,38(2):111-119.

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Online: March 28,2019
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