Cracking spoilage in jar cream cheese: Introducing, modeling and preventing

This study aimed to investigate the modeling of antimicrobial activity (AA) of nisin and sorbate on Clostridium sporogenes in jar cream cheese (JCC) using the linear regression (LR), multilayer perceptron (MLP) neural network, and reduced error pruning tree (REPTree) methods, in order to prevent the late blowing defect (LBD) in the cheese. Both preservatives used in JCC samples showed AA against C. sporogenes; so that sorbate at all the concentrations used in JCC samples inhibited cracking spoilage during storage period at 35 °C. However, nisin could not inhibit cracking spoilage at concentration of 30 ppm in the samples, and a higher concentration of it was needed. The three models used in this study, followed the similar pattern in both training and validation datasets for nisin and sorbat in JCC. The R² and root mean square error (RMSE) values of training and validation datasets showed the superiority of the REPTree model compared to the MLP and LR models (conventional methods) in the modeling of AA of nisin and sorbate against C. sporogenes in JCC.