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| 储粮预测微生物模型的研究进展 | |
| 引用本文： | 岳晓禹.储粮预测微生物模型的研究进展[J].中国粮油学报,2012,27(5):118-123. |
| 作者姓名： | 岳晓禹 |
| 作者单位： | 1. 郑州牧业工程高等专科学校质检系,郑州450011;中国农业大学食品科学与营养工程学院,北京100083 2. 郑州牧业工程高等专科学校质检系,郑州,450011 3. 中国农业大学食品科学与营养工程学院,北京,100083 |
| 基金项目： | 河南省重点科技发展计划(112102110034);河南省教育厅自然科学研究计划(2011A550014);郑州牧专博士科研启动资金(5040190) |
| 摘 要： | 粮食的安全储藏,关系到食品安全和人类健康。依据预测微生物学,构建储粮中微生物生长预测模型,可以快速对储粮中微生物的生长情况进行判断,对储粮中病原微生物和腐败微生物的控制有重要的意义。对实现"生态储粮",确保储粮安全也具有重要的理论和实际应用价值。以文献综述形式,简要概述了储粮微生物,根据不同的数学模型,综述了初级、二级和三级模型中常见的模型,并在此基础上,简述了储粮中主要有害霉菌模拟研究的最新研究进展。 |
| 关 键 词： | 储粮 预测微生物 模型 综述 |
| 收稿时间： | 8/3/2011 12:00:00 AM |
| 修稿时间： | 2012/2/22 0:00:00 |
Research progress in predictive microbiology models of stored grain |
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| Yue Xiaoyu , Zhang Hengye , Xin Ting , Li Xin , Liu Xiangdong , Niu Tiangui.Research progress in predictive microbiology models of stored grain[J].Journal of the Chinese Cereals and Oils Association,2012,27(5):118-123. | |
| Authors: | Yue Xiaoyu Zhang Hengye Xin Ting Li Xin Liu Xiangdong Niu Tiangui |
| Affiliation: | 2 (Department of Quality Detection and Management,Zhengzhou College of Animal Husbandry Engineering1,Zhengzhou 450011)(College of Food Science and Nutritional Engineering,China Agricultural University2,Beijing 100083) |
| Abstract: | The safety of the grain storage related to food safety and human health.According to the predictive microbiology,the microorganism’s growth in stored grain can be fast judgment in advance by construction of predictive microbiology model.It plays an important part in controlling the growth of pathogen and the spoilage microorganism in stored grain.It is of important theoretical and practical application value to realize the "ecological storage of grain" and ensure the security of the grain storage.This paper provides a review on the microbes of grain storage.According to different mathematical model,this paper reviews the primary,secondary and tertiary model.Besides,and the research progress of the main harmful mold simulation in grain storage was discussed. |
| Keywords: | stored grain predictive microbiology model reviews |
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