Study Of Microbial Growth Models Of TPC And Shelf Life Prediction For Chilled Beef

The chilled beef has become the mainstream of meat consumption for its safety, health and rich nutrition. Chilled beef can suffer accelerated metamorphism during processing, storage, transportation and sales as there are many microbial activities especially psychrophilic bacteria during chilled time. Consumers can not judge beef quality accurately only through sensary analysis while traditional microbial inspection is time-consuming and laborious. Mathematical models can control the spoilage of meat and grasp the growth rules of microbial organisms during different temperatures. Especially mathematical models can ensure the safety of products through inspecting the actual distribution and procession. Total plate count (TPC) can reflect the corruption situation in some extent and is the most commonly used microbial inspection in factories. Establishing TPC growth models and shelf life prediction models of chilled beef during aerobic storage time can provide some safety condition basis for meat plants. This study determined some kinds of quality indexes including TPC, TVB-N(Total Volotile Basic Nitrogen), colour a, colour DE, pH, water holding capacity and contents of psychrophilic bacteria on chilled beef segmented during storage under 4℃. The spoilage of TPC limit was confirmed according to equation of linear regression between TPC and TVB-N. Meanwhile main spoilage organisms were cultured and the correlation of TPC and spoilage organisms was analyzed. Modidied Gompertz model and square root model were established according to the experimental datas of TPC on chilled beef at 0℃, 4℃, 7℃, 10℃, 15℃and 20℃. Then the models were validated. Finally, the fluctuant models were established and validated as in actual processing the temperature is fluctuant. The predict model of shelf life was set up.The results showed that:1) With the increase of microbial counts on chilled beef during 4℃, physiochemical indexes made dynamic changes. During storage the pH at first declined and then rose. TVBN kept increasing. The redness a* value and DE (difference error) values of beef were found to gradually decline with the preservation time. TVB-N and redness a* value showed the best correlation with TPC. The water holding capacity and pH value were significantly correlated with TPC. Especially, the pearson indexe of TPC and TVB-N was maximum.And the correlated values between TPC and quality indexes are higher than psychrophilic count. 2) The pearson quotas between TPC and main spoilage organisms were best correlated.The result showed that it was available to model TPC in chilled beef as the pearson indexes between TPC and Pseudomonas, Acinetobacter, Lactobacillus, Carnobacterium were higher than 0.94. In some extent, TPC can reflect the quality of beef.3) The equation of linear regression of TPC and TVB-N was established y=4.347+0.2432x (R²=91.40% Pr=0.0029) according to the best correlation between TPC and TVB-N. The spoilage limit of TPC is 10^7.995cfu/g when TVB-N reached 15mg/100g.4) The Modified Gompertz models were set up in constant temperatures and R² values were greater than 0.95. The square root model was used to consider the maximum specific growth rate of TPC and temperature (μmax^1/2-t), and the secondary model of were obtained:μ^1/2=0.01412(t+6.859) (R²=0.9651).The macimum specific growth rate and temperature showed good linear relationship and residual values were fluctuant by 0. The experimental datas were in 95% confidence intervals between predicted upper limit and lower limit.5) The Modified models were validated at constant temperatures (4℃and 10℃) and fluctuant temperatures. The results showed that the predicted values fluctuated in a range of about 10%, and the differences between predicted and observed values (A_f) were less than 20%. So the predict models of TPC can predict the microbial quality of chilled beef during storage.6) The predictive model of shelf life was constructed as follows: SL=λ-[(8.346-N0)/μmax×2.718]×{ln [-ln[(7.995-N0)/(8.346-N0)]]-1}. The shelf life can be predicted from the equation if N0 and temperature T are determined.