Application of Non-linear Models to Predict Inhibition Effects of Various Plant Hydrosols on Listeria monocytogenes Inoculated on Fresh-Cut Apples

Abstract

In this study, we studied the effects of some plant hydrosols obtained from bay leaf, black cumin, rosemary, sage, and thyme in reducing Listeria monocytogenes on the surface of fresh-cut apple cubes. Adaptive neurofuzzy inference system (ANFIS), artificial neural network (ANN), and multiple linear regression (MLR) models were used for describing the behavior of L. monocytogenes against the hydrosol treatments. Approximately 1-1.5 log CFU/g decreases in L. monocytogenes counts were observed after individual hydrosol treatments for 20 min. By extending the treatment time to 60 min, thyme, sage, or rosemary hydrosols eliminated L. monocytogenes, whereas black cumin and bay leaf hydrosols did not lead to additional reductions. In addition to antibacterial measurements, the abilities of ANFIS, ANN, and MLR models were compared with respect to estimation of the survival of L. monocytogenes. The root mean square error, mean absolute error, and determination coefficient statistics were used as comparison criteria. The comparison results indicated that the ANFIS model performed the best for estimating the effects of the plant hydrosols on L. monocytogenes counts. The ANN model was also effective; the MLR model was found to be poor at estimating L. monocytogenes numbers.