食品科学 ›› 2020, Vol. 41 ›› Issue (5): 31-38.doi: 10.7506/spkx1002-6630-20190219-106

• 基础研究 • 上一篇    下一篇

黄酒非生物稳定性的预测模型

谢广发,陆健,孙军勇,陆胤,彭祺,钱斌,金建顺,王兰,傅祖康,鲁振东,刘彩琴   

  1. (1.浙江树人大学生物与环境工程学院,浙江 绍兴 312028;2. 浙江树人大学绍兴黄酒学院,浙江 绍兴 312028;3.江南大学生物工程学院,江苏 无锡 214122;4.江南大学 粮食发酵工艺与技术国家工程实验室,江苏 无锡 214122;5.国家黄酒工程技术研究中心,浙江 绍兴 312000;6.会稽山绍兴酒有限公司,浙江 绍兴 312000)
  • 出版日期:2020-03-15 发布日期:2020-03-23
  • 基金资助:
    浙江树人大学人才引进项目(2018R009);浙江树人大学“中青年学术项目团队”项目; “十三五”国家重点研发计划重点专项(2016YFD04005-04);浙江省重点研发计划项目(2017C02006)

Predictive Modeling of the Nonbiological Stability of Chinese Yellow Wine

XIE Guangfa, LU Jian, SUN Junyong, LU Yin, PENG Qi, QIAN Bin, JIN Jianshun, WANG Lan, FU Zukang, LU Zhendong, LIU Caiqin   

  1. (1. College of Biology and Environmental Engineering, Zhejiang Shuren University, Shaoxing 312028, China; 2. College of Shaoxing Huangjiu, Zhejiang Shuren University, Shaoxing 312028, China; 3. School of Biotechnology, Jiangnan University, Wuxi 214122, China; 4. National Engineering Laboratory for Cereal Fermentation Technology, Jiangnan University, Wuxi 214122, China; 5. National Engineering Research Center for Chinese Huangjiu, Shaoxing 312000, China; 6. Kuaijishan Shaoxing Wine Co. Ltd., Shaoxing 312000, China)
  • Online:2020-03-15 Published:2020-03-23

摘要: 为建立黄酒非生物稳定性预测模型来评估其货架期的浊度,研究了与黄酒混浊相关的成分,包括总酚、儿茶素、敏感多酚、总氮、隆丁区分、敏感蛋白和铁离子水平,分析了各成分与黄酒浊度之间的关系,建立了与黄酒稳定性相关的多元线性回归预测模型。结果表明,瓶装黄酒的浊度(Y1)与样品浊度增加4 NTU所需冷热处理循环数(X1)、敏感蛋白含量(X2)、总氮质量浓度(X3)、低分子质量氮质量浓度(X4)和中分子质量氮质量浓度(X5)均显著相关(P<0.05),其多元线性回归方程为Y1=2.79-0.485X1+0.663X2+0.327X3+1.577X4-3.864X5。验证实验结果表明,瓶装黄酒浊度的多元线性回归方程预测值与存放12 个月后的实测值具有较好的对应性,说明该预测模型具有较好的应用价值。

关键词: 黄酒, 蛋白, 多酚, 非生物稳定性, 预测模型

Abstract: In order to establish a predictive model for determining the turbidity of Chinese yellow wine to estimate its shelf life, the chemical components related to the turbidity were investigated, including total polyphenols, catechins, sensitive polyphenols, total nitrogen, Lundin fractions, sensitive proteins and iron ions. A multivariate linear regression model for predicting the turbidity was developed. The results showed that the turbidity (Y1) of bottled wine was significantly correlated with the number of heating-cooling cycles required for an increase of 4 NTU in turbidity (X1), sensitive protein content (X2), total nitrogen content (X3), low molecular mass nitrogen content (X4) and medium molecular mass nitrogen content (X5). The linear regression equation developed was Y1 = 2.79 ? 0.485X1 + 0.663X2 + 0.327X3 + 1.577X4 ? 3.864X5. The measured turbidity of wine stored for 12 months was in good agreement with the predicted value, suggesting the prediction model has a good application potential.

Key words: Chinese yellow wine, protein, polyphenol, nonbiological stability, prediction model

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