Modeling the effects of different conditions on the inhibitory activity of antimicrobial lipopeptide (AMPNT-6) against Staphylococcus aureus growth and enterotoxin production in shrimp meat

Several bacteria, including Staphylococcus aureus (S. aureus), have been identified as food-borne pathogenic bacteria that cause many cases of food poisoning. This has prompted researchers to identify novel antimicrobial compounds. NT-6 antimicrobial lipopeptide (AMPNT-6) secreted by Bacillus subtilis can inhibit S. aureus growth and enterotoxin B production. The effects of temperature, sodium chloride, pH and sodium metabisulfite on the activity of AMPNT-6 applied to control S. aureus growth and enterotoxin B production in shrimp were investigated by using the response surface method. Design-Expert software was chosen to conduct data analysis and develop the models to demonstrate whether AMPNT-6 is able to control the growth of S. aureus and toxin production in shrimp exposed to different environmental conditions. The results showed that temperature, sodium chloride concentration, pH and sodium metabisulfite concentration had no significant effects on the antimicrobial activity of AMPNT-6 and there were no significant interactions between them. Quadratic polynomial mathematical models were established, and the quadratic regression equations of predicted value LgN (logarithm of bacterial colony number) and Y (enterotoxin B) to the independent variables x (1) (temperature), x (2) (sodium chloride concentration), x (3) (pH), x (4) (sodium metabisulfite concentration) and x (5) (AMPNT-6 concentration) were constructed as: AMPNT-6 showed stable antimicrobial activity that was hardly affected by external factors such as temperature, salinity, pH and sodium metabisulfite. Observed experimental values were comparable with the predicted values. It is concluded that models that showed good fitness can be used to predict the inhibitory effects of AMPNT-6 on S. aureus growth and enterotoxin B production in shrimp exposed to different external variables.