Investigation of Microbial, Chemical and Color Changes of Fish Burgers in Different Storage Conditions using Artificial Neural Network

Abstract

The aim of this study was to evaluate and predict changes in total viable count (TVC), Pseudomonas, Psychrotroph, Enterobacteriaceae, Lactic Acid bacteria and chemical parameters including volatile nitrogen bases (TVB-N) and malondialdehyde (MDA) in cooking silver carp burger at 45, 55, 65, 75 and 85 °C using artificial neural network. The cooking time was 20 and 30 min and the storage was 18 days. Cooking at 55 °C for 30 min had no effect on inactivating other groups, but it did affect Pseudomonas. Cooking at 65 °C for 20 min controlled Psychotrophs and Enterobacteriaceae and had no effect on other groups. The amount of Pseudomonas during cooking time of 20 min at 45 and 55 °C and Lactobacillus during cooking time of 20 min at 45 °C were not within the standard bacterial load and the results showed that fish burgers could be consumed in other treatments until the end of the storage period, except in these treatments. At both cooking times, the cooking temperature of 85 °C inactivated Pseudomonas, Enterobacteriaceae and Lactic Acid bacteria. Temperature had the most and time had the least effect on the model proposed for MDA. The burgers were within range of TVB-N during storage. By increasing the storage time, the color index L* was increasing and the color of the burgers tended to be white. It can be said that the neural network (MLP) results are reliable and can be used to reduce the cost of experiments in the production industry of burger.

Keywords

• artificial neural network
• chemical changes
• fish burger
• microbial changes