Prediction on Microbiological Quality of Industrial Chicken Sausages during Distribution to Retailer vicinity Bangkok

Ashadi Yaacob, Jirarat Anuntagool

Abstract

Predictive survival of microbiological quality of chicken sausages was simulated using ComBase® microbial predictive software. The prediction was based on real time and temperature monitoring chicken sausages during distribution to retailer around Bangkok area. Six pathogens, which were Pseudomonas spp., Staphylococcus aureus, Salmonella spp., Listeria monocytogenes, Escherichia coli and Clostridium perfringens were selected respectively for simulation during distribution. Five parameters were selected to predict the growth of selected pathogens during distribution period (maximum temperature from observation periods, pH, Aw, and initial log of Total Aerobic Plate Count (TAPC). The result showed that Clostridium perfringens, Listeria monocytogenes, Salmonella spp., Staphylococcus aureus, E.coli and pseudomonas spp count ranged from 0.11 log CFU/g to 1 log CFU/g during distribution at 19.5°C for 9.05 hours. Pseudomonas spp showed the highest growth (1 log cfu/g ) while Clostridium perfringens showed the lowest growth (0.11 log cfu/g ) after distribution. As a conclusion, higher the temperature and longer the observation period could increase the growth rate of selected pathogens on distribution the products.

 

Keywords

Chicken sausages, temperature distribution, predictive analysis, microbiological quality, pathogens

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