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研究生:陳映蓉
研究生(外文):Chen, Ying-Rong
論文名稱:腸炎弧菌之預測生長模型發展-以冷藏白蝦為例
論文名稱(外文):Development of Predictive Growth Model for Vibrio parahaemolyticus: Illustrated by Chilled White Shrimp
指導教授:蕭心怡蕭心怡引用關係
指導教授(外文):Hsiao, Hsin-I
口試委員:蕭心怡楊振昌鄭光成潘崇良
口試委員(外文):Hsiao, Hsin-IYang, Chen-ChangCheng, Kuan-ChenPan, Chorng-Liang
口試日期:2015-09-14
學位類別:碩士
校院名稱:國立臺灣海洋大學
系所名稱:食品科學系
學門:農業科學學門
學類:食品科學類
論文種類:學術論文
論文出版年:2015
畢業學年度:104
語文別:英文
論文頁數:66
中文關鍵詞:腸炎弧菌白蝦生長預測模型
外文關鍵詞:Vibrio parahaemolyticusWhite shrimpPredictive model
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Vibrio parahaemolyticus is the major cause for food poisoning in Taiwan. Predictive microbiology can be applied in microbiological food research, product development, hazard analysis and critical control points (HACCP) and quantitative microbiological risk assessment (QMRA). Nowadays, research regarding prediction of V. parahaemolyticus on shrimp is very limited. Thus, the purpose of this study is to develop a predictive growth model of V. parahaemolyticus on chilled white shrimp. For describing the primary growth parameters during storage, the Buchana three-phase linear model was used to regress the growth curves, in 12, 15, 20, 25, 30, 35ºC, the growth rates (GRs) were 0.15, 0.11, 0.25, 0.43, 0.71, 1.11 log cfu/h, the lag times (LGs) were 7.5, 6.43, 5.12, 2.42, 1.01 hours, respectively. In secondary model, the GRs had a better description by polynomial model than linear regression, and the LTs were described by linear regression. The R2 were 0.998 and 0.987, RMSE were 0.018, 0.278, respectively, indicating GRs and LTs were good fit to the observed data. In external validation, three additional temperatures, 17, 23, 32ºC, were used to compare the GRs and LTs with prediction. The RMSE, Bf and Af of GRs were 0.083, 1.150, 1.272, and the RMSE, Bf and Af of LTs were 0.589, 0.880, 1.136, respectively. In comparison with literature, the V. parahaemolyticus’ growth speed was faster in white shrimp than oyster and salmon. Linear heating test from 12 to 35°C include two methods (A and B), method A was considered lag time predictions, while method B was not. If the linear heating test within 8 hours, there’s better predictions with using method A. On the contrary, if the linear heating time over than 8 hours, method B could provide better predictions. Besides, no matter how long does the linear heating test take, method A could provide a good overall lag time prediction. Overall, this model provided a acceptable predicted growth model, which can describe the growth of V. parahaemolyticus in chilled shrimp between 12 to 35ºC.
腸炎弧菌 (Vibiro parahaemolyticus) 是臺灣主要引起細菌性食品中毒之病原菌,而預測微生物學可被應用於食品微生物研究、產品開發、危害分析重要管制點與風險評估等研究。現今有關於蝦中腸炎弧菌之預測微生物研究十分地有限,因此,本研究目的為建立冷藏白蝦中腸炎弧菌之生長預測模型。於一級模型部分,使用 Buchana 三期線性模型描述儲藏於不同溫度的生長曲線,並將所得生長參數(生長速率、遲滯時間)進行二級模型探討。在12、15、20、25、30、35ºC 下,生長速率分別為每小時 0.15、0.11、0.25、0.43、0.71、1.11 log cfu,而遲滯時間分別為7.5、6.43、5.12、2.42、1.01 小時。二級模型部分,生長速率方面,與線性迴歸相比,利用多項式模型可較良好地描述12–35ºC 之生長速率;另外,使用線性迴歸即能夠良好的描述遲滯時間,也就是說生長速率與遲滯時間皆能準確的描述本研究的數據,R2值分別為0.998、0.987,RMSE值為0.018、0.278。外部驗證部分,本實驗另外發展了 3 個額外的溫度 (17、23、32 ºC) 之初級生長參數,與預測結果相比後得知,生長速率之 RMSE、Bf、Af 值分別為0.083、1.150、1.272;遲滯時間之 RMSE、Bf、Af 值分別為0.589、0880、1.136。另於文獻比較的組合可知,腸炎弧菌在白蝦中生長速度較牡蠣與鮭魚快。在12至35ºC直線加熱驗證部分,可分為考慮遲滯時間(方法A)與不考慮遲滯時間(方法B)兩方法。若直線加熱時間於8小時內,使用方法A可得到較良好的預測結果;相反地,若直線加熱時間大於8小時,則建議使用方法B。此外,無論在多長時間的直線加熱下,方法A都能良好的預測整體的遲滯時間。總言之,本研究提供良好的冷藏白蝦中腸炎弧菌生長預測模型,可預估在12 至 35ºC 間腸炎弧菌生長情形。
Abstract I
摘 要 II
Table of content III
List of tables V
List of figures VI
Ⅰ. Introduction 1
1.1 Research background 1
1.2 The purpose of research 3
1.3 The flow diagram of research 4
Ⅱ. Literature Review 5
2.1 Vibrio parahaemolyticus 5
2.1.1 Growth characteristics 5
2.1.2 Pathogenic characteristics 5
2.1.3 Vibrio parahaemolyticus in seafood 6
2.1.3.1 Shrimp 6
2.1.3.2 Inhibitor 6
2.1.4 Outbreaks worldwide 7
2.1.4.1 Food poisoning statistics in Taiwan 7
2.2 Fishery industry 9
2.2.1 Taiwan’s fishery industry 9
2.2.1.1 Shrimp 9
2.2.2 Food safety of seafood 10
2.3 Predictive microbiology 11
2.3.1 The predictive microbiology 11
2.3.2 History of predictive microbiology 11
2.3.3 Development of predictive model 12
2.3.4 Validation of predictive models 13
2.3.4.1 Goodness-of-fit 13
Ⅲ. Materials and methods 14
3.1 Materials 14
3.1.1 Strains 14
3.1.2 Shrimp 14
3.1.3 Mediums and Chemicals 14
3.1.4 Instruments 15
3.2 Methods 16
3.2.1 Sample pretreatment 16
3.2.2 Gamma irradiation 16
3.2.3 Microorganism test 16
3.2.3.1 Aerobic plate count 16
3.2.3.2 Vibrio parahaemolyticus tests 17
3.2.3.3 Halophilic bacterium tests 17
3.2.4 Inoculation 17
3.2.5 Development of primary model 17
3.2.6 Development of secondary model 18
3.2.7 Validation the predictive model 18
3.2.7.1 Internal validation 19
3.2.7.2 External validation 19
3.2.7.2.1 Additional temperatures tests 19
3.2.7.2.2 Literature comparison 19
3.2.7.2.3 Linear heating tests 20
3.2.8 Statistical analysis 20
Ⅳ. Results and discussion 21
4.1 Primary model 21
4.1.1 Eliminate background microorganism 21
4.1.2 Primary parameters 21
4.2 Secondary model 22
4.2.1 Growth rate 22
4.2.2 Lag time 22
4.3 Validation 23
4.3.1 Internal validation 23
4.3.2 External validation 23
4.3.2.1 Additional temperatures tests 23
4.3.2.2 Literature comparison 24
4.3.2.3 Linear heating tests 24
4.4 Limitations 26
Ⅴ. Conclusion 27
Ⅵ. Reference 28
Appendix 65
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