It is well known that different microorganisms require different fermentation conditions due to their strain characteristics to produce their beneficial fermentation products. Therefore, Lifeasible provides technical consulting services for fermentation process optimization, mainly focusing on the selection of different components of the culture medium such as carbon, nitrogen, and trace elements, as well as the optimization of fermentation conditions including fermentation temperature, pH, dissolved oxygen, etc. By using different process optimization methods, we can effectively improve the productivity of the fermentation process, while reducing your production costs and facilitating the improvement of your fermentation process.

What We Offer

In the process of optimizing microbial fermentation processes, Lifeasible effectively conducts individual and combined optimization tests of fermentation medium components and culture conditions to help our customers efficiently and systematically obtain microbial target products.

• Selection of carbon and nitrogen sources for fermentation medium
• Inorganic salts in the fermentation medium
• Initial pH value during fermentation
• Inoculum size and seed age during fermentation
• Temperature and dissolved oxygen during fermentation

Lifeasible offers optimization methods including simple one-variable-at-a-time and statistically relevant methods such as Orthogonal experiment design and Well-distributed design. We applied statistical software to mathematically simulate and optimize the experimental results, screened the optimization factors based on the Plackett-Burman experimental design scheme, and then used Response Surface Methodology (RSM) based on the central combinatorial experimental design to achieve the optimization purpose.

• Orthogonal experiment

We use the "orthogonal table" to arrange and analyze the multi-factor problem, and the results can be directly compared and visually analyzed to find the main factors affecting the index. It has the advantages of fewer tests, good effect, simple method, easy to use, and high application efficiency.

• Plackett-Burman

We applied this method to a two-level experimental design with a large number of factors to quickly and effectively identify the most important factors that have a significant impact on the test results from the many factors examined.

• Response Surface Methodology (RSM)

RSM combines mathematical modeling, experimental design, and statistical analysis techniques. We use the data obtained through a reasonable experimental design to fit the functional relationship between the factors and the test results (response values) using a multiple quadratic regression equation, establish the regression equation, and draw a graph to identify the optimal region in the graph and seek the optimal process parameters, to solve the impact of multivariate factors on the test. It has the characteristics of intuition and high reliability.

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For research use only, not intended for any clinical use.

Related Services

• Microbial Culture
• Lactic Acid Bacteria Screening Analysis
• Yeast Screening and Identification