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Basic Research | Publishing Language: Chinese | Open Access

Optimization of High-density Culture Medium for Staphylococcus carnosus Based on Artificial Neural Network-Genetic Algorithm

Yi WANG1 Chaozhi ZHU1Xueyuan BAI1Yangyi ZHENG1Xinjun ZHANG2Lin TONG3Gaiming ZHAO1 ( )
School of Food Science and Technology, Henan Agricultural University, Zhengzhou 450002, China
Zhongwei Comprehensive Experimental Station, National Beef Cattle Industrial Technology System, Zhongwei 755000, China
Tongliao Comprehensive Experimental Station, National Beef Cattle Industrial Technology System, Tongliao 028000, China
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Abstract

This study aimed to achieve high-density culture of Staphylococcus carnosus for the purpose of preparing highly active starter cultures. Tryptic soy broth was used as the basal culture medium. Optimization of medium components was carried out using one-factor-at-a-time method and Box-Behnken design combined with response surface methodology (RSM). Meanwhile, an artificial neural network-genetic algorithm (ANN-GA) model was developed. The results indicated that the nitrogen source was the most significant factor influencing the viable count of S. carnosus. Compared with the RSM model, the ANN-GA model provided more accurate predictions with smaller prediction errors and superior optimization results. The optimal medium determined by the ANN-GA method was composed of 3.21 g/L glucose, 20.17 g/L soy protein peptone, 20.17 g/L beef extract powder, 5.63 g/L dipotassium phosphate, 5.0 g/L sodium chloride, and 0.2 g/L magnesium sulfate heptahydrate. In a 5 L fermentor, the maximum viable bacterial count reached 1.67 × 1010 CFU/mL.

CLC number: TS201.3 Document code: A Article ID: 1001-8123(2025)05-0001-09

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Meat Research
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Cite this article:
WANG Y, ZHU C, BAI X, et al. Optimization of High-density Culture Medium for Staphylococcus carnosus Based on Artificial Neural Network-Genetic Algorithm. Meat Research, 2025, 39(5): 1-9. https://doi.org/10.7506/rlyj1001-8123-20241021-275

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Received: 21 October 2024
Published: 31 May 2025
© China Meat Research Center 2025.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).