New approaches to modeling Staphylococcus aureus inactivation by ultrasound

Abstract

Ultrasound (US) is an effective technology to inactivate vegetative microorganisms in foods. In this study, the effect of amplitude levels (0.4, 7.5, and 37.5), duty cycles (0.3:0.7 s, 0.7:0.3 s, and 0.9: 0.1 s) and time (0, 2, 4, 6, 8, 10, 12, and 14 days) of US on inactivation of Staphylococcus aureus were investigated. In addition, genetic algorithm-artificial neural network (GA-ANN) and adaptive neuro-fuzzy inference system (ANFIS) models were used to predict inactivation of S. aureus. The GA-ANN and ANFIS were fed with three inputs of amplitude levels, duty cycles, and time. The inactivation rate of S. aureus was increased by increasing the amplitude levels, and the best inactivation was obtained at a 37.5 μm amplitude for which the S. aureus population was reduced to 2.59 CFU/mL. The high inactivation of S. aureus was achieved under a duty cycle of 0.7:0.3 s with reduction of the population to 1.49 CFU/mL. The developed GA-ANN, which included 17 hidden neurons, could predict the S. aureus population with a coefficient of determination of 0.986. The overall agreement between ANFIS predictions and experimental data was also very good (R 2  = 0.979). Sensitivity analysis results showed that the amplitude level was the most sensitive factor for prediction of S. aureus.