沈旺旺,高振洪,樊沁昕,刘东红,丁甜.温度对自热食品理化指标影响及其货架期预测方法[J].包装工程,2021,42(9):141-151.
SHEN Wang-wang,GAO Zhen-hong,FAN Qin-xin,LIU Dong-hong,DING Tian.Impact of Temperature on Physical and Chemical Properties of Self-Heating Food and Its Shelf Life Prediction Estimation[J].Packaging Engineering,2021,42(9):141-151. |
| 温度对自热食品理化指标影响及其货架期预测方法 |
| Impact of Temperature on Physical and Chemical Properties of Self-Heating Food and Its Shelf Life Prediction Estimation |
| 投稿时间：2020-11-01 |
| DOI：10.19554/j.cnki.1001-3563.2021.09.020 |
| 中文关键词: 自热食品 货架期预测 BP神经网络 贮藏温度 品质变化 |
| 英文关键词: self-heating food prediction of shelf life BP neural network storage temperature, quality change |
| 基金项目:国家重点研发计划(2018YFD0400504) |
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| 中文摘要: |
| 目的 以自热食品土豆牛肉菜肴包为研究对象，考察温度对其贮藏品质的影响，同时建立自热食品土豆牛肉菜肴包的货架期预测模型。方法 利用加速货架期实验的方法，研究不同贮藏温度下(40，50，60 ℃)土豆牛肉菜肴包品质的变化，通过相关性分析选定货架期预测的代表性指标，基于理化指标变化运用化学动力学模型、Q10模型和BP神经网络模型建立自热食品的货架期预测模型，对其常温下货架期进行预测。结果 在3种温度下，样品pH值基本呈现先下降再上升最后下降的趋势，且温度越高变化越剧烈;3种温度下，均有L*值先缓慢下降后急剧下降，a*值先是快速升高之后速率逐渐减慢，b*值则是相对较稳定的缓慢上升的趋势;脂质氧化和蛋白质水解的反应速率加快，致使丙二醛和挥发性盐基氮含量增加。相较于化学动力学模型和Q10模型，基于先验条件的BP神经网络模型最适用于该自热食品货架期预测，最终误差收敛于7.16×10−5，用该模型预测25 ℃土豆牛肉菜肴包的货架期为430 d。结论 温度对土豆牛肉菜肴包各项品质指标均有显著影响，同时确立了货架期预测的最优模型为考虑先验条件的BP神经网络模型。 |
| 英文摘要: |
| Focusing on the self-heating roast potato and beef dishes, this paper studied the effect of temperature on the storage properties by using accelerated shelf life method, the chemical dynamics model, Q10 model and BP neural network model is established to determine the shelf life prediction model based on the physical and chemical indicators. Under the conditions of 40 ℃, 50 ℃ and 60 ℃, the pH of food basically decreased first, then increased and finally decreased again, and the pH of food changed more dramatically with the increase of temperature. Under the three temperatures storage conditions, there was a gradual decrease and then a sharp decrease in Lvalue, a rapid increase and then a gradual decrease in the rate of a value, and b value reflected a relatively stable trend of slow rise. The reaction rate of lipid oxidation and proteolysis is accelerated, which leads to the increase of malondialdehyde and volatile basic nitrogen content. Compared with the chemical dynamics model and Q10 model, the BP neural network model based on prior conditions is most suitable for the shelf life prediction of self-heating food. The final error converges to 7.16×10−5, and the shelf life of potato and beef package at 25 ℃ is 430 days by using this model. Temperature has a significant effect on the quality indexes of roast potato and beef dishes, and the optimal shelf life prediction model is BP neural network model considering prior conditions. |
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