环境因子对轻腌大黄鱼中溶藻弧菌生长/非生长界面的影响
|
摘要
为建立生长/非生长界面模型来预测溶藻弧菌在环境因子交互作用下的生长概率,探究环境因子对溶藻弧菌的生长的交互作用,该文选取轻腌大黄鱼室温25℃贮藏货架期终点分离的溶藻弧菌作为研究对象,研究25℃下p H值、水分活度(water activity,aw)和盐分对其生长概率的交互影响。使用Gompertz模型对其生长情况进行拟合,比较其生长动力学参数。用简单Logistic方程、二阶线性Logistic回归方程拟合及概率神经网络算法(probabilistic neural network,PNN)建立溶藻弧菌生长/非生长界面模型,使用正确率、假阳性率对其拟合优度进行比较。结果表明:二阶线性Logistic回归方程拟合效果更优,验证集的正确率为90.9%,PNN验证集正确率为90.0%。随着盐分的增大,生长/非生长的界限明显向低水分活度、低p H值方向移动;在相同盐分条件范围内,高水分活度且p H值较高条件下,比生长速率较高,延滞期也相应较短;随着盐分的增长,0.91与0.90低水分活度条件下溶藻弧菌也开始缓慢增长,但存在较长时间的延滞期,高盐分对溶藻弧菌有生长促进作用。研究表明:PNN可在工业生产中对溶藻弧菌的生长和非生长快速预测分类,通过二阶线性Logistic可评估p H值、aw和盐分许用范围内水产品的安全性。通过构建溶藻弧菌概率模型和动力学模型,可为改进贮藏条件和产品配方、确保轻腌大黄鱼质量安全提供支持。
The aim of the study was to develop a growth/no-growth interface model to predict the growth probability of Vibrio alginolyticus associated with lightly salted Pseudosciaena crocea under 3 environmental factors, and to explore the inhibitory effect of environmental factors on the growth kinetics of target micro-organism. The effects of p H value, water activity(aw) and NaC l content on the growth probability of the Vibrio alginolytica were studied at ambient temperature(25 ℃). At present, mainly the "fence technology" is used to change the growth environment of microorganisms by changing the water activity, salt, acetic acid, Nisin and sugar, so as to achieve the role of inhibition to microorganisms. Logistic regression is a commonly used method for simulating the microbial growth boundary(growth/non-growth interface) in food, through which the growth environment can be adjusted and the shelf life can be extended. Artificial neural network model PNN(probabilistic neural network) is a feed forward neural network with strong nonlinear pattern classification ability and high accuracy of nonlinear algorithm, which can solve the growth/non-growth interface problems, and the PNN has simple structure and high training speed without considering the complex chemical reaction during storage. Simple logistic equation, second-order linear logistic regression equation and PNN artificial neural network model were used to establish the growth/non-growth interface model of Vibrio alginolyticus, while fraction correct(FC) and false alarm rate(FAR) were used to compare the goodness of fit of the 3 models. The Gompertz model was used to fit the growth condition, and the growth kinetics parameters were obtained. The results showed that the second-order linear logistic regression equation had better fitting results, the consistency index of the training set was 94.8%, and that of the validation set was 90.9%, while the consistency index of the PNN artificial neural network was 95.6% and 90.0% for the training and validation set, respectively. The FAR of the second-order linear logistic regression equation was 5%(training set) and 0(validation set), while that of the PNN artificial neural network was 6.6%(training set) and 22%(validation set). The effects of the environmental factors were as follows: With the increase of salt content, the growth/no-growth boundary obviously moved to low water activity and low pH value. In the same salty condition, in the range of high aw and high pH value, the growth rate was higher and the retardation period was shorter. With the increase of salt content, even under low aw such as 0.91 and 0.90, the Vibrio alginolyticus also began to grow slowly, but there was a long lag time. The conclusions are obtained: PNN artificial neural network can do quick classification prediction on the growth/no-growth data of Vibrio alginolyticus in the industrial production, and the second-order linear logistic regression can evaluate the stability of aquatic products under the conditions of aw, pH value and salt content. By constructing the probabilistic models and kinetic models of Vibrio alginolyticus which can assess the stability of characteristic aquatic products in the range of p H value, aw and salt content, it can provide the guide to suppress microorganisms without the use of chemical preservatives to ensure quality and safety of pickled Pseudosciaena crocea.
The aim of the study was to develop a growth/no-growth interface model to predict the growth probability of Vibrio alginolyticus associated with lightly salted Pseudosciaena crocea under 3 environmental factors, and to explore the inhibitory effect of environmental factors on the growth kinetics of target micro-organism. The effects of p H value, water activity(aw) and NaC l content on the growth probability of the Vibrio alginolytica were studied at ambient temperature(25 ℃). At present, mainly the "fence technology" is used to change the growth environment of microorganisms by changing the water activity, salt, acetic acid, Nisin and sugar, so as to achieve the role of inhibition to microorganisms. Logistic regression is a commonly used method for simulating the microbial growth boundary(growth/non-growth interface) in food, through which the growth environment can be adjusted and the shelf life can be extended. Artificial neural network model PNN(probabilistic neural network) is a feed forward neural network with strong nonlinear pattern classification ability and high accuracy of nonlinear algorithm, which can solve the growth/non-growth interface problems, and the PNN has simple structure and high training speed without considering the complex chemical reaction during storage. Simple logistic equation, second-order linear logistic regression equation and PNN artificial neural network model were used to establish the growth/non-growth interface model of Vibrio alginolyticus, while fraction correct(FC) and false alarm rate(FAR) were used to compare the goodness of fit of the 3 models. The Gompertz model was used to fit the growth condition, and the growth kinetics parameters were obtained. The results showed that the second-order linear logistic regression equation had better fitting results, the consistency index of the training set was 94.8%, and that of the validation set was 90.9%, while the consistency index of the PNN artificial neural network was 95.6% and 90.0% for the training and validation set, respectively. The FAR of the second-order linear logistic regression equation was 5%(training set) and 0(validation set), while that of the PNN artificial neural network was 6.6%(training set) and 22%(validation set). The effects of the environmental factors were as follows: With the increase of salt content, the growth/no-growth boundary obviously moved to low water activity and low pH value. In the same salty condition, in the range of high aw and high pH value, the growth rate was higher and the retardation period was shorter. With the increase of salt content, even under low aw such as 0.91 and 0.90, the Vibrio alginolyticus also began to grow slowly, but there was a long lag time. The conclusions are obtained: PNN artificial neural network can do quick classification prediction on the growth/no-growth data of Vibrio alginolyticus in the industrial production, and the second-order linear logistic regression can evaluate the stability of aquatic products under the conditions of aw, pH value and salt content. By constructing the probabilistic models and kinetic models of Vibrio alginolyticus which can assess the stability of characteristic aquatic products in the range of p H value, aw and salt content, it can provide the guide to suppress microorganisms without the use of chemical preservatives to ensure quality and safety of pickled Pseudosciaena crocea.
引文
[1]许钟,郭全友.淡腌大黄鱼贮藏中的品质变化及腐败菌分析[J].食品科学,2008,29(12):697-700.Xu Zhong,Guo Quan-you,Quality changes and spoilage bacteria in light salted pseudosciaena crocea during Storage[J],Food science,2008,29(12):697-700.(in Chinese with English abstract)
[2]郭全友,王锡昌,杨宪时.不同贮藏温度下养殖大黄鱼货架期预测模型的构建[J].农业工程学报,2012,28(10):267-273.Guo Quanyou,Wang Xichang,Yang Xianshi.Predictive model construction of shelf life for cultured Pseudosciaena crocea stored at different temperatures[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2012,28(10):267-273.(in Chinese with English abstract)
[3]Hilmer A F,John D B,Malin C,et al.Identification and decarboxylase activities of bacteria isolated from decomposed mahimahi(Coryphaena hippurus)after incubation at 0 and 32C[J].International Journal of Food Microbiology,1985,(6):331-340.
[4]程亚琪,隋凌云.一起溶藻弧菌引起的食物中毒[J].临床医药文献电子杂志,2014,1(11):2043Chen Yaqi,Sui Lingyun,With food poisoning caused by Vibrio alginolyticus[J],Electronic Journal of Medicine clinical literature,2014,1(11):2043.(in Chinese with English abstract)
[5]Hormansdorfer S,Wentges H,Neugebaur K,et al.Isolation of Vibrio alginolyticus from seawater aquaria[J].International Journal of Hygiene and Environmental Health,2000,203(2):169-175.
[6]Kaneko T,Colwell R..Distribution of Vibrio parahaemolyticus and Related Organisms in the Atlantic Ocean off South Carolina and Georgia[J].Applied and Environmental Microbiology.1974,28(6):1009-1017.
[7]Dang T D T,Mertens L,Vermeulen A,et al.Modelling the growth/no growth boundary of Zygosaccharomyces bailii in acidic conditions:A contribution to the alternative method to preserve foods without using chemical preservatives[J].International Journal of Food Microbiology,2010,137(1):1-12.
[8]Garcia D,Ramos A J,Sanchis V,et al.Predicting mycotoxins in foods:a review.[J].Food Microbiolgy,2009,26(8):757-769.
[9]Sosa-Morales M E,Garcia H S,López-Malo A.Colletotrichum gloeosporioides growth-no growth interface after selected microwave treatments[J].Journal of food Protection,2009,72(7):1427-1433.
[10]Theys T E,Geeraerd A H,Devlieghere F,et al.On the selection of relevant environmental factors to predict microbial dynamics in solidified media[J].Food Microbiology,2010,27(2):220-228.
[11]Bolton L F,Frank J F.Defining the growth/no-growth interface for Listeria monocytogenes in Mexican-style cheese based on salt,p H,and moisture content[J].Journal of Food Protection,1999,62(6):601-609.
[12]Koutsoumanis P K,Sofos N J.Effect of inoculum size on the combined temperature,p H and aw limits for growth of Listeria monocytogenes[J].International Journal of Food Microbiology,2006,104(1):83-91.
[13]Skandamis P N,Stopforth J D,Kendall P A.Modelling the effect of inoculum size and acid adaptation on growth/no growth interface of Escherichia coli O157:H7[J].International Journal of Food Microbiology,2007,(3),237-249.
[14]Marín S,Hodzic I,Ramos A J,et al.Predicting the growth/no growth boundary and ochratoxin A production by Aspergillus carbonarius in pistachio nuts[J].Food Microbiology.2008,25(5),683-689.
[15]Presser K A,Ross T,Ratkowsky D A.Modelling the growth limits(growth no growth interface)of Escherichia coli as a function of temperature,p H,lactic acid concentration,and water activity[J].Applied and Environmental Microbiology,1998,64(5):1773-1779.
[16]Lanciotti R,Sinigaglia M,Gardini F,et al.Growth/no growth interfaces of Bacillus cereus,Staphylococcus aureus and Salmonella enteritidis in model systems based on water activity,p H,temperature and ethanol concentration[J].Food Microbiology,2001,18(6):659-668.
[17]Fernandez-Navarro F,Valero A,Hervas-Martinez C.Development of a multi-classification neural network model to determine the microbial growth/no growth interface[J].International Journal of Food Microbiology,2010,141(3):203-212.
[18]Deschuyffeleer N,Vermeulen A,Daelman J.Modelling of the growth/no growth interface of Wallemia sebi and Eurotium herbariorum as a function of p H,aw and ethanol concentration[J].International Journal of Food Microbiology,2015(192):77-85.
[19]Simon L,Karim M N.Probabilistic neural networks using Bayesian decision strategies and a modified Gompertz model for growth phase classification in the batch culture of Bacillus subtilis[J].Biochemistry Engineer,2001,7(1):41-48.
[20]Hajmeer M,Basheer I.A probabilistic neural network approach for modeling and classification of bacterial growth/no-growth data[J].Journal of Microbiological Methods,2002,51(2):217-226.
[21]Parthena Kotzekidou,Chapter 3-Factors influencing microbial safety of ready-to-eat foods,In Food Hygiene and Toxicology in Ready-to-Eat Foods[M]San Diego,Academic Press,2016:33-50.
[22]陈琛,李学英,杨宪时,等.环境因子交互作用下蜡样芽孢杆菌生长/非生长界面模型的建立与评价[J].现代食品科技,2015,31(12):205-213.Chen Chen,Li Xueying,Yang Xiangshi,et al.Modeling and Evaluating the Growth/No Growth Boundaries of Bacillus cereus:Effect of Temperature,p H,and Water Activity[J].Modern Food Science and Technology,2015,31(12):205-213.(in Chinese with English abstract)
[23]Ce Yu,Valerie J.Davidson,Simon X.Yang.A neural network approach to predict survival/death and growth/no-growth interfaces for Escherichia coli O157:H7[J]Food Microbiology,2006(23):552–560.
[24]渠飞翔,李学英,杨宪时.软烤贻贝中蜡样芽孢杆菌生长/非生长界面模型建立与评价[J].食品与机械,2016,32(4):143-147.Qu Feixiang,Li Xueying,Yang Xianshi.Modeling and evaluating on growth/no growth boundaries of Bacilus cereus on soft-baked musels[J].Food&Machinery,2016,32(4):144-147.(in Chinese with English abstract)
[25]赵学广,李学英,杨宪时.三因子金黄色葡萄球菌生长/非生长模型构建[J].食品与机械,2016,32(3):122-126.Zhao Xueguang,Li Xueying,Yang Xianshi.Establishment of Model on growth/no growth boundary of Staphylococcus aureus based for three factors[J].Food&Machinery,2016,32(3):123-126.(in Chinese with English abstract)
[26]Marvig C L,Kristiansen R M,Nielsen D S.Growth/no growth models for Zygosaccharomyces rouxii associated with acidic,sweet intermediate moisture food products[J].International Journal of Food Microbiology,2015,192(2):51-57.
[27]孙俊,路心资,张晓东,等.基于高光谱图像的红豆品种GA-PNN神经网络鉴别[J].农业机械学报,2016,47(6):215-221.Sun Jun,Lu Xinzi,Zhang Xiaodong,et al.Identification of Red Bean Variety with Probabilistic GA-PNN Based on Hyperspectral Imaging[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(6):215-221.
[28]赵桂艳,李一峰.基于PNN神经网络和聚类分析的变压器故障诊断[J].信息系统工程,2015,28(6):94-95.Zhao Guiyan,Li Yifeng.PNN transformer fault diagnosis based on neural network and cluster analysis[J].Information Systems Engineering,2015,28(6):94-95.
[29]Hajmeer M N,Basheer I A.A hybrid Bayesian–neural network approach for probabilistic modeling of bacterial growth/no-growth interface[J].International Journal of Food Microbiology,2003,82(3),233–243.
[30]Valero A,Carrasco E,Perez-Rodriguez F,et al.Growth/no growth model of Listeria monocytogenes as a function of temperature,p H,citric acid and ascorbic acid[J].European Food Research and Technology,2006,224(1):91-100.
[31]修艳辉,郭全友,姜朝军.p H、水分活度和Na Cl对腐败希瓦氏菌生长/非生长界限及生长动力学参数的影响[J].现代食品科技,2016,32(6):156-162+199.Xiu Yanhui,Guo Quanyou,Jiang Chaojun.Effect of p H,water activity and common salt on the growth/no growth boundary and growth kinetic parameters of Shewanella putrefaciens[J].Modern Food Science and Technology,2016,32(6):156-162.(in Chinese with English abstract)
[32]朱彦祺,郭全友,李保国,等.不同温度下腐败希瓦氏菌(Shewanela putrefaciens)生长动力学模型的比较与评价[J].食品科学,2016,37(13):147-152.Zhu Yanqi,Guo Quanyou,Li Baoguo,et al.Comparison and evaluation of models for the growth of Shewanella putrefaciens at different temperatures[J].Food Science,2016,37(13):147-152.(in Chinese with English abstract)
[2]郭全友,王锡昌,杨宪时.不同贮藏温度下养殖大黄鱼货架期预测模型的构建[J].农业工程学报,2012,28(10):267-273.Guo Quanyou,Wang Xichang,Yang Xianshi.Predictive model construction of shelf life for cultured Pseudosciaena crocea stored at different temperatures[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2012,28(10):267-273.(in Chinese with English abstract)
[3]Hilmer A F,John D B,Malin C,et al.Identification and decarboxylase activities of bacteria isolated from decomposed mahimahi(Coryphaena hippurus)after incubation at 0 and 32C[J].International Journal of Food Microbiology,1985,(6):331-340.
[4]程亚琪,隋凌云.一起溶藻弧菌引起的食物中毒[J].临床医药文献电子杂志,2014,1(11):2043Chen Yaqi,Sui Lingyun,With food poisoning caused by Vibrio alginolyticus[J],Electronic Journal of Medicine clinical literature,2014,1(11):2043.(in Chinese with English abstract)
[5]Hormansdorfer S,Wentges H,Neugebaur K,et al.Isolation of Vibrio alginolyticus from seawater aquaria[J].International Journal of Hygiene and Environmental Health,2000,203(2):169-175.
[6]Kaneko T,Colwell R..Distribution of Vibrio parahaemolyticus and Related Organisms in the Atlantic Ocean off South Carolina and Georgia[J].Applied and Environmental Microbiology.1974,28(6):1009-1017.
[7]Dang T D T,Mertens L,Vermeulen A,et al.Modelling the growth/no growth boundary of Zygosaccharomyces bailii in acidic conditions:A contribution to the alternative method to preserve foods without using chemical preservatives[J].International Journal of Food Microbiology,2010,137(1):1-12.
[8]Garcia D,Ramos A J,Sanchis V,et al.Predicting mycotoxins in foods:a review.[J].Food Microbiolgy,2009,26(8):757-769.
[9]Sosa-Morales M E,Garcia H S,López-Malo A.Colletotrichum gloeosporioides growth-no growth interface after selected microwave treatments[J].Journal of food Protection,2009,72(7):1427-1433.
[10]Theys T E,Geeraerd A H,Devlieghere F,et al.On the selection of relevant environmental factors to predict microbial dynamics in solidified media[J].Food Microbiology,2010,27(2):220-228.
[11]Bolton L F,Frank J F.Defining the growth/no-growth interface for Listeria monocytogenes in Mexican-style cheese based on salt,p H,and moisture content[J].Journal of Food Protection,1999,62(6):601-609.
[12]Koutsoumanis P K,Sofos N J.Effect of inoculum size on the combined temperature,p H and aw limits for growth of Listeria monocytogenes[J].International Journal of Food Microbiology,2006,104(1):83-91.
[13]Skandamis P N,Stopforth J D,Kendall P A.Modelling the effect of inoculum size and acid adaptation on growth/no growth interface of Escherichia coli O157:H7[J].International Journal of Food Microbiology,2007,(3),237-249.
[14]Marín S,Hodzic I,Ramos A J,et al.Predicting the growth/no growth boundary and ochratoxin A production by Aspergillus carbonarius in pistachio nuts[J].Food Microbiology.2008,25(5),683-689.
[15]Presser K A,Ross T,Ratkowsky D A.Modelling the growth limits(growth no growth interface)of Escherichia coli as a function of temperature,p H,lactic acid concentration,and water activity[J].Applied and Environmental Microbiology,1998,64(5):1773-1779.
[16]Lanciotti R,Sinigaglia M,Gardini F,et al.Growth/no growth interfaces of Bacillus cereus,Staphylococcus aureus and Salmonella enteritidis in model systems based on water activity,p H,temperature and ethanol concentration[J].Food Microbiology,2001,18(6):659-668.
[17]Fernandez-Navarro F,Valero A,Hervas-Martinez C.Development of a multi-classification neural network model to determine the microbial growth/no growth interface[J].International Journal of Food Microbiology,2010,141(3):203-212.
[18]Deschuyffeleer N,Vermeulen A,Daelman J.Modelling of the growth/no growth interface of Wallemia sebi and Eurotium herbariorum as a function of p H,aw and ethanol concentration[J].International Journal of Food Microbiology,2015(192):77-85.
[19]Simon L,Karim M N.Probabilistic neural networks using Bayesian decision strategies and a modified Gompertz model for growth phase classification in the batch culture of Bacillus subtilis[J].Biochemistry Engineer,2001,7(1):41-48.
[20]Hajmeer M,Basheer I.A probabilistic neural network approach for modeling and classification of bacterial growth/no-growth data[J].Journal of Microbiological Methods,2002,51(2):217-226.
[21]Parthena Kotzekidou,Chapter 3-Factors influencing microbial safety of ready-to-eat foods,In Food Hygiene and Toxicology in Ready-to-Eat Foods[M]San Diego,Academic Press,2016:33-50.
[22]陈琛,李学英,杨宪时,等.环境因子交互作用下蜡样芽孢杆菌生长/非生长界面模型的建立与评价[J].现代食品科技,2015,31(12):205-213.Chen Chen,Li Xueying,Yang Xiangshi,et al.Modeling and Evaluating the Growth/No Growth Boundaries of Bacillus cereus:Effect of Temperature,p H,and Water Activity[J].Modern Food Science and Technology,2015,31(12):205-213.(in Chinese with English abstract)
[23]Ce Yu,Valerie J.Davidson,Simon X.Yang.A neural network approach to predict survival/death and growth/no-growth interfaces for Escherichia coli O157:H7[J]Food Microbiology,2006(23):552–560.
[24]渠飞翔,李学英,杨宪时.软烤贻贝中蜡样芽孢杆菌生长/非生长界面模型建立与评价[J].食品与机械,2016,32(4):143-147.Qu Feixiang,Li Xueying,Yang Xianshi.Modeling and evaluating on growth/no growth boundaries of Bacilus cereus on soft-baked musels[J].Food&Machinery,2016,32(4):144-147.(in Chinese with English abstract)
[25]赵学广,李学英,杨宪时.三因子金黄色葡萄球菌生长/非生长模型构建[J].食品与机械,2016,32(3):122-126.Zhao Xueguang,Li Xueying,Yang Xianshi.Establishment of Model on growth/no growth boundary of Staphylococcus aureus based for three factors[J].Food&Machinery,2016,32(3):123-126.(in Chinese with English abstract)
[26]Marvig C L,Kristiansen R M,Nielsen D S.Growth/no growth models for Zygosaccharomyces rouxii associated with acidic,sweet intermediate moisture food products[J].International Journal of Food Microbiology,2015,192(2):51-57.
[27]孙俊,路心资,张晓东,等.基于高光谱图像的红豆品种GA-PNN神经网络鉴别[J].农业机械学报,2016,47(6):215-221.Sun Jun,Lu Xinzi,Zhang Xiaodong,et al.Identification of Red Bean Variety with Probabilistic GA-PNN Based on Hyperspectral Imaging[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(6):215-221.
[28]赵桂艳,李一峰.基于PNN神经网络和聚类分析的变压器故障诊断[J].信息系统工程,2015,28(6):94-95.Zhao Guiyan,Li Yifeng.PNN transformer fault diagnosis based on neural network and cluster analysis[J].Information Systems Engineering,2015,28(6):94-95.
[29]Hajmeer M N,Basheer I A.A hybrid Bayesian–neural network approach for probabilistic modeling of bacterial growth/no-growth interface[J].International Journal of Food Microbiology,2003,82(3),233–243.
[30]Valero A,Carrasco E,Perez-Rodriguez F,et al.Growth/no growth model of Listeria monocytogenes as a function of temperature,p H,citric acid and ascorbic acid[J].European Food Research and Technology,2006,224(1):91-100.
[31]修艳辉,郭全友,姜朝军.p H、水分活度和Na Cl对腐败希瓦氏菌生长/非生长界限及生长动力学参数的影响[J].现代食品科技,2016,32(6):156-162+199.Xiu Yanhui,Guo Quanyou,Jiang Chaojun.Effect of p H,water activity and common salt on the growth/no growth boundary and growth kinetic parameters of Shewanella putrefaciens[J].Modern Food Science and Technology,2016,32(6):156-162.(in Chinese with English abstract)
[32]朱彦祺,郭全友,李保国,等.不同温度下腐败希瓦氏菌(Shewanela putrefaciens)生长动力学模型的比较与评价[J].食品科学,2016,37(13):147-152.Zhu Yanqi,Guo Quanyou,Li Baoguo,et al.Comparison and evaluation of models for the growth of Shewanella putrefaciens at different temperatures[J].Food Science,2016,37(13):147-152.(in Chinese with English abstract)