預測微生物學在冷藏食品流通過程中品質管理應用之探討

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Title:	預測微生物學在冷藏食品流通過程中品質管理應用之探討
Other Titles:	Studies on the application of predictive microbiology to monitor the qualitative variation of refrigerated foods in distribution chain
Authors:	洪名璟 Hung, Ming-Jing
Contributors:	李根永 Li, Ken-Yuon 東海大學食品科學系
Keywords:	預測微生物學;預測模式;模擬語言;品質管理;腸球菌 predictive microbiology;predictive model;simulation language
Date:	1995
Issue Date:	2011-05-25T09:41:11Z (UTC)
Abstract:	本研究以中式香腸為樣本，分別儲存於8、10、15、20、25℃，測定酸敗菌在5個不同定溫下之生長曲線，分別以高柏玆模式 (Gompertz model)及對數生長曲線迴歸模式來決定出生長參數：比生長速率(specific growth rate)及生長遲滯時間(lag time)，再以線性模式(linear model) 及平方根模式(square-root model)來估算其他溫度下之比生長速率及生長遲滯時間之預測值，以預測中式香腸中主要酸敗菌之生長菌數。將二種模式之預測菌數值與實際之觀測菌數值進行比較，找出較佳的模式來作為中式香腸主要酸敗菌之生長預測模式。結果發現使用對數生長曲線迴歸模式所得到的生長參數所獲得的比生長速率與溫度、以及生長遲滯時間與比生長速率倒數的線性關係式來預測恆定溫度（17℃）下之生長菌數與觀測值最為接近。最後將線性預測模式放入SLAM Ⅱ模擬語言，模擬流通過程中香腸主要酸敗菌的生長預測，以探討此方法是否能夠應用於流通過程中之品質管理。模擬結果發現，同一批製造的香腸，於儲藏過程中由於初菌數以及放置位置的不同，而造成展示期時間長短不一的情況。因此預測模式與模擬語言互相配合，對數據化管理而言是值得發展的的輔助工具。中式香腸的主要酸敗菌經分離培養，以 MicroStation System軟體加以比對鑑定，結果是Enterococcus faecalis的機率大於75%。 Experimental growth data of dominant spoilage micro- organism in Chinese sausage at various temperatures (8,10, 15,20,25 ℃) were fitted with modified-Gompertz model, and the specific growth rate as well as lag time were determined according to the parameters of the non-linear regression equation. Growth parameters were also derived from the log phase of growth curves directly. Two type of Belehradek models, linear model and square- root model, describing the effect of temperature on specific growth rate were compared. The linear model was found more adequate to predict the specific growth rate than that of square-root model. A SLAM Ⅱ(Simulation Language for Alternative Modeling) network-based model is developed to simulate the increasing of dominant spoilage microorganism in Chinese sausage in distribution chain. Believingly, the simulation data are helpful to decide the shelf life of refrigerated sausage, and monitor the qualitative variation of the products on retail shelf.
Appears in Collections:	[Department of Food Science ] Master's Theses

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