储藏期金华火腿中优势霉菌生长预测模型的建立及其应用
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摘要
对我国八种金华火腿自然块做了微生物菌群分布基本研究及主要菌群——霉菌的分离鉴定，并就霉菌中主要菌群在储藏期生长变化情况做了研究。其主要目的是为了确定生长预测模型中的目的微生物。根据逐次递进的方法，按照菌种生物量在总菌落中的优势性，产生毒素及在火腿中检出率稳定的要求，确定了目的微生物为杂色曲霉。
按照推荐的火腿储藏低温及霉菌适宜生长温度范围，选取五种不同温度建立生长预测模型。结果表明杂色曲霉在4℃、10℃温度条件下，用Linear模型和Quadratic模型拟合效果较好，且后者优于前者；在20℃、28℃和30℃条件下，用经典Gompertz模型和Logistic模型拟合效果较好。经过t检验和F检验过程验证40个生长曲线样本，Gompertz模型被接受度最好，t检验达到100％接受度，而F检验达到78％。
模型数据与实验数据进行对比，验证模型的有效性。结果表明，在低温4℃和10℃条件下，采用最佳拟合模型——Quadratic模型验证，在较高温度20℃、28℃和30℃条件下，采用最佳拟合模型——Gompertz模型验证，总效果在50％～100％之间。相对而言，Gompertz模型与实验数据的拟合效果更好。
采用模型模拟数据，考虑杂色曲霉毒素产生的条件简单且杂色曲霉产毒率为80％左右，并结合生物量优势，提出了金华火腿中杂色曲霉的风险内容及未来评估方向等。该课题研究主要是建立杂色曲霉的生长预测模型，并没有针对其中毒素建立模型，这成为评价毒素危害最为不利的因素，原因一为建立毒素预测模型难度很大且有效性有待验证；二是微生物风险评估目前只能进行定性分析。但总而言之，该课题的风险评估所提出的观点在适用范围内是科学合理的。
The paper has accomplished research on the elementary pattern of microflora, isolation and identification of main fungal-mould with experiment material of eight kinds of JinHua ham pieces, as well as the growth change of genera during shelf-life. The purpose was to confirm the objective microorganism in growth predictive model. According to the gradual-approach method, the requirement that the genera number prevails over the whole microorganism, mycotoxins metabolizing during shelf-life, and the steady rate of being picked out, the Aspergillus versicolor has been designated as the objective microorganism.
As the recommended ham's storage temperature and mould's optimal temperature range, there were five different temperatures determined to establish growth predictive models. The results showed that Linear model and Quadratic model were much more simulated the observed data of Aspergillus versicolor at 4
℃ and 10℃, in addition, the latter was verified to be better than the formal. At 20℃, 28℃ and 30℃, the effects of simulation by classical Gompertz model and Logistic model had gained better results. After verifying 40 samples of growth curve by F and t test, Gompertz model has been testified as the most available model with 100% acceptance by t test and 78% acceptance by F test.
The experiment compared the data by simulated with that by observed, in order to validating the effectiveness of models. The results showed that the entire validity has gained 50 %~100 %, when using Quadratic model at 4℃ and 10℃ and Gompertz model at 20℃, 28℃ and 30℃. In a conclusion, the effect of simulation of Gompertz model was better correspondingly.
According to the simulate data from model, considering the facts that the condition metabolizing mycotoxins of Aspergillus versicolor was easy correspondingly, as well as the 80 % of metabolizing mycotoxins rate, I bringed forward the risk contents and the assessment future orientation of Aspergillus versicolor in Jinhua ham. The predictive models established, which were mainly adapted to simulate the biomass growth of
Aspergillus versicolor but the mycotoxins, were greatly limit to assess the hazard of mycotoxins. Reasons were effectiveness and difficulty of establishing mycotoxins models, and microorganism's actual assessment that was just based on the nature of evaluate target. However, the viewpoints and conclusion advanced in risk assessment were effective and rational.
按照推荐的火腿储藏低温及霉菌适宜生长温度范围，选取五种不同温度建立生长预测模型。结果表明杂色曲霉在4℃、10℃温度条件下，用Linear模型和Quadratic模型拟合效果较好，且后者优于前者；在20℃、28℃和30℃条件下，用经典Gompertz模型和Logistic模型拟合效果较好。经过t检验和F检验过程验证40个生长曲线样本，Gompertz模型被接受度最好，t检验达到100％接受度，而F检验达到78％。
模型数据与实验数据进行对比，验证模型的有效性。结果表明，在低温4℃和10℃条件下，采用最佳拟合模型——Quadratic模型验证，在较高温度20℃、28℃和30℃条件下，采用最佳拟合模型——Gompertz模型验证，总效果在50％～100％之间。相对而言，Gompertz模型与实验数据的拟合效果更好。
采用模型模拟数据，考虑杂色曲霉毒素产生的条件简单且杂色曲霉产毒率为80％左右，并结合生物量优势，提出了金华火腿中杂色曲霉的风险内容及未来评估方向等。该课题研究主要是建立杂色曲霉的生长预测模型，并没有针对其中毒素建立模型，这成为评价毒素危害最为不利的因素，原因一为建立毒素预测模型难度很大且有效性有待验证；二是微生物风险评估目前只能进行定性分析。但总而言之，该课题的风险评估所提出的观点在适用范围内是科学合理的。
The paper has accomplished research on the elementary pattern of microflora, isolation and identification of main fungal-mould with experiment material of eight kinds of JinHua ham pieces, as well as the growth change of genera during shelf-life. The purpose was to confirm the objective microorganism in growth predictive model. According to the gradual-approach method, the requirement that the genera number prevails over the whole microorganism, mycotoxins metabolizing during shelf-life, and the steady rate of being picked out, the Aspergillus versicolor has been designated as the objective microorganism.
As the recommended ham's storage temperature and mould's optimal temperature range, there were five different temperatures determined to establish growth predictive models. The results showed that Linear model and Quadratic model were much more simulated the observed data of Aspergillus versicolor at 4
℃ and 10℃, in addition, the latter was verified to be better than the formal. At 20℃, 28℃ and 30℃, the effects of simulation by classical Gompertz model and Logistic model had gained better results. After verifying 40 samples of growth curve by F and t test, Gompertz model has been testified as the most available model with 100% acceptance by t test and 78% acceptance by F test.
The experiment compared the data by simulated with that by observed, in order to validating the effectiveness of models. The results showed that the entire validity has gained 50 %~100 %, when using Quadratic model at 4℃ and 10℃ and Gompertz model at 20℃, 28℃ and 30℃. In a conclusion, the effect of simulation of Gompertz model was better correspondingly.
According to the simulate data from model, considering the facts that the condition metabolizing mycotoxins of Aspergillus versicolor was easy correspondingly, as well as the 80 % of metabolizing mycotoxins rate, I bringed forward the risk contents and the assessment future orientation of Aspergillus versicolor in Jinhua ham. The predictive models established, which were mainly adapted to simulate the biomass growth of
Aspergillus versicolor but the mycotoxins, were greatly limit to assess the hazard of mycotoxins. Reasons were effectiveness and difficulty of establishing mycotoxins models, and microorganism's actual assessment that was just based on the nature of evaluate target. However, the viewpoints and conclusion advanced in risk assessment were effective and rational.
引文
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[5] 林克忠．金华火腿的色香味．杭州食品科技，1998，120(3)：31～32
[6] Whiting R. C.. Predictive Microbiology in Food. Review Food Science and Nutrition, 1995.35: 467～468
[7] McMeekin T. A., Olley J.. Predictive microbiology: theory and application, 1993. 54～57
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[9] Whiting R C., Buchanan R. L.. Food Microbiology, 1993.10: 175
[10] Nauta M., J. Modelling bacterial growth in quantitative microbiological risk assessment: is it possible?. International Journal of Food Microbiology, 2002. 73: 297-304
[11] Battey S., Duiy S., Schainer D. W., Modellingmould spoilage in cold-filled ready-to-&ink beverages by Aspergillus niger and Penicillium spinulosum Alyce, Food Microbiology, 2001. 18: 521～529
[12] 王为．二十一世纪流行食品预测．食品与发酵工业，2002，26(3)：38
[13] 李博，李里特，辰巳英三．预测微生物学的研究进展．食品与发酵工业，2001，27：54～58
[14] McMeekin T.A., Ross T., Predictive microbiology: providing a knowledge-based framework for change management. International Journal of Food Microbiology, 2002. 78: 133-151
[15] 苏世彦．食品微生物检验手册．北京：中国轻工业出版社，1998，247～264
[16] Samson R.A., Hoerstra E.S., Introduction to food-and airborne fungi. Centraal Bureau Voor Schimmel Cultures, 2000. 10～244
[17] 张亚军，陈有亮．微生物在传统干腌火腿成熟过程的作用．肉类工业，2003，47(8)：23-25
[18] 王叔淳．食品卫生检测技术手册．北京：化学工业出版社(第三版)，2003，145～237
[19] Cooray R., Toxin. Food Chemistry, 1984. 22: 529
[20] Barbara D., Beck C. The use of information on susceptibility in risk assessment: state of the science and the potential for improvement. Environmental Toxicology and Pharmacology, 1997. 6(4):
229～234
[21] Brown D.W., Yu J-H, Kelkar H. S., Femandes M., Twenty-five coregulated transcripts define a sterigmatocystin gene duster in Aspergillus nidu/ans. Science USA, 1996. 93: 1418～1422.
[22] Bergh K., Brakhage H. A,: Regulation of the Aspergillus nidulans penicillin biosynthesis gene acvA (pcbAB) by amino acids: implication for involvement of transcription factor PACC. Application in Environment and Microbiology, 1998.64: 843～849.
[23] McDonald K., Sun Da-Wen, Predictive food microbiology for the meat industry: a review. International Journal of Food Microbiology, 1999. 52: 1～27
[24] Breand S., Fardel S., Flandrois J.P., A model describing the relationship between lag time and mild temperature increase duration. International. Journal. Food Microbiology, 2001.38: 157～167
[25] Application of modeling in HACCP plan development. International Journal of Food Microbiology, 1995.25: 251～261
[26] Martinus A.J.S., van B., On the use of the Weibull model to describe thermal inactivation of microbial vegetative cells. International Joumal of Food Microbiology, 2002. 74: 139～159
[27] Soboleva T.K., Pleasants A.B., Predictive microbiology and food safety. International Journal of Food Microbiology, 2000. 57: 183～192
[28] Soboleva, T.K., Pleasants, A.B., Population growth as a nonlinear stochastic process, in: proceedings of the first pestern Pacific and third Australian-Japan workshop on Stochastic models in engineering.Technology and Management, 1999. 497～503.
[29] 郭剑飞，李柏林，欧杰．等，基于食品安全性的预测微生物学研究模式．食品科技，2004，2：3～8
[30] USDA的MPM软件
[31] 严伟民．金华火腿的加工工艺技术．肉类工业，1995，4：10～12
[32] Brody A., Packaging performers. Food Processing Industry [J], 2000. 1:9～12
[33] Valk L., Pieckova E., Growth modelling of heat-resistant fungi: the effect of water activity. International Journal of Food Microbiology, 2001.63: 11～17
[34] Henson S., Caswell J., Food safety regulation: an overview of contemporary issues. Food Policy, 1999. 24: 589～603
[35] 王卫．栅栏技术在食品开发中的实用[J]．食品科学，1997，18(3)：9～13
[36] 许天宇．食品微生物生长预测模型．食品科学，1995，(16)：17～21
[37] Peleg M., Microbial survival curves D the reality of -at "shoulders" and absolute thermal death
times. Food Research International, 2003.3: 531～538
[38] Messens W., Verluyten J., Leroy F., Modelling growth and bacteriocin production by Lactobacillus curvatus LTH 1174 in response to temperature and pH values used for European sausage fermentation processes. International Journal of Food Microbiology, 2003.81: 41～52
[39] 联合国粮食及农业组织食品及营养司食品质量标准处．鸡蛋和肉鸡沙门氏菌风险分析．世界卫生组织食品安全司，2001，4～19
[40] 江汉湖．食品微生物学．北京：中国农业出版社，2001，428～431
[41] 南庆贤，肉类工业手册，北京：中国轻工业出版社，2003，386～387
[42] Brian J．B．Wood主编，徐岩译．发酵食品微生物学．北京：中国轻工业出版社．2001，332～340
[43] 杨洁彬，王晶，王泊琴．食品安全性．北京：中国轻工业出版社，2002，145～149
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