Study On Controlling Saccharomyces Cerevisiae In Orange Juice With Predictive Microbiology

Concentrated orange juice was in lack and the price was high in world wide. In China, the policy encouraged profoundly process of argricultural product, and nationl orange juice market was expanding. A new wave of development for orange juice industry would appear in China in the future.But in the orange juice process, raw material carring, devices and staff contamination always made the juice contaminated by microorganisims easily. The application of predictive microbiology on controlling orange juice spoilage would offer a theoretical precondition for spoilage control; direct the production and consumption of orange juice. The reseach method would be helpful for quality control of other juice beverages.Three strains of spoilage microorganisms were separated from spoilage 100% orange juice sold in market. After regular observation, they were identified as yeast. With bioMerieux Vitek system, the three strains were identified as Saccharomyces cerevisiae. Saccharomyces cerevisiae, No. CICIM Y0086, was obtained from CICIM in Jiangnan University. Its capacity of spoilage to orange juice was similaer with the strain separated from orange juice. So, CICIM Y0086 was treated as the modeling microorganism of spoilage orange juice.Several brands of orange juice were analyzed, including pH, content of reducing sugar and temperatures. Growth curves were drew to compare the differences of Saccharomyces cerevisiae growth in different factors and levels. Initial concentration of yeast was also considered. Finally, temperature was confirmed to be the critical fator which influent the growth of Saccharomyces cerevisiae in orange juice. It was also the critical factor which should be concerned in the predictive model.In the process of building model, Gompertz model had a better fitness than Logistic model in first grade model. The predictive equations in first grade model under typical temperatures as follow: 5℃: f(t)=0.4669+0.3378×exp(-exp(-0.0969×(t-9.6150))); 8℃: f(t)=0.4669+3.0030×exp(-exp(-0.0320×(t-32.2900))); 25℃:f(t)=0.4669+7.6190×exp(-exp(-0.0695×(t-23.5200))); 35℃:f(t)=0.4669+7.5060×exp(-exp(-0.0622×(t-24.5200))).After optimizing the classic square root model, the second grade model was: k=-0.0004T2 + 0.0215T-0.1033.In the application study of predictive model, the predicton of Lag phase duration for Saccharomyces cerevisiae under 25℃and 35℃were confirmed. The prediction of yeast growth paraments in second grade model was reliable. The connection between growth model and alcohol produing model enlarged the application extension of predictive model.